feat: initial HSAP platform
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
167
algorithms/dms_yolo/code/docs/en/datasets/classify/caltech101.md
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algorithms/dms_yolo/code/docs/en/datasets/classify/caltech101.md
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---
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comments: true
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description: Explore the widely-used Caltech-101 dataset with 9,000 images across 101 categories. Ideal for object recognition tasks in machine learning and computer vision.
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keywords: Caltech-101, dataset, object recognition, machine learning, computer vision, YOLO, deep learning, research, AI
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---
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# Caltech-101 Dataset
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The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is a widely used dataset for object recognition tasks, containing around 9,000 images from 101 object categories. The categories were chosen to reflect a variety of real-world objects, and the images themselves were carefully selected and annotated to provide a challenging benchmark for object recognition algorithms.
|
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|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/isc06_9qnM0"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics Platform
|
||||
</p>
|
||||
|
||||
!!! note "Automatic Data Splitting"
|
||||
|
||||
The Caltech-101 dataset, as provided, does not come with pre-defined train/validation splits. However, when you use the training commands provided in the usage examples below, the Ultralytics framework will automatically split the dataset for you. The default split used is 80% for the training set and 20% for the validation set.
|
||||
|
||||
## Key Features
|
||||
|
||||
- The Caltech-101 dataset comprises around 9,000 color images divided into 101 categories.
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||||
- The categories encompass a wide variety of objects, including animals, vehicles, household items, and people.
|
||||
- The number of images per category varies, with about 40 to 800 images in each category.
|
||||
- Images are of variable sizes, with most images being medium resolution.
|
||||
- Caltech-101 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.
|
||||
|
||||
## Dataset Structure
|
||||
|
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Unlike many other datasets, the Caltech-101 dataset is not formally split into training and testing sets. Users typically create their own splits based on their specific needs. However, a common practice is to use a random subset of images for training (e.g., 30 images per category) and the remaining images for testing.
|
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|
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## Applications
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||||
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The Caltech-101 dataset is extensively used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object recognition tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. Its wide variety of categories and high-quality images make it an excellent dataset for research and development in the field of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
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## Usage
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To train a YOLO model on the Caltech-101 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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||||
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||||
!!! example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="caltech101", epochs=100, imgsz=416)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo classify train data=caltech101 model=yolo26n-cls.pt epochs=100 imgsz=416
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```
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## Sample Images and Annotations
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|
||||
The Caltech-101 dataset contains high-quality color images of various objects, providing a well-structured dataset for [image classification](https://www.ultralytics.com/glossary/image-classification) tasks. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
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The example showcases the variety and complexity of the objects in the Caltech-101 dataset, emphasizing the significance of a diverse dataset for training robust object recognition models.
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||||
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## Citations and Acknowledgments
|
||||
|
||||
If you use the Caltech-101 dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
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||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{fei2007learning,
|
||||
title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
|
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author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
|
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journal={Computer vision and Image understanding},
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volume={106},
|
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number={1},
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pages={59--70},
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year={2007},
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publisher={Elsevier}
|
||||
}
|
||||
```
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||||
|
||||
We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Caltech-101 dataset used for in machine learning?
|
||||
|
||||
The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is widely used in machine learning for object recognition tasks. It contains around 9,000 images across 101 categories, providing a challenging benchmark for evaluating object recognition algorithms. Researchers leverage it to train and test models, especially Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs) and Support Vector Machines (SVMs), in computer vision.
|
||||
|
||||
### How can I train an Ultralytics YOLO model on the Caltech-101 dataset?
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||||
|
||||
To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the provided code snippets. For example, to train for 100 epochs:
|
||||
|
||||
!!! example "Train Example"
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||||
|
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=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
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||||
|
||||
# Train the model
|
||||
results = model.train(data="caltech101", epochs=100, imgsz=416)
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```
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|
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=== "CLI"
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||||
|
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```bash
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||||
# Start training from a pretrained *.pt model
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||||
yolo classify train data=caltech101 model=yolo26n-cls.pt epochs=100 imgsz=416
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||||
```
|
||||
|
||||
For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the key features of the Caltech-101 dataset?
|
||||
|
||||
The Caltech-101 dataset includes:
|
||||
|
||||
- Around 9,000 color images across 101 categories.
|
||||
- Categories covering a diverse range of objects, including animals, vehicles, and household items.
|
||||
- Variable number of images per category, typically between 40 and 800.
|
||||
- Variable image sizes, with most being medium resolution.
|
||||
|
||||
These features make it an excellent choice for training and evaluating object recognition models in machine learning and computer vision.
|
||||
|
||||
### Why should I cite the Caltech-101 dataset in my research?
|
||||
|
||||
Citing the Caltech-101 dataset in your research acknowledges the creators' contributions and provides a reference for others who might use the dataset. The recommended citation is:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{fei2007learning,
|
||||
title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
|
||||
author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
|
||||
journal={Computer vision and Image understanding},
|
||||
volume={106},
|
||||
number={1},
|
||||
pages={59--70},
|
||||
year={2007},
|
||||
publisher={Elsevier}
|
||||
}
|
||||
```
|
||||
|
||||
Citing helps in maintaining the integrity of academic work and assists peers in locating the original resource.
|
||||
|
||||
### Can I use Ultralytics Platform for training models on the Caltech-101 dataset?
|
||||
|
||||
Yes, you can use [Ultralytics Platform](https://platform.ultralytics.com) for training models on the Caltech-101 dataset. Ultralytics Platform provides an intuitive platform for managing datasets, training models, and deploying them without extensive coding. For a detailed guide, refer to the [how to train your custom models with Ultralytics Platform](https://www.ultralytics.com/blog/how-to-train-your-custom-models-with-ultralytics-hub) blog post.
|
||||
148
algorithms/dms_yolo/code/docs/en/datasets/classify/caltech256.md
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algorithms/dms_yolo/code/docs/en/datasets/classify/caltech256.md
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---
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comments: true
|
||||
description: Explore the Caltech-256 dataset, featuring 30,000 images across 257 categories, ideal for training and testing object recognition algorithms.
|
||||
keywords: Caltech-256 dataset, object classification, image dataset, machine learning, computer vision, deep learning, YOLO, training dataset
|
||||
---
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||||
|
||||
# Caltech-256 Dataset
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|
||||
The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is an extensive collection of images used for object classification tasks. It contains around 30,000 images divided into 257 categories (256 object categories and 1 background category). The images are carefully curated and annotated to provide a challenging and diverse benchmark for object recognition algorithms.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/isc06_9qnM0"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics Platform
|
||||
</p>
|
||||
|
||||
!!! note "Automatic Data Splitting"
|
||||
|
||||
The Caltech-256 dataset, as provided, does not come with pre-defined train/validation splits. However, when you use the training commands provided in the usage examples below, the Ultralytics framework will automatically split the dataset for you. The default split used is 80% for the training set and 20% for the validation set.
|
||||
|
||||
## Key Features
|
||||
|
||||
- The Caltech-256 dataset comprises around 30,000 color images divided into 257 categories.
|
||||
- Each category contains a minimum of 80 images.
|
||||
- The categories encompass a wide variety of real-world objects, including animals, vehicles, household items, and people.
|
||||
- Images are of variable sizes and resolutions.
|
||||
- Caltech-256 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
Like [Caltech-101](../classify/caltech101.md), the Caltech-256 dataset does not have a formal split between training and testing sets. Users typically create their own splits according to their specific needs. A common practice is to use a random subset of images for training and the remaining images for testing.
|
||||
|
||||
## Applications
|
||||
|
||||
The Caltech-256 dataset is extensively used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object recognition tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. Its diverse set of categories and high-quality images make it an invaluable dataset for research and development in the field of machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="caltech256", epochs=100, imgsz=416)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=caltech256 model=yolo26n-cls.pt epochs=100 imgsz=416
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The Caltech-256 dataset contains high-quality color images of various objects, providing a comprehensive dataset for object recognition tasks. Here are some examples of images from the dataset ([credit](https://ml4a.github.io/demos/tsne_viewer.html)):
|
||||
|
||||
|
||||
|
||||
The example showcases the diversity and complexity of the objects in the Caltech-256 dataset, emphasizing the importance of a varied dataset for training robust object recognition models.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the Caltech-256 dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{griffin2007caltech,
|
||||
title={Caltech-256 object category dataset},
|
||||
author={Griffin, Gregory and Holub, Alex and Perona, Pietro},
|
||||
year={2007}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Caltech-256 dataset and why is it important for machine learning?
|
||||
|
||||
The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is a large image dataset used primarily for object classification tasks in machine learning and computer vision. It consists of around 30,000 color images divided into 257 categories, covering a wide range of real-world objects. The dataset's diverse and high-quality images make it an excellent benchmark for evaluating object recognition algorithms, which is crucial for developing robust machine learning models.
|
||||
|
||||
### How can I train a YOLO model on the Caltech-256 dataset using Python or CLI?
|
||||
|
||||
To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. Refer to the model [Training](../../modes/train.md) page for additional options.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="caltech256", epochs=100, imgsz=416)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=caltech256 model=yolo26n-cls.pt epochs=100 imgsz=416
|
||||
```
|
||||
|
||||
### What are the most common use cases for the Caltech-256 dataset?
|
||||
|
||||
The Caltech-256 dataset is widely used for various object recognition tasks such as:
|
||||
|
||||
- Training Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs)
|
||||
- Evaluating the performance of Support Vector Machines (SVMs)
|
||||
- Benchmarking new deep learning algorithms
|
||||
- Developing [object detection](https://www.ultralytics.com/glossary/object-detection) models using frameworks like Ultralytics YOLO
|
||||
|
||||
Its diversity and comprehensive annotations make it ideal for research and development in machine learning and computer vision.
|
||||
|
||||
### How is the Caltech-256 dataset structured and split for training and testing?
|
||||
|
||||
The Caltech-256 dataset does not come with a predefined split for training and testing. Users typically create their own splits according to their specific needs. A common approach is to randomly select a subset of images for training and use the remaining images for testing. This flexibility allows users to tailor the dataset to their specific project requirements and experimental setups.
|
||||
|
||||
### Why should I use Ultralytics YOLO for training models on the Caltech-256 dataset?
|
||||
|
||||
Ultralytics YOLO models offer several advantages for training on the Caltech-256 dataset:
|
||||
|
||||
- **High Accuracy**: YOLO models are known for their state-of-the-art performance in object detection tasks.
|
||||
- **Speed**: They provide real-time inference capabilities, making them suitable for applications requiring quick predictions.
|
||||
- **Ease of Use**: With [Ultralytics Platform](https://platform.ultralytics.com), users can train, validate, and deploy models without extensive coding.
|
||||
- **Pretrained Models**: Starting from pretrained models, like `yolo26n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
|
||||
For more details, explore our [comprehensive training guide](../../modes/train.md) and learn about [image classification](../../tasks/classify.md) with Ultralytics YOLO.
|
||||
173
algorithms/dms_yolo/code/docs/en/datasets/classify/cifar10.md
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173
algorithms/dms_yolo/code/docs/en/datasets/classify/cifar10.md
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@@ -0,0 +1,173 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the CIFAR-10 dataset, featuring 60,000 color images in 10 classes. Learn about its structure, applications, and how to train models using YOLO.
|
||||
keywords: CIFAR-10, dataset, machine learning, computer vision, image classification, YOLO, deep learning, neural networks
|
||||
---
|
||||
|
||||
# CIFAR-10 Dataset
|
||||
|
||||
The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a collection of images used widely for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision algorithms. It was developed by researchers at the CIFAR institute and consists of 60,000 32x32 color images in 10 different classes.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/fLBbyhPbWzY"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- The CIFAR-10 dataset consists of 60,000 images, divided into 10 classes.
|
||||
- Each class contains 6,000 images, split into 5,000 for training and 1,000 for testing.
|
||||
- The images are colored and of size 32x32 pixels.
|
||||
- The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
|
||||
- CIFAR-10 is commonly used for training and testing in the field of machine learning and computer vision.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The CIFAR-10 dataset is split into two subsets:
|
||||
|
||||
1. **Training Set**: This subset contains 50,000 images used for training machine learning models.
|
||||
2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
|
||||
|
||||
## Applications
|
||||
|
||||
The CIFAR-10 dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a well-rounded dataset for research and development in the field of machine learning and computer vision.
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO model on the CIFAR-10 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar10", epochs=100, imgsz=32)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar10 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The CIFAR-10 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
||||
The example showcases the variety and complexity of the objects in the CIFAR-10 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the CIFAR-10 dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@TECHREPORT{Krizhevsky09learningmultiple,
|
||||
author={Alex Krizhevsky},
|
||||
title={Learning multiple layers of features from tiny images},
|
||||
institution={},
|
||||
year={2009}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
|
||||
|
||||
## FAQ
|
||||
|
||||
### How can I train a YOLO model on the CIFAR-10 dataset?
|
||||
|
||||
To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow the examples provided for both Python and CLI. Here is a basic example to train your model for 100 epochs with an image size of 32x32 pixels:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar10", epochs=100, imgsz=32)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar10 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
For more details, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the key features of the CIFAR-10 dataset?
|
||||
|
||||
The CIFAR-10 dataset consists of 60,000 color images divided into 10 classes. Each class contains 6,000 images, with 5,000 for training and 1,000 for testing. The images are 32x32 pixels in size and vary across the following categories:
|
||||
|
||||
- Airplanes
|
||||
- Cars
|
||||
- Birds
|
||||
- Cats
|
||||
- Deer
|
||||
- Dogs
|
||||
- Frogs
|
||||
- Horses
|
||||
- Ships
|
||||
- Trucks
|
||||
|
||||
This diverse dataset is essential for training image classification models in fields such as machine learning and computer vision. For more information, visit the CIFAR-10 sections on [dataset structure](#dataset-structure) and [applications](#applications).
|
||||
|
||||
### Why use the CIFAR-10 dataset for image classification tasks?
|
||||
|
||||
The CIFAR-10 dataset is an excellent benchmark for image classification due to its diversity and structure. It contains a balanced mix of 60,000 labeled images across 10 different categories, which helps in training robust and generalized models. It is widely used for evaluating deep learning models, including Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs) and other machine learning algorithms. The dataset is relatively small, making it suitable for quick experimentation and algorithm development. Explore its numerous applications in the [applications](#applications) section.
|
||||
|
||||
### How is the CIFAR-10 dataset structured?
|
||||
|
||||
The CIFAR-10 dataset is structured into two main subsets:
|
||||
|
||||
1. **Training Set**: Contains 50,000 images used for training machine learning models.
|
||||
2. **Testing Set**: Consists of 10,000 images for testing and benchmarking the trained models.
|
||||
|
||||
Each subset comprises images categorized into 10 classes, with their annotations readily available for model training and evaluation. For more detailed information, refer to the [dataset structure](#dataset-structure) section.
|
||||
|
||||
### How can I cite the CIFAR-10 dataset in my research?
|
||||
|
||||
If you use the CIFAR-10 dataset in your research or development projects, make sure to cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@TECHREPORT{Krizhevsky09learningmultiple,
|
||||
author={Alex Krizhevsky},
|
||||
title={Learning multiple layers of features from tiny images},
|
||||
institution={},
|
||||
year={2009}
|
||||
}
|
||||
```
|
||||
|
||||
Acknowledging the dataset's creators helps support continued research and development in the field. For more details, see the [citations and acknowledgments](#citations-and-acknowledgments) section.
|
||||
|
||||
### What are some practical examples of using the CIFAR-10 dataset?
|
||||
|
||||
The CIFAR-10 dataset is often used for training image classification models, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). These models can be employed in various computer vision tasks including [object detection](https://www.ultralytics.com/glossary/object-detection), [image recognition](https://www.ultralytics.com/glossary/image-recognition), and automated tagging. To see some practical examples, check the code snippets in the [usage](#usage) section.
|
||||
141
algorithms/dms_yolo/code/docs/en/datasets/classify/cifar100.md
Normal file
141
algorithms/dms_yolo/code/docs/en/datasets/classify/cifar100.md
Normal file
@@ -0,0 +1,141 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the CIFAR-100 dataset, consisting of 60,000 32x32 color images across 100 classes. Ideal for machine learning and computer vision tasks.
|
||||
keywords: CIFAR-100, dataset, machine learning, computer vision, image classification, deep learning, YOLO, training, testing, Alex Krizhevsky
|
||||
---
|
||||
|
||||
# CIFAR-100 Dataset
|
||||
|
||||
The [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a significant extension of the CIFAR-10 dataset, composed of 60,000 32x32 color images in 100 different classes. It was developed by researchers at the CIFAR institute, offering a more challenging dataset for more complex machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/6bZeCs0xwO4"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on CIFAR-100 | Step-by-Step Image Classification Tutorial 🚀
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- The CIFAR-100 dataset consists of 60,000 images, divided into 100 classes.
|
||||
- Each class contains 600 images, split into 500 for training and 100 for testing.
|
||||
- The images are colored and of size 32x32 pixels.
|
||||
- The 100 different classes are grouped into 20 coarse categories for higher level classification.
|
||||
- CIFAR-100 is commonly used for training and testing in the field of machine learning and computer vision.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The CIFAR-100 dataset is split into two subsets:
|
||||
|
||||
1. **Training Set**: This subset contains 50,000 images used for training machine learning models.
|
||||
2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
|
||||
|
||||
## Applications
|
||||
|
||||
The CIFAR-100 dataset is extensively used for training and evaluating deep learning models in [image classification](https://www.ultralytics.com/glossary/image-classification) tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a more challenging and comprehensive dataset for research and development in the field of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision.
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar100", epochs=100, imgsz=32)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar100 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The CIFAR-100 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
||||
The example showcases the variety and complexity of the objects in the CIFAR-100 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the CIFAR-100 dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@TECHREPORT{Krizhevsky09learningmultiple,
|
||||
author={Alex Krizhevsky},
|
||||
title={Learning multiple layers of features from tiny images},
|
||||
institution={},
|
||||
year={2009}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the CIFAR-100 dataset and why is it significant?
|
||||
|
||||
The [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) is a large collection of 60,000 32x32 color images classified into 100 classes. Developed by the Canadian Institute For Advanced Research (CIFAR), it provides a challenging dataset ideal for complex machine learning and computer vision tasks. Its significance lies in the diversity of classes and the small size of the images, making it a valuable resource for training and testing [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models, like Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs), using frameworks such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/).
|
||||
|
||||
### How do I train a YOLO model on the CIFAR-100 dataset?
|
||||
|
||||
You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI commands. Here's how:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="cifar100", epochs=100, imgsz=32)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=cifar100 model=yolo26n-cls.pt epochs=100 imgsz=32
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, please refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the primary applications of the CIFAR-100 dataset?
|
||||
|
||||
The CIFAR-100 dataset is extensively used in training and evaluating deep learning models for image classification. Its diverse set of 100 classes, grouped into 20 coarse categories, provides a challenging environment for testing algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning approaches. This dataset is a key resource in research and development within machine learning and computer vision fields, particularly for [object recognition](https://docs.ultralytics.com/tasks/classify/) and classification tasks.
|
||||
|
||||
### How is the CIFAR-100 dataset structured?
|
||||
|
||||
The CIFAR-100 dataset is split into two main subsets:
|
||||
|
||||
1. **Training Set**: Contains 50,000 images used for training machine learning models.
|
||||
2. **Testing Set**: Consists of 10,000 images used for testing and benchmarking the trained models.
|
||||
|
||||
Each of the 100 classes contains 600 images, with 500 images for training and 100 for testing, making it uniquely suited for rigorous academic and industrial research.
|
||||
|
||||
### Where can I find sample images and annotations from the CIFAR-100 dataset?
|
||||
|
||||
The CIFAR-100 dataset includes a variety of color images of various objects, making it a structured dataset for image classification tasks. You can refer to the documentation page to see [sample images and annotations](#sample-images-and-annotations). These examples highlight the dataset's diversity and complexity, important for training robust image classification models. For more datasets suitable for classification tasks, check out [Ultralytics' classification datasets overview](https://docs.ultralytics.com/datasets/classify/).
|
||||
@@ -0,0 +1,141 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Fashion-MNIST dataset, a modern replacement for MNIST with 70,000 Zalando article images. Ideal for benchmarking machine learning models.
|
||||
keywords: Fashion-MNIST, image classification, Zalando dataset, machine learning, deep learning, CNN, dataset overview
|
||||
---
|
||||
|
||||
# Fashion-MNIST Dataset
|
||||
|
||||
The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is a database of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) algorithms.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/eX5ad6udQ9Q"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- Fashion-MNIST contains 60,000 training images and 10,000 testing images of Zalando's article images.
|
||||
- The dataset comprises grayscale images of size 28x28 pixels.
|
||||
- Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.
|
||||
- Fashion-MNIST is widely used for training and testing in the field of machine learning, especially for image classification tasks.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The Fashion-MNIST dataset is split into two subsets:
|
||||
|
||||
1. **Training Set**: This subset contains 60,000 images used for training machine learning models.
|
||||
2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
|
||||
|
||||
## Labels
|
||||
|
||||
Each training and test example is assigned to one of the following labels:
|
||||
|
||||
```
|
||||
0. T-shirt/top
|
||||
1. Trouser
|
||||
2. Pullover
|
||||
3. Dress
|
||||
4. Coat
|
||||
5. Sandal
|
||||
6. Shirt
|
||||
7. Sneaker
|
||||
8. Bag
|
||||
9. Ankle boot
|
||||
```
|
||||
|
||||
## Applications
|
||||
|
||||
The Fashion-MNIST dataset is widely used for training and evaluating deep learning models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. The dataset's simple and well-structured format makes it an essential resource for researchers and practitioners in the field of machine learning and computer vision.
|
||||
|
||||
## Usage
|
||||
|
||||
To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 28x28, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=fashion-mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The Fashion-MNIST dataset contains grayscale images of Zalando's article images, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
||||
The example showcases the variety and complexity of the images in the Fashion-MNIST dataset, highlighting the importance of a diverse dataset for training robust image classification models.
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Fashion-MNIST dataset and how is it different from MNIST?
|
||||
|
||||
The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is a collection of 70,000 grayscale images of Zalando's article images, intended as a modern replacement for the original MNIST dataset. It serves as a benchmark for machine learning models in the context of image classification tasks. Unlike MNIST, which contains handwritten digits, Fashion-MNIST consists of 28x28-pixel images categorized into 10 fashion-related classes, such as T-shirt/top, trouser, and ankle boot.
|
||||
|
||||
### How can I train a YOLO model on the Fashion-MNIST dataset?
|
||||
|
||||
To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use both Python and CLI commands. Here's a quick example to get you started:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo26n-cls.pt")
|
||||
|
||||
# Train the model on Fashion-MNIST
|
||||
results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo classify train data=fashion-mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
For more detailed training parameters, refer to the [Training page](../../modes/train.md).
|
||||
|
||||
### Why should I use the Fashion-MNIST dataset for benchmarking my machine learning models?
|
||||
|
||||
The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is widely recognized in the [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) community as a robust alternative to MNIST. It offers a more complex and varied set of images, making it an excellent choice for benchmarking image classification models. The dataset's structure, comprising 60,000 training images and 10,000 testing images, each labeled with one of 10 classes, makes it ideal for evaluating the performance of different machine learning algorithms in a more challenging context.
|
||||
|
||||
### Can I use Ultralytics YOLO for image classification tasks like Fashion-MNIST?
|
||||
|
||||
Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLO26, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
|
||||
|
||||
### What are the key features and structure of the Fashion-MNIST dataset?
|
||||
|
||||
The Fashion-MNIST dataset is divided into two main subsets: 60,000 training images and 10,000 testing images. Each image is a 28x28-pixel grayscale picture representing one of 10 fashion-related classes. The simplicity and well-structured format make it ideal for training and evaluating models in machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks. For more details on the dataset structure, see the [Dataset Structure section](#dataset-structure).
|
||||
|
||||
### How can I acknowledge the use of the Fashion-MNIST dataset in my research?
|
||||
|
||||
If you utilize the Fashion-MNIST dataset in your research or development projects, it's important to acknowledge it by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This helps in attributing the data to Zalando Research, who made the dataset available for public use.
|
||||
132
algorithms/dms_yolo/code/docs/en/datasets/classify/imagenet.md
Normal file
132
algorithms/dms_yolo/code/docs/en/datasets/classify/imagenet.md
Normal file
@@ -0,0 +1,132 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the extensive ImageNet dataset and discover its role in advancing deep learning in computer vision. Access pretrained models and training examples.
|
||||
keywords: ImageNet, deep learning, visual recognition, computer vision, pretrained models, YOLO, dataset, object detection, image classification
|
||||
---
|
||||
|
||||
# ImageNet Dataset
|
||||
|
||||
[ImageNet](https://www.image-net.org/) is a large-scale database of annotated images designed for use in visual object recognition research. It contains over 14 million images, with each image annotated using WordNet synsets, making it one of the most extensive resources available for training [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
|
||||
|
||||
## ImageNet Pretrained Models
|
||||
|
||||
{% include "macros/yolo-cls-perf.md" %}
|
||||
|
||||
## Key Features
|
||||
|
||||
- ImageNet contains over 14 million high-resolution images spanning thousands of object categories.
|
||||
- The dataset is organized according to the WordNet hierarchy, with each synset representing a category.
|
||||
- ImageNet is widely used for training and benchmarking in the field of computer vision, particularly for [image classification](https://www.ultralytics.com/glossary/image-classification) and [object detection](https://www.ultralytics.com/glossary/object-detection) tasks.
|
||||
- The annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been instrumental in advancing computer vision research.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The ImageNet dataset is organized using the WordNet hierarchy. Each node in the hierarchy represents a category, and each category is described by a synset (a collection of synonymous terms). The images in ImageNet are annotated with one or more synsets, providing a rich resource for training models to recognize various objects and their relationships.
|
||||
|
||||
## ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
|
||||
|
||||
The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/) has been an important event in the field of computer vision. It has provided a platform for researchers and developers to evaluate their algorithms and models on a large-scale dataset with standardized evaluation metrics. The ILSVRC has led to significant advancements in the development of deep learning models for image classification, object detection, and other computer vision tasks.
|
||||
|
||||
## Applications
|
||||
|
||||
The ImageNet dataset is widely used for training and evaluating deep learning models in various computer vision tasks, such as image classification, object detection, and object localization. Some popular deep learning architectures, such as [AlexNet](https://en.wikipedia.org/wiki/AlexNet), [VGG](https://arxiv.org/abs/1409.1556), and [ResNet](https://arxiv.org/abs/1512.03385), were developed and benchmarked using the ImageNet dataset.
|
||||
|
||||
## Usage
|
||||
|
||||
To train a deep learning model on the ImageNet dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The ImageNet dataset contains high-resolution images spanning thousands of object categories, providing a diverse and extensive dataset for training and evaluating computer vision models. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
||||
The example showcases the variety and complexity of the images in the ImageNet dataset, highlighting the importance of a diverse dataset for training robust computer vision models.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the ImageNet dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{ILSVRC15,
|
||||
author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
|
||||
title={ImageNet Large Scale Visual Recognition Challenge},
|
||||
year={2015},
|
||||
journal={International Journal of Computer Vision (IJCV)},
|
||||
volume={115},
|
||||
number={3},
|
||||
pages={211-252}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the ImageNet dataset and how is it used in computer vision?
|
||||
|
||||
The [ImageNet dataset](https://www.image-net.org/) is a large-scale database consisting of over 14 million high-resolution images categorized using WordNet synsets. It is extensively used in visual object recognition research, including image classification and object detection. The dataset's annotations and sheer volume provide a rich resource for training deep learning models. Notably, models like AlexNet, VGG, and ResNet have been trained and benchmarked using ImageNet, showcasing its role in advancing computer vision.
|
||||
|
||||
### How can I use a pretrained YOLO model for image classification on the ImageNet dataset?
|
||||
|
||||
To use a pretrained Ultralytics YOLO model for image classification on the ImageNet dataset, follow these steps:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
For more in-depth training instruction, refer to our [Training page](../../modes/train.md).
|
||||
|
||||
### Why should I use the Ultralytics YOLO26 pretrained models for my ImageNet dataset projects?
|
||||
|
||||
Ultralytics YOLO26 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLO26n-cls model, with a top-1 accuracy of 70.0% and a top-5 accuracy of 89.4%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO26 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
|
||||
|
||||
### How is the ImageNet dataset structured, and why is it important?
|
||||
|
||||
The ImageNet dataset is organized using the WordNet hierarchy, where each node in the hierarchy represents a category described by a synset (a collection of synonymous terms). This structure allows for detailed annotations, making it ideal for training models to recognize a wide variety of objects. The diversity and annotation richness of ImageNet make it a valuable dataset for developing robust and generalizable deep learning models. More about this organization can be found in the [Dataset Structure](#dataset-structure) section.
|
||||
|
||||
### What role does the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) play in computer vision?
|
||||
|
||||
The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/) has been pivotal in driving advancements in computer vision by providing a competitive platform for evaluating algorithms on a large-scale, standardized dataset. It offers standardized evaluation metrics, fostering innovation and development in areas such as image classification, object detection, and [image segmentation](https://www.ultralytics.com/glossary/image-segmentation). The challenge has continuously pushed the boundaries of what is possible with deep learning and computer vision technologies.
|
||||
129
algorithms/dms_yolo/code/docs/en/datasets/classify/imagenet10.md
Normal file
129
algorithms/dms_yolo/code/docs/en/datasets/classify/imagenet10.md
Normal file
@@ -0,0 +1,129 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover ImageNet10 a compact version of ImageNet for rapid model testing and CI checks. Perfect for quick evaluations in computer vision tasks.
|
||||
keywords: ImageNet10, ImageNet, Ultralytics, CI tests, sanity checks, training pipelines, computer vision, deep learning, dataset
|
||||
---
|
||||
|
||||
# ImageNet10 Dataset
|
||||
|
||||
The [ImageNet10](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://www.ultralytics.com/) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset.
|
||||
|
||||
## Key Features
|
||||
|
||||
- ImageNet10 is a compact version of ImageNet, with 20 images representing the first 10 classes of the original dataset.
|
||||
- The dataset is organized according to the WordNet hierarchy, mirroring the structure of the full ImageNet dataset.
|
||||
- It is ideally suited for CI tests, sanity checks, and rapid testing of training pipelines in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
|
||||
- Although not designed for model benchmarking, it can provide a quick indication of a model's basic functionality and correctness.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The ImageNet10 dataset, like the original [ImageNet](../classify/imagenet.md), is organized using the WordNet hierarchy. Each of the 10 classes in ImageNet10 is described by a synset (a collection of synonymous terms). The images in ImageNet10 are annotated with one or more synsets, providing a compact resource for testing models to recognize various objects and their relationships.
|
||||
|
||||
## Applications
|
||||
|
||||
The ImageNet10 dataset is useful for quickly testing and debugging computer vision models and pipelines. Its small size allows for rapid iteration, making it ideal for [continuous integration](../../help/CI.md) tests and sanity checks. It can also be used for fast preliminary testing of new models or changes to existing models before moving on to full-scale testing with the complete [ImageNet dataset](../classify/imagenet.md).
|
||||
|
||||
## Usage
|
||||
|
||||
To test a deep learning model on the ImageNet10 dataset with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Test Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet10", epochs=5, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet10 model=yolo26n-cls.pt epochs=5 imgsz=224
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The ImageNet10 dataset contains a subset of images from the original ImageNet dataset. These images are chosen to represent the first 10 classes in the dataset, providing a diverse yet compact dataset for quick testing and evaluation.
|
||||
|
||||
|
||||
|
||||
The example showcases the variety and complexity of the images in the ImageNet10 dataset, highlighting its usefulness for sanity checks and quick testing of computer vision models.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the ImageNet10 dataset in your research or development work, please cite the original ImageNet paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{ILSVRC15,
|
||||
author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
|
||||
title={ImageNet Large Scale Visual Recognition Challenge},
|
||||
year={2015},
|
||||
journal={International Journal of Computer Vision (IJCV)},
|
||||
volume={115},
|
||||
number={3},
|
||||
pages={211-252}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the ImageNet10 dataset and how is it different from the full ImageNet dataset?
|
||||
|
||||
The [ImageNet10](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/imagenet10.zip) dataset is a compact subset of the original [ImageNet](https://www.image-net.org/) database, created by Ultralytics for rapid CI tests, sanity checks, and training pipeline evaluations. ImageNet10 comprises only 20 images, representing the first image in the training and validation sets of the first 10 classes in ImageNet. Despite its small size, it maintains the structure and diversity of the full dataset, making it ideal for quick testing but not for benchmarking models.
|
||||
|
||||
### How can I use the ImageNet10 dataset to test my deep learning model?
|
||||
|
||||
To test your deep learning model on the ImageNet10 dataset with an image size of 224x224, use the following code snippets.
|
||||
|
||||
!!! example "Test Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenet10", epochs=5, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenet10 model=yolo26n-cls.pt epochs=5 imgsz=224
|
||||
```
|
||||
|
||||
Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.
|
||||
|
||||
### Why should I use the ImageNet10 dataset for CI tests and sanity checks?
|
||||
|
||||
The ImageNet10 dataset is designed specifically for CI tests, sanity checks, and quick evaluations in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) pipelines. Its small size allows for rapid iteration and testing, making it perfect for continuous integration processes where speed is crucial. By maintaining the structural complexity and diversity of the original ImageNet dataset, ImageNet10 provides a reliable indication of a model's basic functionality and correctness without the overhead of processing a large dataset.
|
||||
|
||||
### What are the main features of the ImageNet10 dataset?
|
||||
|
||||
The ImageNet10 dataset has several key features:
|
||||
|
||||
- **Compact Size**: With only 20 images, it allows for rapid testing and debugging.
|
||||
- **Structured Organization**: Follows the WordNet hierarchy, similar to the full ImageNet dataset.
|
||||
- **CI and Sanity Checks**: Ideally suited for continuous integration tests and sanity checks.
|
||||
- **Not for Benchmarking**: While useful for quick model evaluations, it is not designed for extensive benchmarking.
|
||||
|
||||
### How does ImageNet10 compare to other small datasets like ImageNette?
|
||||
|
||||
While both [ImageNet10](imagenet10.md) and [ImageNette](imagenette.md) are subsets of ImageNet, they serve different purposes. ImageNet10 contains just 20 images (2 per class) from the first 10 classes of ImageNet, making it extremely lightweight for CI testing and quick sanity checks. In contrast, ImageNette contains thousands of images across 10 easily distinguishable classes, making it more suitable for actual model training and development. ImageNet10 is designed for verification of pipeline functionality, while ImageNette is better for meaningful but faster-than-full-ImageNet training experiments.
|
||||
193
algorithms/dms_yolo/code/docs/en/datasets/classify/imagenette.md
Normal file
193
algorithms/dms_yolo/code/docs/en/datasets/classify/imagenette.md
Normal file
@@ -0,0 +1,193 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the ImageNette dataset, a subset of ImageNet with 10 classes for efficient training and evaluation of image classification models. Ideal for ML and CV projects.
|
||||
keywords: ImageNette dataset, ImageNet subset, image classification, machine learning, deep learning, YOLO, Convolutional Neural Networks, ML dataset, education, training
|
||||
---
|
||||
|
||||
# ImageNette Dataset
|
||||
|
||||
The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [ImageNet](https://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of ImageNet for software development and education.
|
||||
|
||||
## Key Features
|
||||
|
||||
- ImageNette contains images from 10 different classes such as tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute.
|
||||
- The dataset comprises colored images of varying dimensions.
|
||||
- ImageNette is widely used for training and testing in the field of machine learning, especially for image classification tasks.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The ImageNette dataset is split into two subsets:
|
||||
|
||||
1. **Training Set**: This subset contains several thousands of images used for training machine learning models. The exact number varies per class.
|
||||
2. **Validation Set**: This subset consists of several hundreds of images used for validating and benchmarking the trained models. Again, the exact number varies per class.
|
||||
|
||||
## Applications
|
||||
|
||||
The ImageNette dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), and various other machine learning algorithms. The dataset's straightforward format and well-chosen classes make it a handy resource for both beginner and experienced practitioners in the field of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
|
||||
## Usage
|
||||
|
||||
To train a model on the ImageNette dataset for 100 epochs with a standard image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenette", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenette model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for [image classification](https://www.ultralytics.com/glossary/image-classification) tasks. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
||||
The example showcases the variety and complexity of the images in the ImageNette dataset, highlighting the importance of a diverse dataset for training robust image classification models.
|
||||
|
||||
## ImageNette160 and ImageNette320
|
||||
|
||||
For faster prototyping and training, the ImageNette dataset is also available in two reduced sizes: [ImageNette160](https://github.com/fastai/imagenette) and [ImageNette320](https://github.com/fastai/imagenette). These datasets maintain the same classes and structure as the full ImageNette dataset, but the images are resized to a smaller dimension. As such, these versions of the dataset are particularly useful for preliminary model testing, or when computational resources are limited.
|
||||
|
||||
To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imagenette320' in the training command. The following code snippets illustrate this:
|
||||
|
||||
!!! example "Train Example with ImageNette160"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model with ImageNette160
|
||||
results = model.train(data="imagenette160", epochs=100, imgsz=160)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model with ImageNette160
|
||||
yolo classify train data=imagenette160 model=yolo26n-cls.pt epochs=100 imgsz=160
|
||||
```
|
||||
|
||||
!!! example "Train Example with ImageNette320"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model with ImageNette320
|
||||
results = model.train(data="imagenette320", epochs=100, imgsz=320)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model with ImageNette320
|
||||
yolo classify train data=imagenette320 model=yolo26n-cls.pt epochs=100 imgsz=320
|
||||
```
|
||||
|
||||
These smaller versions of the dataset allow for rapid iterations during the development process while still providing valuable and realistic image classification tasks.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the ImageNette dataset?
|
||||
|
||||
The [ImageNette dataset](https://github.com/fastai/imagenette) is a simplified subset of the larger [ImageNet dataset](https://www.image-net.org/), featuring only 10 easily distinguishable classes such as tench, English springer, and French horn. It was created to offer a more manageable dataset for efficient training and evaluation of image classification models. This dataset is particularly useful for quick software development and educational purposes in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision.
|
||||
|
||||
### How can I use the ImageNette dataset for training a YOLO model?
|
||||
|
||||
To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following commands. Make sure to have the Ultralytics YOLO environment set up.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagenette", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagenette model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
For more details, see the [Training](../../modes/train.md) documentation page.
|
||||
|
||||
### Why should I use ImageNette for image classification tasks?
|
||||
|
||||
The ImageNette dataset is advantageous for several reasons:
|
||||
|
||||
- **Quick and Simple**: It contains only 10 classes, making it less complex and time-consuming compared to larger datasets.
|
||||
- **Educational Use**: Ideal for learning and teaching the basics of image classification since it requires less computational power and time.
|
||||
- **Versatility**: Widely used to train and benchmark various machine learning models, especially in image classification.
|
||||
|
||||
For more details on model training and dataset management, explore the [Dataset Structure](#dataset-structure) section.
|
||||
|
||||
### Can the ImageNette dataset be used with different image sizes?
|
||||
|
||||
Yes, the ImageNette dataset is also available in two resized versions: ImageNette160 and ImageNette320. These versions help in faster prototyping and are especially useful when computational resources are limited.
|
||||
|
||||
!!! example "Train Example with ImageNette160"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt")
|
||||
|
||||
# Train the model with ImageNette160
|
||||
results = model.train(data="imagenette160", epochs=100, imgsz=160)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model with ImageNette160
|
||||
yolo classify train data=imagenette160 model=yolo26n-cls.pt epochs=100 imgsz=160
|
||||
```
|
||||
|
||||
For more information, refer to [Training with ImageNette160 and ImageNette320](#imagenette160-and-imagenette320).
|
||||
|
||||
### What are some practical applications of the ImageNette dataset?
|
||||
|
||||
The ImageNette dataset is extensively used in:
|
||||
|
||||
- **Educational Settings**: To educate beginners in machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
- **Software Development**: For rapid prototyping and development of image classification models.
|
||||
- **Deep Learning Research**: To evaluate and benchmark the performance of various deep learning models, especially Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs).
|
||||
|
||||
Explore the [Applications](#applications) section for detailed use cases.
|
||||
153
algorithms/dms_yolo/code/docs/en/datasets/classify/imagewoof.md
Normal file
153
algorithms/dms_yolo/code/docs/en/datasets/classify/imagewoof.md
Normal file
@@ -0,0 +1,153 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the ImageWoof dataset, a challenging subset of ImageNet focusing on 10 dog breeds, designed to enhance image classification models. Learn more on Ultralytics Docs.
|
||||
keywords: ImageWoof dataset, ImageNet subset, dog breeds, image classification, deep learning, machine learning, Ultralytics, training dataset, noisy labels
|
||||
---
|
||||
|
||||
# ImageWoof Dataset
|
||||
|
||||
The [ImageWoof](https://github.com/fastai/imagenette) dataset is a subset of the [ImageNet](imagenet.md) consisting of 10 classes that are challenging to classify, since they're all dog breeds. It was created as a more difficult task for [image classification](https://www.ultralytics.com/glossary/image-classification) algorithms to solve, aiming at encouraging development of more advanced models.
|
||||
|
||||
## Key Features
|
||||
|
||||
- ImageWoof contains images of 10 different dog breeds: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, and Old English sheepdog.
|
||||
- The dataset provides images at various resolutions (full size, 320px, 160px), accommodating for different computational capabilities and research needs.
|
||||
- It also includes a version with noisy labels, providing a more realistic scenario where labels might not always be reliable.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The ImageWoof dataset structure is based on the dog breed classes, with each breed having its own directory of images. Similar to other classification datasets, it follows a split-directory format with separate folders for training and validation sets.
|
||||
|
||||
## Applications
|
||||
|
||||
The ImageWoof dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, especially when it comes to more complex and similar classes. The dataset's challenge lies in the subtle differences between the dog breeds, pushing the limits of model's performance and generalization. It's particularly valuable for:
|
||||
|
||||
- Benchmarking classification model performance on fine-grained categories
|
||||
- Testing model robustness against similar-looking classes
|
||||
- Developing algorithms that can distinguish subtle visual differences
|
||||
- Evaluating transfer learning capabilities from general to specific domains
|
||||
|
||||
## Usage
|
||||
|
||||
To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="imagewoof", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=imagewoof model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
## Dataset Variants
|
||||
|
||||
ImageWoof dataset comes in three different sizes to accommodate various research needs and computational capabilities:
|
||||
|
||||
1. **Full Size (imagewoof)**: This is the original version of the ImageWoof dataset. It contains full-sized images and is ideal for final training and performance benchmarking.
|
||||
|
||||
2. **Medium Size (imagewoof320)**: This version contains images resized to have a maximum edge length of 320 pixels. It's suitable for faster training without significantly sacrificing model performance.
|
||||
|
||||
3. **Small Size (imagewoof160)**: This version contains images resized to have a maximum edge length of 160 pixels. It's designed for rapid prototyping and experimentation where training speed is a priority.
|
||||
|
||||
To use these variants in your training, simply replace 'imagewoof' in the dataset argument with 'imagewoof320' or 'imagewoof160'. For example:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# For medium-sized dataset
|
||||
model.train(data="imagewoof320", epochs=100, imgsz=224)
|
||||
|
||||
# For small-sized dataset
|
||||
model.train(data="imagewoof160", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Load a pretrained model and train on the medium-sized dataset
|
||||
yolo classify train model=yolo26n-cls.pt data=imagewoof320 epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The ImageWoof dataset contains colorful images of various dog breeds, providing a challenging dataset for image classification tasks. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
||||
The example showcases the subtle differences and similarities among the different dog breeds in the ImageWoof dataset, highlighting the complexity and difficulty of the classification task.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the ImageWoof dataset in your research or development work, please make sure to acknowledge the creators of the dataset by linking to the [official dataset repository](https://github.com/fastai/imagenette).
|
||||
|
||||
We would like to acknowledge the [FastAI](https://www.fast.ai/) team for creating and maintaining the ImageWoof dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) research community. For more information about the ImageWoof dataset, visit the [ImageWoof dataset repository](https://github.com/fastai/imagenette).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the ImageWoof dataset in Ultralytics?
|
||||
|
||||
The [ImageWoof](https://github.com/fastai/imagenette) dataset is a challenging subset of ImageNet focusing on 10 specific dog breeds. Created to push the limits of image classification models, it features breeds like Beagle, Shih-Tzu, and Golden Retriever. The dataset includes images at various resolutions (full size, 320px, 160px) and even noisy labels for more realistic training scenarios. This complexity makes ImageWoof ideal for developing more advanced deep learning models.
|
||||
|
||||
### How can I train a model using the ImageWoof dataset with Ultralytics YOLO?
|
||||
|
||||
To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNN) model on the ImageWoof dataset using Ultralytics YOLO for 100 epochs at an image size of 224x224, you can use the following code:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo26n-cls.pt") # Load a pretrained model
|
||||
results = model.train(data="imagewoof", epochs=100, imgsz=224)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo classify train data=imagewoof model=yolo26n-cls.pt epochs=100 imgsz=224
|
||||
```
|
||||
|
||||
For more details on available training arguments, refer to the [Training](../../modes/train.md) page.
|
||||
|
||||
### What versions of the ImageWoof dataset are available?
|
||||
|
||||
The ImageWoof dataset comes in three sizes:
|
||||
|
||||
1. **Full Size (imagewoof)**: Ideal for final training and benchmarking, containing full-sized images.
|
||||
2. **Medium Size (imagewoof320)**: Resized images with a maximum edge length of 320 pixels, suited for faster training.
|
||||
3. **Small Size (imagewoof160)**: Resized images with a maximum edge length of 160 pixels, perfect for rapid prototyping.
|
||||
|
||||
Use these versions by replacing 'imagewoof' in the dataset argument accordingly. Note, however, that smaller images may yield lower classification [accuracy](https://www.ultralytics.com/glossary/accuracy) but can be useful for quicker iterations.
|
||||
|
||||
### How do noisy labels in the ImageWoof dataset benefit training?
|
||||
|
||||
Noisy labels in the ImageWoof dataset simulate real-world conditions where labels might not always be accurate. Training models with this data helps develop robustness and generalization in image classification tasks. This prepares the models to handle ambiguous or mislabeled data effectively, which is often encountered in practical applications.
|
||||
|
||||
### What are the key challenges of using the ImageWoof dataset?
|
||||
|
||||
The primary challenge of the ImageWoof dataset lies in the subtle differences among the dog breeds it includes. Since it focuses on 10 closely related breeds, distinguishing between them requires more advanced and fine-tuned image classification models. This makes ImageWoof an excellent benchmark to test the capabilities and improvements of [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models.
|
||||
210
algorithms/dms_yolo/code/docs/en/datasets/classify/index.md
Normal file
210
algorithms/dms_yolo/code/docs/en/datasets/classify/index.md
Normal file
@@ -0,0 +1,210 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn how to structure datasets for YOLO classification tasks. Detailed folder structure and usage examples for effective training.
|
||||
keywords: YOLO, image classification, dataset structure, CIFAR-10, Ultralytics, machine learning, training data, model evaluation
|
||||
---
|
||||
|
||||
# Image Classification Datasets Overview
|
||||
|
||||
## Dataset Structure for YOLO Classification Tasks
|
||||
|
||||
For [Ultralytics](https://www.ultralytics.com/) YOLO classification tasks, the dataset must be organized in a specific split-directory structure under the `root` directory to facilitate proper training, testing, and optional validation processes. This structure includes separate directories for training (`train`) and testing (`test`) phases, with an optional directory for validation (`val`).
|
||||
|
||||
Each of these directories should contain one subdirectory for each class in the dataset. The subdirectories are named after the corresponding class and contain all the images for that class. Ensure that each image file is named uniquely and stored in a common format such as JPEG or PNG.
|
||||
|
||||
### Folder Structure Example
|
||||
|
||||
Consider the [CIFAR-10](cifar10.md) dataset as an example. The folder structure should look like this:
|
||||
|
||||
```
|
||||
cifar-10-/
|
||||
|
|
||||
|-- train/
|
||||
| |-- airplane/
|
||||
| | |-- 10008_airplane.png
|
||||
| | |-- 10009_airplane.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- automobile/
|
||||
| | |-- 1000_automobile.png
|
||||
| | |-- 1001_automobile.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- bird/
|
||||
| | |-- 10014_bird.png
|
||||
| | |-- 10015_bird.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- ...
|
||||
|
|
||||
|-- test/
|
||||
| |-- airplane/
|
||||
| | |-- 10_airplane.png
|
||||
| | |-- 11_airplane.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- automobile/
|
||||
| | |-- 100_automobile.png
|
||||
| | |-- 101_automobile.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- bird/
|
||||
| | |-- 1000_bird.png
|
||||
| | |-- 1001_bird.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- ...
|
||||
|
|
||||
|-- val/ (optional)
|
||||
| |-- airplane/
|
||||
| | |-- 105_airplane.png
|
||||
| | |-- 106_airplane.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- automobile/
|
||||
| | |-- 102_automobile.png
|
||||
| | |-- 103_automobile.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- bird/
|
||||
| | |-- 1045_bird.png
|
||||
| | |-- 1046_bird.png
|
||||
| | |-- ...
|
||||
| |
|
||||
| |-- ...
|
||||
```
|
||||
|
||||
This structured approach ensures that the model can effectively learn from well-organized classes during the training phase and accurately evaluate performance during testing and validation phases.
|
||||
|
||||
## Usage
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=path/to/data model=yolo26n-cls.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! tip
|
||||
|
||||
Most built-in dataset names (for example `cifar10`, `imagenette`, or `mnist160`) will automatically download and cache the data the first time you reference them. Point `data` to a folder path only when you have curated a custom dataset.
|
||||
|
||||
## Supported Datasets
|
||||
|
||||
Ultralytics supports the following datasets with automatic download:
|
||||
|
||||
- [Caltech 101](caltech101.md): A dataset containing images of 101 object categories for [image classification](https://www.ultralytics.com/glossary/image-classification) tasks.
|
||||
- [Caltech 256](caltech256.md): An extended version of Caltech 101 with 256 object categories and more challenging images.
|
||||
- [CIFAR-10](cifar10.md): A dataset of 60K 32x32 color images in 10 classes, with 6K images per class.
|
||||
- [CIFAR-100](cifar100.md): An extended version of CIFAR-10 with 100 object categories and 600 images per class.
|
||||
- [Fashion-MNIST](fashion-mnist.md): A dataset consisting of 70,000 grayscale images of 10 fashion categories for image classification tasks.
|
||||
- [ImageNet](imagenet.md): A large-scale dataset for [object detection](https://www.ultralytics.com/glossary/object-detection) and image classification with over 14 million images and 20,000 categories.
|
||||
- [ImageNet-10](imagenet10.md): A smaller subset of ImageNet with 10 categories for faster experimentation and testing.
|
||||
- [Imagenette](imagenette.md): A smaller subset of ImageNet that contains 10 easily distinguishable classes for quicker training and testing.
|
||||
- [Imagewoof](imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
|
||||
- [MNIST](mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
|
||||
- [MNIST160](mnist.md): First 8 images of each MNIST category from the MNIST dataset. Dataset contains 160 images total.
|
||||
|
||||
### Adding your own dataset
|
||||
|
||||
If you have your own dataset and would like to use it for training classification models with Ultralytics YOLO, ensure that it follows the format specified above under "Dataset Structure" and then point your `data` argument to the dataset directory when initializing your training script.
|
||||
|
||||
## FAQ
|
||||
|
||||
### How do I structure my dataset for YOLO classification tasks?
|
||||
|
||||
To structure your dataset for Ultralytics YOLO classification tasks, you should follow a specific split-directory format. Organize your dataset into separate directories for `train`, `test`, and optionally `val`. Each of these directories should contain subdirectories named after each class, with the corresponding images inside. This facilitates smooth training and evaluation processes. For an example, consider the [CIFAR-10](cifar10.md) dataset format:
|
||||
|
||||
```
|
||||
cifar-10-/
|
||||
|-- train/
|
||||
| |-- airplane/
|
||||
| |-- automobile/
|
||||
| |-- bird/
|
||||
| ...
|
||||
|-- test/
|
||||
| |-- airplane/
|
||||
| |-- automobile/
|
||||
| |-- bird/
|
||||
| ...
|
||||
|-- val/ (optional)
|
||||
| |-- airplane/
|
||||
| |-- automobile/
|
||||
| |-- bird/
|
||||
| ...
|
||||
```
|
||||
|
||||
For more details, visit the [Dataset Structure for YOLO Classification Tasks](#dataset-structure-for-yolo-classification-tasks) section.
|
||||
|
||||
### What datasets are supported by Ultralytics YOLO for image classification?
|
||||
|
||||
Ultralytics YOLO supports automatic downloading of several datasets for image classification, including [Caltech 101](caltech101.md), [Caltech 256](caltech256.md), [CIFAR-10](cifar10.md), [CIFAR-100](cifar100.md), [Fashion-MNIST](fashion-mnist.md), [ImageNet](imagenet.md), [ImageNet-10](imagenet10.md), [Imagenette](imagenette.md), [Imagewoof](imagewoof.md), and [MNIST](mnist.md). These datasets are structured in a way that makes them easy to use with YOLO. Each dataset's page provides further details about its structure and applications.
|
||||
|
||||
### How do I add my own dataset for YOLO image classification?
|
||||
|
||||
To use your own dataset with Ultralytics YOLO, ensure it follows the specified directory format required for the classification task, with separate `train`, `test`, and optionally `val` directories, and subdirectories for each class containing the respective images. Once your dataset is structured correctly, point the `data` argument to your dataset's root directory when initializing the training script. Here's an example in Python:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="path/to/your/dataset", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
More details can be found in the [Adding your own dataset](#adding-your-own-dataset) section.
|
||||
|
||||
### Why should I use Ultralytics YOLO for image classification?
|
||||
|
||||
Ultralytics YOLO offers several benefits for image classification, including:
|
||||
|
||||
- **Pretrained Models**: Load pretrained models like `yolo26n-cls.pt` to jump-start your training process.
|
||||
- **Ease of Use**: Simple API and CLI commands for training and evaluation.
|
||||
- **High Performance**: State-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, ideal for real-time applications.
|
||||
- **Support for Multiple Datasets**: Seamless integration with various popular datasets like [CIFAR-10](cifar10.md), [ImageNet](imagenet.md), and more.
|
||||
- **Community and Support**: Access to extensive documentation and an active community for troubleshooting and improvements.
|
||||
|
||||
For additional insights and real-world applications, you can explore [Ultralytics YOLO](https://www.ultralytics.com/yolo).
|
||||
|
||||
### How can I train a model using Ultralytics YOLO?
|
||||
|
||||
Training a model using Ultralytics YOLO can be done easily in both Python and CLI. Here's an example:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=path/to/data model=yolo26n-cls.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
These examples demonstrate the straightforward process of training a YOLO model using either approach. For more information, visit the [Usage](#usage) section and the [Train](https://docs.ultralytics.com/tasks/classify/#train) page for classification tasks.
|
||||
157
algorithms/dms_yolo/code/docs/en/datasets/classify/mnist.md
Normal file
157
algorithms/dms_yolo/code/docs/en/datasets/classify/mnist.md
Normal file
@@ -0,0 +1,157 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the MNIST dataset, a cornerstone in machine learning for handwritten digit recognition. Learn about its structure, features, and applications.
|
||||
keywords: MNIST, dataset, handwritten digits, image classification, deep learning, machine learning, training set, testing set, NIST
|
||||
---
|
||||
|
||||
# MNIST Dataset
|
||||
|
||||
The [MNIST](https://en.wikipedia.org/wiki/MNIST_database) (Modified National Institute of Standards and Technology) dataset is a large database of handwritten digits that is commonly used for training various image processing systems and machine learning models. It was created by "re-mixing" the samples from NIST's original datasets and has become a benchmark for evaluating the performance of [image classification](https://www.ultralytics.com/glossary/image-classification) algorithms.
|
||||
|
||||
## Key Features
|
||||
|
||||
- MNIST contains 60,000 training images and 10,000 testing images of handwritten digits.
|
||||
- The dataset comprises grayscale images of size 28×28 pixels.
|
||||
- The images are normalized to fit into a 28×28 pixel [bounding box](https://www.ultralytics.com/glossary/bounding-box) and anti-aliased, introducing grayscale levels.
|
||||
- MNIST is widely used for training and testing in the field of machine learning, especially for image classification tasks.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The MNIST dataset is split into two subsets:
|
||||
|
||||
1. **Training Set**: This subset contains 60,000 images of handwritten digits used for training machine learning models.
|
||||
2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
|
||||
|
||||
## Dataset Access
|
||||
|
||||
- **Original files**: Download the gzip archives from [Yann LeCun's MNIST page](http://yann.lecun.com/exdb/mnist/) if you want direct control over preprocessing.
|
||||
- **Ultralytics loader**: Use `data="mnist"` (or `data="mnist160"` for the subset below) in your command and the dataset will be downloaded, converted to PNG, and cached automatically.
|
||||
|
||||
Each image in the dataset is labeled with the corresponding digit (0-9), making it a supervised learning dataset ideal for classification tasks.
|
||||
|
||||
## Extended MNIST (EMNIST)
|
||||
|
||||
Extended MNIST (EMNIST) is a newer dataset developed and released by NIST to be the successor to MNIST. While MNIST included images only of handwritten digits, EMNIST includes all the images from NIST Special Database 19, which is a large database of handwritten uppercase and lowercase letters as well as digits. The images in EMNIST were converted into the same 28×28 pixel format, by the same process, as were the MNIST images. Accordingly, tools that work with the older, smaller MNIST dataset will likely work unmodified with EMNIST.
|
||||
|
||||
## Applications
|
||||
|
||||
The MNIST dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. The dataset's simple and well-structured format makes it an essential resource for researchers and practitioners in the field of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
|
||||
Some common applications include:
|
||||
|
||||
- Benchmarking new classification algorithms
|
||||
- Educational purposes for teaching machine learning concepts
|
||||
- Prototyping image recognition systems
|
||||
- Testing model optimization techniques
|
||||
|
||||
## Usage
|
||||
|
||||
To train a CNN model on the MNIST dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 28×28, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="mnist", epochs=100, imgsz=28)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The MNIST dataset contains grayscale images of handwritten digits, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
|
||||
|
||||
|
||||
|
||||
The example showcases the variety and complexity of the handwritten digits in the MNIST dataset, highlighting the importance of a diverse dataset for training robust image classification models.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the MNIST dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{lecun2010mnist,
|
||||
title={MNIST handwritten digit database},
|
||||
author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
|
||||
journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
|
||||
volume={2},
|
||||
year={2010}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the machine learning and computer vision research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](https://en.wikipedia.org/wiki/MNIST_database).
|
||||
|
||||
## MNIST160 Quick Tests
|
||||
|
||||
Need a lightning-fast regression test? Ultralytics also exposes `data="mnist160"`, a 160-image slice containing the first eight samples from each digit class. It mirrors the MNIST directory structure, so you can swap datasets without changing any other arguments:
|
||||
|
||||
!!! example "Train Example with MNIST160"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo classify train data=mnist160 model=yolo26n-cls.pt epochs=5 imgsz=28
|
||||
```
|
||||
|
||||
Use this subset for CI pipelines or sanity checks before committing to the full 70,000-image dataset.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the MNIST dataset, and why is it important in machine learning?
|
||||
|
||||
The [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset, or Modified National Institute of Standards and Technology dataset, is a widely-used collection of handwritten digits designed for training and testing image classification systems. It includes 60,000 training images and 10,000 testing images, all of which are grayscale and 28×28 pixels in size. The dataset's importance lies in its role as a standard benchmark for evaluating image classification algorithms, helping researchers and engineers to compare methods and track progress in the field.
|
||||
|
||||
### How can I use Ultralytics YOLO to train a model on the MNIST dataset?
|
||||
|
||||
To train a model on the MNIST dataset using Ultralytics YOLO, you can follow these steps:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-cls.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="mnist", epochs=100, imgsz=28)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo classify train data=mnist model=yolo26n-cls.pt epochs=100 imgsz=28
|
||||
```
|
||||
|
||||
For a detailed list of available training arguments, refer to the [Training](../../modes/train.md) page.
|
||||
|
||||
### What is the difference between the MNIST and EMNIST datasets?
|
||||
|
||||
The MNIST dataset contains only handwritten digits, whereas the Extended MNIST (EMNIST) dataset includes both digits and uppercase and lowercase letters. EMNIST was developed as a successor to MNIST and utilizes the same 28×28 pixel format for the images, making it compatible with tools and models designed for the original MNIST dataset. This broader range of characters in EMNIST makes it useful for a wider variety of machine learning applications.
|
||||
|
||||
### Can I use Ultralytics Platform to train models on custom datasets like MNIST?
|
||||
|
||||
Yes, you can use [Ultralytics Platform](https://docs.ultralytics.com/platform/) to train models on custom datasets like MNIST. Ultralytics Platform offers a user-friendly interface for uploading datasets, training models, and managing projects without needing extensive coding knowledge. For more details on how to get started, check out the [Ultralytics Platform Quickstart](https://docs.ultralytics.com/platform/quickstart/) page.
|
||||
|
||||
### How does MNIST compare to other image classification datasets?
|
||||
|
||||
MNIST is simpler than many modern datasets like [CIFAR-10](../classify/cifar10.md) or [ImageNet](../classify/imagenet.md), making it ideal for beginners and quick experimentation. While more complex datasets offer greater challenges with color images and diverse object categories, MNIST remains valuable for its simplicity, small file size, and historical significance in the development of machine learning algorithms. For more advanced classification tasks, consider using [Fashion-MNIST](../classify/fashion-mnist.md), which maintains the same structure but features clothing items instead of digits.
|
||||
@@ -0,0 +1,166 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore our African Wildlife Dataset featuring images of buffalo, elephant, rhino, and zebra for training computer vision models. Ideal for research and conservation.
|
||||
keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLO26, wildlife research, conservation, dataset
|
||||
---
|
||||
|
||||
# African Wildlife Dataset
|
||||
|
||||
This dataset showcases four common animal classes typically found in South African nature reserves. It includes images of African wildlife such as buffalo, elephant, rhino, and zebra, providing valuable insights into their characteristics. Essential for training [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) algorithms, this dataset aids in identifying animals in various habitats, from zoos to forests, and supports wildlife research.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/biIW5Z6GYl0"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLO26
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The African wildlife objects detection dataset is split into three subsets:
|
||||
|
||||
- **Training set**: Contains 1052 images, each with corresponding annotations.
|
||||
- **Validation set**: Includes 225 images, each with paired annotations.
|
||||
- **Testing set**: Comprises 227 images, each with paired annotations.
|
||||
|
||||
## Applications
|
||||
|
||||
This dataset can be applied in various computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection), object tracking, and research. Specifically, it can be used to train and evaluate models for identifying African wildlife objects in images, which can have applications in wildlife conservation, ecological research, and monitoring efforts in natural reserves and protected areas. Additionally, it can serve as a valuable resource for educational purposes, enabling students and researchers to study and understand the characteristics and behaviors of different animal species.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file defines the dataset configuration, including paths, classes, and other pertinent details. For the African wildlife dataset, the `african-wildlife.yaml` file is located at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/african-wildlife.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/african-wildlife.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=african-wildlife.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("path/to/best.pt") # load an African wildlife fine-tuned model
|
||||
|
||||
# Inference using the model
|
||||
results = model.predict("https://ultralytics.com/assets/african-wildlife-sample.jpg")
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start prediction with a finetuned *.pt model
|
||||
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/african-wildlife-sample.jpg"
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The African wildlife dataset comprises a wide variety of images showcasing diverse animal species and their natural habitats. Below are examples of images from the dataset, each accompanied by its corresponding annotations.
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different object sizes, aspect ratios, and contexts.
|
||||
|
||||
This example illustrates the variety and complexity of images in the African wildlife dataset, emphasizing the benefits of including mosaicing during the training process.
|
||||
|
||||
## Citations, License and Acknowledgments
|
||||
|
||||
We'd like to thank the original dataset author, [Bianca Ferreira](https://www.kaggle.com/biancaferreira/datasets), for releasing this dataset to the community. The Ultralytics team has updated and adapted it internally so it can be used seamlessly with [Ultralytics YOLO](https://www.ultralytics.com/yolo) models. This dataset is available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
|
||||
If you use this dataset in your research, please cite it using the mentioned details:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
|
||||
@dataset{Ferreira_African_Wildlife_Ultralytics_Adaptation_2024,
|
||||
author = {Ferreira, Bianca},
|
||||
title = {African Wildlife Detection Dataset (Ultralytics YOLO Adaptation)},
|
||||
url = {https://docs.ultralytics.com/datasets/detect/african-wildlife/},
|
||||
note = {Original dataset by Bianca Ferreira; adapted for Ultralytics YOLO by Glenn Jocher and Muhammad Rizwan Munawar},
|
||||
license = {AGPL-3.0},
|
||||
version = {1.0.0},
|
||||
year = {2024}
|
||||
}
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the African Wildlife Dataset, and how can it be used in computer vision projects?
|
||||
|
||||
The African Wildlife Dataset includes images of four common animal species found in South African nature reserves: buffalo, elephant, rhino, and zebra. It is a valuable resource for training computer vision algorithms in object detection and animal identification. The dataset supports various tasks like object tracking, research, and conservation efforts. For more information on its structure and applications, refer to the [Dataset Structure](#dataset-structure) section and [Applications](#applications) of the dataset.
|
||||
|
||||
### How do I train a YOLO26 model using the African Wildlife Dataset?
|
||||
|
||||
You can train a YOLO26 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=african-wildlife.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional training parameters and options, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### Where can I find the YAML configuration file for the African Wildlife Dataset?
|
||||
|
||||
The YAML configuration file for the African Wildlife Dataset, named `african-wildlife.yaml`, can be found at [this GitHub link](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/african-wildlife.yaml). This file defines the dataset configuration, including paths, classes, and other details crucial for training [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models. See the [Dataset YAML](#dataset-yaml) section for more details.
|
||||
|
||||
### Can I see sample images and annotations from the African Wildlife Dataset?
|
||||
|
||||
Yes, the African Wildlife Dataset includes a wide variety of images showcasing diverse animal species in their natural habitats. You can view sample images and their corresponding annotations in the [Sample Images and Annotations](#sample-images-and-annotations) section. This section also illustrates the use of mosaicing technique to combine multiple images into one for enriched batch diversity, enhancing the model's generalization ability.
|
||||
|
||||
### How can the African Wildlife Dataset be used to support wildlife conservation and research?
|
||||
|
||||
The African Wildlife Dataset is ideal for supporting wildlife conservation and research by enabling the training and evaluation of models to identify African wildlife in different habitats. These models can assist in [monitoring animal populations](https://docs.ultralytics.com/solutions/), studying their behavior, and recognizing conservation needs. Additionally, the dataset can be utilized for educational purposes, helping students and researchers understand the characteristics and behaviors of different animal species. More details can be found in the [Applications](#applications) section.
|
||||
153
algorithms/dms_yolo/code/docs/en/datasets/detect/argoverse.md
Normal file
153
algorithms/dms_yolo/code/docs/en/datasets/detect/argoverse.md
Normal file
@@ -0,0 +1,153 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the comprehensive Argoverse dataset by Argo AI for 3D tracking, motion forecasting, and stereo depth estimation in autonomous driving research.
|
||||
keywords: Argoverse dataset, autonomous driving, 3D tracking, motion forecasting, stereo depth estimation, Argo AI, LiDAR point clouds, high-resolution images, HD maps
|
||||
---
|
||||
|
||||
# Argoverse Dataset
|
||||
|
||||
The [Argoverse](https://www.argoverse.org/) dataset is a collection of data designed to support research in autonomous driving tasks, such as 3D tracking, motion forecasting, and stereo depth estimation. Developed by Argo AI, the dataset provides a wide range of high-quality sensor data, including high-resolution images, LiDAR point clouds, and map data.
|
||||
|
||||
!!! note
|
||||
|
||||
The Argoverse dataset `*.zip` file required for training was removed from Amazon S3 after the shutdown of Argo AI by Ford, but we have made it available for manual download on [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
|
||||
|
||||
## Key Features
|
||||
|
||||
- Argoverse contains over 290K labeled 3D object tracks and 5 million object instances across 1,263 distinct scenes.
|
||||
- The dataset includes high-resolution camera images, LiDAR point clouds, and richly annotated HD maps.
|
||||
- Annotations include 3D bounding boxes for objects, object tracks, and trajectory information.
|
||||
- Argoverse provides multiple subsets for different tasks, such as 3D tracking, motion forecasting, and stereo depth estimation.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The Argoverse dataset is organized into three main subsets:
|
||||
|
||||
1. **Argoverse 3D Tracking**: This subset contains 113 scenes with over 290K labeled 3D object tracks, focusing on 3D object tracking tasks. It includes LiDAR point clouds, camera images, and sensor calibration information.
|
||||
2. **Argoverse Motion Forecasting**: This subset consists of 324K vehicle trajectories collected from 60 hours of driving data, suitable for motion forecasting tasks.
|
||||
3. **Argoverse Stereo Depth Estimation**: This subset is designed for stereo depth estimation tasks and includes over 10K stereo image pairs with corresponding LiDAR point clouds for ground truth depth estimation.
|
||||
|
||||
## Applications
|
||||
|
||||
The Argoverse dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in autonomous driving tasks such as 3D object tracking, motion forecasting, and stereo depth estimation. The dataset's diverse set of sensor data, object annotations, and map information make it a valuable resource for researchers and practitioners in the field of autonomous driving.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Argoverse dataset, the `Argoverse.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/Argoverse.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/Argoverse.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Argoverse.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The Argoverse dataset contains a diverse set of sensor data, including camera images, LiDAR point clouds, and HD map information, providing rich context for autonomous driving tasks. Here are some examples of data from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Argoverse 3D Tracking**: This image demonstrates an example of 3D object tracking, where objects are annotated with 3D bounding boxes. The dataset provides LiDAR point clouds and camera images to facilitate the development of models for this task.
|
||||
|
||||
The example showcases the variety and complexity of the data in the Argoverse dataset and highlights the importance of high-quality sensor data for autonomous driving tasks.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the Argoverse dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@inproceedings{chang2019argoverse,
|
||||
title={Argoverse: 3D Tracking and Forecasting with Rich Maps},
|
||||
author={Chang, Ming-Fang and Lambert, John and Sangkloy, Patsorn and Singh, Jagjeet and Bak, Slawomir and Hartnett, Andrew and Wang, Dequan and Carr, Peter and Lucey, Simon and Ramanan, Deva and others},
|
||||
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
||||
pages={8748--8757},
|
||||
year={2019}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Argoverse dataset and its key features?
|
||||
|
||||
The [Argoverse](https://www.argoverse.org/) dataset, developed by Argo AI, supports autonomous driving research. It includes over 290K labeled 3D object tracks and 5 million object instances across 1,263 distinct scenes. The dataset provides high-resolution camera images, LiDAR point clouds, and annotated HD maps, making it valuable for tasks like 3D tracking, motion forecasting, and stereo depth estimation.
|
||||
|
||||
### How can I train an Ultralytics YOLO model using the Argoverse dataset?
|
||||
|
||||
To train a YOLO26 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Argoverse.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a detailed explanation of the arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What types of data and annotations are available in the Argoverse dataset?
|
||||
|
||||
The Argoverse dataset includes various sensor data types such as high-resolution camera images, LiDAR point clouds, and HD map data. Annotations include 3D bounding boxes, object tracks, and trajectory information. These comprehensive annotations are essential for accurate model training in tasks like 3D object tracking, motion forecasting, and stereo depth estimation.
|
||||
|
||||
### How is the Argoverse dataset structured?
|
||||
|
||||
The dataset is divided into three main subsets:
|
||||
|
||||
1. **Argoverse 3D Tracking**: Contains 113 scenes with over 290K labeled 3D object tracks, focusing on 3D object tracking tasks. It includes LiDAR point clouds, camera images, and sensor calibration information.
|
||||
2. **Argoverse Motion Forecasting**: Consists of 324K vehicle trajectories collected from 60 hours of driving data, suitable for motion forecasting tasks.
|
||||
3. **Argoverse Stereo Depth Estimation**: Includes over 10K stereo image pairs with corresponding LiDAR point clouds for ground truth depth estimation.
|
||||
|
||||
### Where can I download the Argoverse dataset now that it has been removed from Amazon S3?
|
||||
|
||||
The Argoverse dataset `*.zip` file, previously available on Amazon S3, can now be manually downloaded from [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
|
||||
|
||||
### What is the YAML configuration file used for with the Argoverse dataset?
|
||||
|
||||
A YAML file contains the dataset's paths, classes, and other essential information. For the Argoverse dataset, the configuration file, `Argoverse.yaml`, can be found at the following link: [Argoverse.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml).
|
||||
|
||||
For more information about YAML configurations, see our [datasets](../index.md) guide.
|
||||
198
algorithms/dms_yolo/code/docs/en/datasets/detect/brain-tumor.md
Normal file
198
algorithms/dms_yolo/code/docs/en/datasets/detect/brain-tumor.md
Normal file
@@ -0,0 +1,198 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the brain tumor detection dataset with MRI/CT images. Essential for training AI models for early diagnosis and treatment planning.
|
||||
keywords: brain tumor dataset, MRI scans, CT scans, brain tumor detection, medical imaging, AI in healthcare, computer vision, early diagnosis, treatment planning
|
||||
---
|
||||
|
||||
# Brain Tumor Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-brain-tumor-detection-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Brain Tumor Dataset In Colab"></a>
|
||||
|
||||
A brain tumor detection dataset consists of medical images from MRI or CT scans, containing information about brain tumor presence, location, and characteristics. This dataset is essential for training [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) algorithms to automate brain tumor identification, aiding in early diagnosis and treatment planning in [healthcare applications](https://www.ultralytics.com/solutions/ai-in-healthcare).
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ogTBBD8McRk"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Brain Tumor Detection using Ultralytics Platform
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The brain tumor dataset is divided into two subsets:
|
||||
|
||||
- **Training set**: Consisting of 893 images, each accompanied by corresponding annotations.
|
||||
- **Testing set**: Comprising 223 images, with annotations paired for each one.
|
||||
|
||||
The dataset contains two classes:
|
||||
|
||||
- **Negative**: Images without brain tumors
|
||||
- **Positive**: Images with brain tumors
|
||||
|
||||
## Applications
|
||||
|
||||
The application of brain tumor detection using computer vision enables [early diagnosis](https://www.ultralytics.com/blog/ai-and-radiology-a-new-era-of-precision-and-efficiency), treatment planning, and monitoring of tumor progression. By analyzing medical imaging data like MRI or CT scans, [computer vision systems](https://docs.ultralytics.com/tasks/detect/) assist in accurately identifying brain tumors, aiding in timely medical intervention and personalized treatment strategies.
|
||||
|
||||
Medical professionals can leverage this technology to:
|
||||
|
||||
- Reduce diagnostic time and improve accuracy
|
||||
- Assist in surgical planning by precisely locating tumors
|
||||
- Monitor treatment effectiveness over time
|
||||
- Support research in oncology and neurology
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the brain tumor dataset, the `brain-tumor.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/brain-tumor.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/brain-tumor.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/brain-tumor.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/brain-tumor.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a [YOLO26](https://docs.ultralytics.com/models/yolo26/) model on the brain tumor dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=brain-tumor.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("path/to/best.pt") # load a brain-tumor fine-tuned model
|
||||
|
||||
# Inference using the model
|
||||
results = model.predict("https://ultralytics.com/assets/brain-tumor-sample.jpg")
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start prediction with a finetuned *.pt model
|
||||
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/brain-tumor-sample.jpg"
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The brain tumor dataset encompasses a wide array of medical images featuring brain scans with and without tumors. Presented below are examples of images from the dataset, accompanied by their respective annotations.
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: Displayed here is a training batch comprising mosaiced dataset images. Mosaicing, a training technique, consolidates multiple images into one, enhancing batch diversity. This approach aids in improving the model's capacity to generalize across various tumor sizes, shapes, and locations within brain scans.
|
||||
|
||||
This example highlights the diversity and intricacy of images within the brain tumor dataset, underscoring the advantages of incorporating mosaicing during the training phase for [medical image analysis](https://www.ultralytics.com/blog/using-yolo11-for-tumor-detection-in-medical-imaging).
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
The dataset has been made available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
|
||||
If you use this dataset in your research or development work, please cite it appropriately:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@dataset{Ultralytics_Brain_Tumor_Dataset_2023,
|
||||
author = {Ultralytics},
|
||||
title = {Brain Tumor Detection Dataset},
|
||||
year = {2023},
|
||||
publisher = {Ultralytics},
|
||||
url = {https://docs.ultralytics.com/datasets/detect/brain-tumor/}
|
||||
}
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the structure of the brain tumor dataset available in Ultralytics documentation?
|
||||
|
||||
The brain tumor dataset is divided into two subsets: the **training set** consists of 893 images with corresponding annotations, while the **testing set** comprises 223 images with paired annotations. This structured division aids in developing robust and accurate computer vision models for detecting brain tumors. For more information on the dataset structure, visit the [Dataset Structure](#dataset-structure) section.
|
||||
|
||||
### How can I train a YOLO26 model on the brain tumor dataset using Ultralytics?
|
||||
|
||||
You can train a YOLO26 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=brain-tumor.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a detailed list of available arguments, refer to the [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the benefits of using the brain tumor dataset for AI in healthcare?
|
||||
|
||||
Using the brain tumor dataset in AI projects enables early diagnosis and treatment planning for brain tumors. It helps in automating brain tumor identification through computer vision, facilitating accurate and timely medical interventions, and supporting personalized treatment strategies. This application holds significant potential in improving patient outcomes and medical efficiencies. For more insights on AI applications in healthcare, see [Ultralytics' healthcare solutions](https://www.ultralytics.com/solutions/ai-in-healthcare).
|
||||
|
||||
### How do I perform inference using a fine-tuned YOLO26 model on the brain tumor dataset?
|
||||
|
||||
Inference using a fine-tuned YOLO26 model can be performed with either Python or CLI approaches. Here are the examples:
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("path/to/best.pt") # load a brain-tumor fine-tuned model
|
||||
|
||||
# Inference using the model
|
||||
results = model.predict("https://ultralytics.com/assets/brain-tumor-sample.jpg")
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start prediction with a finetuned *.pt model
|
||||
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/brain-tumor-sample.jpg"
|
||||
```
|
||||
|
||||
### Where can I find the YAML configuration for the brain tumor dataset?
|
||||
|
||||
The YAML configuration file for the brain tumor dataset can be found at [brain-tumor.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/brain-tumor.yaml). This file includes paths, classes, and additional relevant information necessary for training and evaluating models on this dataset.
|
||||
173
algorithms/dms_yolo/code/docs/en/datasets/detect/coco.md
Normal file
173
algorithms/dms_yolo/code/docs/en/datasets/detect/coco.md
Normal file
@@ -0,0 +1,173 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the COCO dataset for object detection and segmentation. Learn about its structure, usage, pretrained models, and key features.
|
||||
keywords: COCO dataset, object detection, segmentation, benchmarking, computer vision, pose estimation, YOLO models, COCO annotations
|
||||
---
|
||||
|
||||
# COCO Dataset
|
||||
|
||||
The [COCO](https://cocodataset.org/#home) (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uDrn9QZJ2lk"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics COCO Dataset Overview
|
||||
</p>
|
||||
|
||||
## COCO Pretrained Models
|
||||
|
||||
{% include "macros/yolo-det-perf.md" %}
|
||||
|
||||
## Key Features
|
||||
|
||||
- COCO contains 330K images, with 200K images having annotations for object detection, segmentation, and captioning tasks.
|
||||
- The dataset comprises 80 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as umbrellas, handbags, and sports equipment.
|
||||
- Annotations include object bounding boxes, segmentation masks, and captions for each image.
|
||||
- COCO provides standardized evaluation metrics like [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) for object detection, and mean Average [Recall](https://www.ultralytics.com/glossary/recall) (mAR) for segmentation tasks, making it suitable for comparing model performance.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The COCO dataset is split into three subsets:
|
||||
|
||||
1. **Train2017**: This subset contains 118K images for training object detection, segmentation, and captioning models.
|
||||
2. **Val2017**: This subset has 5K images used for validation purposes during model training.
|
||||
3. **Test2017**: This subset consists of 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
|
||||
|
||||
## Applications
|
||||
|
||||
The COCO dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [Ultralytics YOLO](../../models/yolo26.md), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)), and keypoint detection (such as [OpenPose](https://arxiv.org/abs/1812.08008)). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the COCO dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The COCO dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the COCO dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO dataset and why is it important for computer vision?
|
||||
|
||||
The [COCO dataset](https://cocodataset.org/#home) (Common Objects in Context) is a large-scale dataset used for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and captioning. It contains 330K images with detailed annotations for 80 object categories, making it essential for benchmarking and training computer vision models. Researchers use COCO due to its diverse categories and standardized evaluation metrics like mean Average [Precision](https://www.ultralytics.com/glossary/precision) (mAP).
|
||||
|
||||
### How can I train a YOLO model using the COCO dataset?
|
||||
|
||||
To train a YOLO26 model using the COCO dataset, you can use the following code snippets:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
Refer to the [Training page](../../modes/train.md) for more details on available arguments.
|
||||
|
||||
### What are the key features of the COCO dataset?
|
||||
|
||||
The COCO dataset includes:
|
||||
|
||||
- 330K images, with 200K annotated for object detection, segmentation, and captioning.
|
||||
- 80 object categories ranging from common items like cars and animals to specific ones like handbags and sports equipment.
|
||||
- Standardized evaluation metrics for object detection (mAP) and segmentation (mean Average Recall, mAR).
|
||||
- **Mosaicing** technique in training batches to enhance model generalization across various object sizes and contexts.
|
||||
|
||||
### Where can I find pretrained YOLO26 models trained on the COCO dataset?
|
||||
|
||||
Pretrained YOLO26 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
|
||||
|
||||
- [YOLO26n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt)
|
||||
- [YOLO26s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt)
|
||||
- [YOLO26m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m.pt)
|
||||
- [YOLO26l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l.pt)
|
||||
- [YOLO26x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x.pt)
|
||||
|
||||
These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
|
||||
|
||||
### How is the COCO dataset structured and how do I use it?
|
||||
|
||||
The COCO dataset is split into three subsets:
|
||||
|
||||
1. **Train2017**: 118K images for training.
|
||||
2. **Val2017**: 5K images for validation during training.
|
||||
3. **Test2017**: 20K images for benchmarking trained models. Results need to be submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
|
||||
|
||||
The dataset's YAML configuration file is available at [coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml), which defines paths, classes, and dataset details.
|
||||
@@ -0,0 +1,234 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO12-Formats dataset, a test dataset featuring all 12 supported image formats (AVIF, BMP, DNG, HEIC, JP2, JPEG, JPG, MPO, PNG, TIF, TIFF, WebP) for validating image loading pipelines.
|
||||
keywords: COCO12-Formats, Ultralytics, dataset, image formats, object detection, YOLO, AVIF, BMP, DNG, HEIC, JP2, JPEG, PNG, TIFF, WebP, MPO
|
||||
---
|
||||
|
||||
# COCO12-Formats Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO12-Formats dataset is a specialized test dataset designed to validate image loading across all 12 supported image format extensions. It contains 12 images (6 for training, 6 for validation), each saved in a different format to ensure comprehensive testing of the image loading pipeline.
|
||||
|
||||
This dataset is invaluable for:
|
||||
|
||||
- **Testing image format support**: Verify that all supported formats load correctly
|
||||
- **CI/CD pipelines**: Automated testing of format compatibility
|
||||
- **Debugging**: Isolate format-specific issues in training pipelines
|
||||
- **Development**: Validate new format additions or changes
|
||||
|
||||
## Supported Formats
|
||||
|
||||
The dataset includes one image for each of the 12 supported format extensions defined in `ultralytics/data/utils.py`:
|
||||
|
||||
| Format | Extension | Description | Train/Val |
|
||||
| ------ | --------- | ------------------------------------ | --------- |
|
||||
| AVIF | `.avif` | AV1 Image File Format (modern) | Train |
|
||||
| BMP | `.bmp` | Bitmap - uncompressed raster format | Train |
|
||||
| DNG | `.dng` | Digital Negative - Adobe RAW format | Train |
|
||||
| HEIC | `.heic` | High Efficiency Image Coding | Train |
|
||||
| JPEG | `.jpeg` | JPEG with full extension | Train |
|
||||
| JPG | `.jpg` | JPEG with short extension | Train |
|
||||
| JP2 | `.jp2` | JPEG 2000 - medical/geospatial | Val |
|
||||
| MPO | `.mpo` | Multi-Picture Object (stereo images) | Val |
|
||||
| PNG | `.png` | Portable Network Graphics | Val |
|
||||
| TIF | `.tif` | TIFF with short extension | Val |
|
||||
| TIFF | `.tiff` | Tagged Image File Format | Val |
|
||||
| WebP | `.webp` | Modern web image format | Val |
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
```
|
||||
coco12-formats/
|
||||
├── images/
|
||||
│ ├── train/ # 6 images (avif, bmp, dng, heic, jpeg, jpg)
|
||||
│ └── val/ # 6 images (jp2, mpo, png, tif, tiff, webp)
|
||||
├── labels/
|
||||
│ ├── train/ # Corresponding YOLO format labels
|
||||
│ └── val/
|
||||
└── coco12-formats.yaml # Dataset configuration
|
||||
```
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
The COCO12-Formats dataset is configured using a YAML file that defines dataset paths and class names. You can review the official `coco12-formats.yaml` file in the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco12-formats.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco12-formats.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco12-formats.yaml"
|
||||
```
|
||||
|
||||
## Dataset Generation
|
||||
|
||||
The dataset can be generated using the provided script that converts source images from COCO8 and COCO128 to all supported formats:
|
||||
|
||||
```python
|
||||
from ultralytics.data.scripts.generate_coco12_formats import generate_coco12_formats
|
||||
|
||||
# Generate the dataset
|
||||
generate_coco12_formats()
|
||||
```
|
||||
|
||||
### Requirements
|
||||
|
||||
Some formats require additional dependencies:
|
||||
|
||||
```bash
|
||||
pip install pillow pillow-heif pillow-avif-plugin
|
||||
```
|
||||
|
||||
#### AVIF System Library (Optional)
|
||||
|
||||
For OpenCV to read AVIF files directly, `libavif` must be installed **before** building OpenCV:
|
||||
|
||||
=== "macOS"
|
||||
|
||||
```bash
|
||||
brew install libavif
|
||||
```
|
||||
|
||||
=== "Ubuntu/Debian"
|
||||
|
||||
```bash
|
||||
sudo apt install libavif-dev libavif-bin
|
||||
```
|
||||
|
||||
=== "From Source"
|
||||
|
||||
```bash
|
||||
git clone -b v1.2.1 https://github.com/AOMediaCodec/libavif.git
|
||||
cd libavif
|
||||
cmake -B build -DAVIF_CODEC_AOM=SYSTEM -DAVIF_BUILD_APPS=ON
|
||||
cmake --build build --config Release --parallel
|
||||
sudo cmake --install build
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
The pip-installed `opencv-python` package may not include AVIF support since it's pre-built. Ultralytics uses Pillow with `pillow-avif-plugin` as a fallback for AVIF images when OpenCV lacks support.
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO model on the COCO12-Formats dataset, use the following examples:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train on COCO12-Formats to test all image formats
|
||||
results = model.train(data="coco12-formats.yaml", epochs=1, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train YOLO on COCO12-Formats
|
||||
yolo detect train data=coco12-formats.yaml model=yolo26n.pt epochs=1 imgsz=640
|
||||
```
|
||||
|
||||
## Format-Specific Notes
|
||||
|
||||
### AVIF (AV1 Image File Format)
|
||||
|
||||
AVIF is a modern image format based on the AV1 video codec, offering excellent compression. Requires `pillow-avif-plugin`:
|
||||
|
||||
```bash
|
||||
pip install pillow-avif-plugin
|
||||
```
|
||||
|
||||
### DNG (Digital Negative)
|
||||
|
||||
DNG is Adobe's open RAW format based on TIFF. For testing purposes, the dataset uses TIFF-based files with the `.dng` extension.
|
||||
|
||||
### JP2 (JPEG 2000)
|
||||
|
||||
JPEG 2000 is a wavelet-based image compression standard offering better compression and quality than traditional JPEG. Commonly used in medical imaging (DICOM), geospatial applications, and digital cinema. Natively supported by both OpenCV and Pillow.
|
||||
|
||||
### MPO (Multi-Picture Object)
|
||||
|
||||
MPO files are used for stereoscopic (3D) images. The dataset stores standard JPEG data with the `.mpo` extension for format testing.
|
||||
|
||||
### HEIC (High Efficiency Image Coding)
|
||||
|
||||
HEIC requires the `pillow-heif` package for proper encoding:
|
||||
|
||||
```bash
|
||||
pip install pillow-heif
|
||||
```
|
||||
|
||||
## Use Cases
|
||||
|
||||
### CI/CD Testing
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
def test_all_image_formats():
|
||||
"""Test that all image formats load correctly."""
|
||||
model = YOLO("yolo26n.pt")
|
||||
results = model.train(data="coco12-formats.yaml", epochs=1, imgsz=64)
|
||||
assert results is not None
|
||||
```
|
||||
|
||||
### Format Validation
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.data.utils import IMG_FORMATS
|
||||
|
||||
# Verify all formats are represented
|
||||
dataset_dir = Path("datasets/coco12-formats/images")
|
||||
found_formats = {f.suffix[1:].lower() for f in dataset_dir.rglob("*.*")}
|
||||
assert found_formats == IMG_FORMATS, f"Missing formats: {IMG_FORMATS - found_formats}"
|
||||
```
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research, please cite:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Doll{\'a}r},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### What Is the COCO12-Formats Dataset Used For?
|
||||
|
||||
The COCO12-Formats dataset is designed for testing image format compatibility in Ultralytics YOLO training pipelines. It ensures all 12 supported image formats (AVIF, BMP, DNG, HEIC, JP2, JPEG, JPG, MPO, PNG, TIF, TIFF, WebP) load and process correctly.
|
||||
|
||||
### Why Test Multiple Image Formats?
|
||||
|
||||
Different image formats have unique characteristics (compression, bit depth, color spaces). Testing all formats ensures:
|
||||
|
||||
- Robust image loading code
|
||||
- Compatibility across diverse datasets
|
||||
- Early detection of format-specific bugs
|
||||
|
||||
### Which Formats Require Special Dependencies?
|
||||
|
||||
- **AVIF**: Requires `pillow-avif-plugin`
|
||||
- **HEIC**: Requires `pillow-heif`
|
||||
|
||||
### Can I Add New Format Tests?
|
||||
|
||||
Yes! Modify the `generate_coco12_formats.py` script to include additional formats. Ensure you also update `IMG_FORMATS` in `ultralytics/data/utils.py`.
|
||||
154
algorithms/dms_yolo/code/docs/en/datasets/detect/coco128.md
Normal file
154
algorithms/dms_yolo/code/docs/en/datasets/detect/coco128.md
Normal file
@@ -0,0 +1,154 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO128 dataset, a versatile and manageable set of 128 images perfect for testing object detection models and training pipelines.
|
||||
keywords: COCO128, Ultralytics, dataset, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO128 Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
[Ultralytics](https://www.ultralytics.com/) COCO128 is a small, but versatile [object detection](https://www.ultralytics.com/glossary/object-detection) dataset composed of the first 128 images of the COCO train 2017 set. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 128 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uDrn9QZJ2lk"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics COCO Dataset Overview
|
||||
</p>
|
||||
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO128 dataset, the `coco128.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco128.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco128.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the COCO128 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco128.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are some examples of images from the COCO128 dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-1.avif" alt="COCO128 object detection dataset mosaic training batch" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the COCO128 dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Ultralytics COCO128 dataset used for?
|
||||
|
||||
The Ultralytics COCO128 dataset is a compact subset containing the first 128 images from the COCO train 2017 dataset. It's primarily used for testing and debugging [object detection](https://www.ultralytics.com/glossary/object-detection) models, experimenting with new detection approaches, and validating training pipelines before scaling to larger datasets. Its manageable size makes it perfect for quick iterations while still providing enough diversity to be a meaningful test case.
|
||||
|
||||
### How do I train a YOLO26 model using the COCO128 dataset?
|
||||
|
||||
To train a YOLO26 model on the COCO128 dataset, you can use either Python or CLI commands. Here's how:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=coco128.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more training options and parameters, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### What are the benefits of using mosaic augmentation with COCO128?
|
||||
|
||||
Mosaic augmentation, as shown in the sample images, combines multiple training images into a single composite image. This technique offers several benefits when training with COCO128:
|
||||
|
||||
- Increases the variety of objects and contexts within each training batch
|
||||
- Improves model generalization across different object sizes and aspect ratios
|
||||
- Enhances detection performance for objects at various scales
|
||||
- Maximizes the utility of a small dataset by creating more diverse training samples
|
||||
|
||||
This technique is particularly valuable for smaller datasets like COCO128, helping models learn more robust features from limited data.
|
||||
|
||||
### How does COCO128 compare to other COCO dataset variants?
|
||||
|
||||
COCO128 (128 images) sits between [COCO8](../detect/coco8.md) (8 images) and the full [COCO](../detect/coco.md) dataset (118K+ images) in terms of size:
|
||||
|
||||
- **COCO8**: Contains just 8 images (4 train, 4 val) - ideal for quick tests and debugging
|
||||
- **COCO128**: Contains 128 images - balanced between size and diversity
|
||||
- **Full COCO**: Contains 118K+ training images - comprehensive but resource-intensive
|
||||
|
||||
COCO128 provides a good middle ground, offering more diversity than COCO8 while remaining much more manageable than the full COCO dataset for experimentation and initial model development.
|
||||
|
||||
### Can I use COCO128 for tasks other than object detection?
|
||||
|
||||
While COCO128 is primarily designed for object detection, the dataset's annotations can be adapted for other computer vision tasks:
|
||||
|
||||
- **Instance segmentation**: Using the segmentation masks provided in the annotations
|
||||
- **Keypoint detection**: For images containing people with keypoint annotations
|
||||
- **Transfer learning**: As a starting point for fine-tuning models for custom tasks
|
||||
|
||||
For specialized tasks like [segmentation](../../tasks/segment.md), consider using purpose-built variants like [COCO8-seg](../segment/coco8-seg.md) which include the appropriate annotations.
|
||||
@@ -0,0 +1,138 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO8-Grayscale dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
|
||||
keywords: COCO8-Grayscale, Ultralytics, dataset, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO8-Grayscale Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO8-Grayscale dataset is a compact yet powerful [object detection](https://www.ultralytics.com/glossary/object-detection) dataset, consisting of the first 8 images from the COCO train 2017 set and converted to grayscale format—4 for training and 4 for validation. This dataset is specifically designed for rapid testing, debugging, and experimentation with [YOLO](https://docs.ultralytics.com/models/yolo26/) grayscale models and training pipelines. Its small size makes it highly manageable, while its diversity ensures it serves as an effective sanity check before scaling up to larger datasets.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/yw2Fo6qjJU4"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on Grayscale Datasets 🚀
|
||||
</p>
|
||||
|
||||
COCO8-Grayscale is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](../../models/yolo26.md), enabling seamless integration into your computer vision workflows.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
The COCO8-Grayscale dataset configuration is defined in a YAML (Yet Another Markup Language) file, which specifies dataset paths, class names, and other essential metadata. You can review the official `coco8-grayscale.yaml` file in the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-grayscale.yaml).
|
||||
|
||||
!!! note
|
||||
|
||||
To train your RGB images in grayscale, you could simply add `channels: 1` to your dataset YAML file. This converts all images to grayscale during training, enabling you to utilize grayscale benefits without requiring a separate dataset.
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8-grayscale.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-grayscale.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the COCO8-Grayscale dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a full list of training options, see the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8-Grayscale
|
||||
results = model.train(data="coco8-grayscale.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train YOLO26n on COCO8-Grayscale using the command line
|
||||
yolo detect train data=coco8-grayscale.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Below is an example of a mosaiced training batch from the COCO8-Grayscale dataset:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/grayscale-mosaic.avif" alt="COCO8 grayscale dataset mosaic training batch" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image illustrates a training batch where multiple dataset images are combined using mosaic augmentation. Mosaic augmentation increases the diversity of objects and scenes within each batch, helping the model generalize better to various object sizes, aspect ratios, and backgrounds.
|
||||
|
||||
This technique is especially useful for small datasets like COCO8-Grayscale, as it maximizes the value of each image during training.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
Special thanks to the [COCO Consortium](https://cocodataset.org/#home) for their ongoing contributions to the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What Is the Ultralytics COCO8-Grayscale Dataset Used For?
|
||||
|
||||
The Ultralytics COCO8-Grayscale dataset is designed for rapid testing and debugging of [object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO](https://docs.ultralytics.com/models/yolo26/) training pipelines and ensuring everything works as expected before scaling to larger datasets. Explore the [COCO8-Grayscale YAML configuration](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-grayscale.yaml) for more details.
|
||||
|
||||
### How Do I Train a YOLO26 Model Using the COCO8-Grayscale Dataset?
|
||||
|
||||
You can train a YOLO26 model on COCO8-Grayscale using either Python or the CLI:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8-Grayscale
|
||||
results = model.train(data="coco8-grayscale.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=coco8-grayscale.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional training options, refer to the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
### Why Should I Use Ultralytics Platform for Managing My COCO8-Grayscale Training?
|
||||
|
||||
[Ultralytics Platform](https://platform.ultralytics.com/) streamlines dataset management, training, and deployment for [YOLO](https://docs.ultralytics.com/models/yolo26/) models—including COCO8-Grayscale. With features like cloud training, real-time monitoring, and intuitive dataset handling, HUB enables you to launch experiments with a single click and eliminates manual setup hassles. Learn more about [Ultralytics Platform](https://platform.ultralytics.com/) and how it can accelerate your computer vision projects.
|
||||
|
||||
### What Are the Benefits of Using Mosaic Augmentation in Training With the COCO8-Grayscale Dataset?
|
||||
|
||||
Mosaic augmentation, as used in COCO8-Grayscale training, combines multiple images into one during each batch. This increases the diversity of objects and backgrounds, helping your [YOLO](https://docs.ultralytics.com/models/yolo26/) model generalize better to new scenarios. Mosaic augmentation is especially valuable for small datasets, as it maximizes the information available in each training step. For more on this, see the [training guide](#usage).
|
||||
|
||||
### How Can I Validate My YOLO26 Model Trained on the COCO8-Grayscale Dataset?
|
||||
|
||||
To validate your YOLO26 model after training on COCO8-Grayscale, use the model's validation commands in either Python or CLI. This evaluates your model's performance using standard metrics. For step-by-step instructions, visit the [YOLO Validation documentation](../../modes/val.md).
|
||||
@@ -0,0 +1,146 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO8-Multispectral dataset, an enhanced version of COCO8 with interpolated spectral channels, ideal for testing multispectral object detection models and training pipelines.
|
||||
keywords: COCO8-Multispectral, Ultralytics, dataset, multispectral, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO8-Multispectral Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO8-Multispectral dataset is an advanced variant of the original COCO8 dataset, designed to facilitate experimentation with multispectral object detection models. It consists of the same 8 images from the COCO train 2017 set—4 for training and 4 for validation—but with each image transformed into a 10-channel multispectral format. By expanding beyond standard RGB channels, COCO8-Multispectral enables the development and evaluation of models that can leverage richer spectral information.
|
||||
|
||||
<p align="center">
|
||||
<img width="640" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/coco8-multispectral-overview.avif" alt="Multispectral imaging for object detection">
|
||||
</p>
|
||||
|
||||
COCO8-Multispectral is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](../../models/yolo26.md), ensuring seamless integration into your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) workflows.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/yw2Fo6qjJU4"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on Multispectral Datasets | Multi-Channel VisionAI 🚀
|
||||
</p>
|
||||
|
||||
## Dataset Generation
|
||||
|
||||
The multispectral images in COCO8-Multispectral were created by interpolating the original RGB images across 10 evenly spaced spectral channels within the visible spectrum. The process includes:
|
||||
|
||||
- **Wavelength Assignment**: Assigning nominal wavelengths to the RGB channels—Red: 650 nm, Green: 510 nm, Blue: 475 nm.
|
||||
- **Interpolation**: Using linear interpolation to estimate pixel values at intermediate wavelengths between 450 nm and 700 nm, resulting in 10 spectral channels.
|
||||
- **Extrapolation**: Applying extrapolation with SciPy's `interp1d` function to estimate values beyond the original RGB wavelengths, ensuring a complete spectral representation.
|
||||
|
||||
This approach simulates a multispectral imaging process, providing a more diverse set of data for model training and evaluation. For further reading on multispectral imaging, see the [Multispectral Imaging Wikipedia article](https://en.wikipedia.org/wiki/Multispectral_imaging).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
The COCO8-Multispectral dataset is configured using a YAML file, which defines dataset paths, class names, and essential metadata. You can review the official `coco8-multispectral.yaml` file in the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-multispectral.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8-multispectral.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-multispectral.yaml"
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
Prepare your TIFF images in `(channel, height, width)` order, saved with `.tiff` or `.tif` extension, and ensure they are `uint8` for use with Ultralytics:
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
# Create and write 10-channel TIFF
|
||||
image = np.ones((10, 640, 640), dtype=np.uint8) # CHW-order
|
||||
cv2.imwritemulti("example.tiff", image)
|
||||
|
||||
# Read TIFF
|
||||
success, frames_list = cv2.imreadmulti("example.tiff")
|
||||
image = np.stack(frames_list, axis=2)
|
||||
print(image.shape) # (640, 640, 10) HWC-order for training and inference
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the COCO8-Multispectral dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a comprehensive list of training options, refer to the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8-Multispectral
|
||||
results = model.train(data="coco8-multispectral.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train YOLO26n on COCO8-Multispectral using the command line
|
||||
yolo detect train data=coco8-multispectral.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details on model selection and best practices, explore the [Ultralytics YOLO model documentation](../../models/yolo26.md) and the [YOLO Model Training Tips guide](https://docs.ultralytics.com/guides/model-training-tips/).
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Below is an example of a mosaiced training batch from the COCO8-Multispectral dataset:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/coco8-multispectral-mosaic-batch.avif" alt="COCO8 multispectral dataset mosaic training batch" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch where multiple dataset images are combined using [mosaic augmentation](https://docs.ultralytics.com/reference/data/augment/). Mosaic augmentation increases the diversity of objects and scenes within each batch, helping the model generalize better to various object sizes, aspect ratios, and backgrounds.
|
||||
|
||||
This technique is especially valuable for small datasets like COCO8-Multispectral, as it maximizes the utility of each image during training.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
Special thanks to the [COCO Consortium](https://cocodataset.org/#home) for their ongoing contributions to the [computer vision community](https://www.ultralytics.com/blog/a-history-of-vision-models).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What Is the Ultralytics COCO8-Multispectral Dataset Used For?
|
||||
|
||||
The Ultralytics COCO8-Multispectral dataset is designed for rapid testing and debugging of [multispectral object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO26](../../models/yolo26.md) training pipelines and ensuring everything works as expected before scaling to larger datasets. For more datasets to experiment with, visit the [Ultralytics Datasets Catalog](https://docs.ultralytics.com/datasets/).
|
||||
|
||||
### How Does Multispectral Data Improve Object Detection?
|
||||
|
||||
Multispectral data provides additional spectral information beyond standard RGB, enabling models to distinguish objects based on subtle differences in reflectance across wavelengths. This can enhance detection accuracy, especially in challenging scenarios. Learn more about [multispectral imaging](https://en.wikipedia.org/wiki/Multispectral_imaging) and its applications in [advanced computer vision](https://www.ultralytics.com/blog/ai-in-aviation-a-runway-to-smarter-airports).
|
||||
|
||||
### Is COCO8-Multispectral Compatible With Ultralytics Platform and YOLO Models?
|
||||
|
||||
Yes, COCO8-Multispectral is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and all [YOLO models](../../models/yolo26.md), including the latest YOLO26. This allows you to easily integrate the dataset into your training and validation workflows.
|
||||
|
||||
### Where Can I Find More Information on Data Augmentation Techniques?
|
||||
|
||||
For a deeper understanding of data augmentation methods such as mosaic and their impact on model performance, refer to the [YOLO Data Augmentation Guide](https://docs.ultralytics.com/guides/yolo-data-augmentation/) and the [Ultralytics Blog on Data Augmentation](https://www.ultralytics.com/blog/the-ultimate-guide-to-data-augmentation-in-2025).
|
||||
|
||||
### Can I Use COCO8-Multispectral for Benchmarking or Educational Purposes?
|
||||
|
||||
Absolutely! The small size and multispectral nature of COCO8-Multispectral make it ideal for benchmarking, educational demonstrations, and prototyping new model architectures. For more benchmarking datasets, see the [Ultralytics Benchmark Dataset Collection](https://docs.ultralytics.com/datasets/).
|
||||
134
algorithms/dms_yolo/code/docs/en/datasets/detect/coco8.md
Normal file
134
algorithms/dms_yolo/code/docs/en/datasets/detect/coco8.md
Normal file
@@ -0,0 +1,134 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics COCO8 dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
|
||||
keywords: COCO8, Ultralytics, dataset, object detection, YOLO26, training, validation, machine learning, computer vision
|
||||
---
|
||||
|
||||
# COCO8 Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The [Ultralytics](https://www.ultralytics.com/) COCO8 dataset is a compact yet powerful [object detection](https://www.ultralytics.com/glossary/object-detection) dataset, consisting of the first 8 images from the COCO train 2017 set—4 for training and 4 for validation. This dataset is specifically designed for rapid testing, debugging, and experimentation with [YOLO](https://docs.ultralytics.com/models/yolo26/) models and training pipelines. Its small size makes it highly manageable, while its diversity ensures it serves as an effective sanity check before scaling up to larger datasets.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/uDrn9QZJ2lk"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics COCO Dataset Overview
|
||||
</p>
|
||||
|
||||
COCO8 is fully compatible with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](../../models/yolo26.md), enabling seamless integration into your computer vision workflows.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
The COCO8 dataset configuration is defined in a YAML (Yet Another Markup Language) file, which specifies dataset paths, class names, and other essential metadata. You can review the official `coco8.yaml` file in the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For a full list of training options, see the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8
|
||||
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train YOLO26n on COCO8 using the command line
|
||||
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Below is an example of a mosaiced training batch from the COCO8 dataset:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-1.avif" alt="COCO8 object detection dataset mosaic training batch" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image illustrates a training batch where multiple dataset images are combined using mosaic augmentation. Mosaic augmentation increases the diversity of objects and scenes within each batch, helping the model generalize better to various object sizes, aspect ratios, and backgrounds.
|
||||
|
||||
This technique is especially useful for small datasets like COCO8, as it maximizes the value of each image during training.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
Special thanks to the [COCO Consortium](https://cocodataset.org/#home) for their ongoing contributions to the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What Is the Ultralytics COCO8 Dataset Used For?
|
||||
|
||||
The Ultralytics COCO8 dataset is designed for rapid testing and debugging of [object detection](https://www.ultralytics.com/glossary/object-detection) models. With only 8 images (4 for training, 4 for validation), it is ideal for verifying your [YOLO](https://docs.ultralytics.com/models/yolo26/) training pipelines and ensuring everything works as expected before scaling to larger datasets. Explore the [COCO8 YAML configuration](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml) for more details.
|
||||
|
||||
### How Do I Train a YOLO26 Model Using the COCO8 Dataset?
|
||||
|
||||
You can train a YOLO26 model on COCO8 using either Python or the CLI:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on COCO8
|
||||
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional training options, refer to the [YOLO Training documentation](../../modes/train.md).
|
||||
|
||||
### Why Should I Use Ultralytics Platform for Managing My COCO8 Training?
|
||||
|
||||
[Ultralytics Platform](https://platform.ultralytics.com/) streamlines dataset management, training, and deployment for [YOLO](https://docs.ultralytics.com/models/yolo26/) models—including COCO8. With features like cloud training, real-time monitoring, and intuitive dataset handling, HUB enables you to launch experiments with a single click and eliminates manual setup hassles. Learn more about [Ultralytics Platform](https://platform.ultralytics.com/) and how it can accelerate your computer vision projects.
|
||||
|
||||
### What Are the Benefits of Using Mosaic Augmentation in Training With the COCO8 Dataset?
|
||||
|
||||
Mosaic augmentation, as used in COCO8 training, combines multiple images into one during each batch. This increases the diversity of objects and backgrounds, helping your [YOLO](https://docs.ultralytics.com/models/yolo26/) model generalize better to new scenarios. Mosaic augmentation is especially valuable for small datasets, as it maximizes the information available in each training step. For more on this, see the [training guide](#usage).
|
||||
|
||||
### How Can I Validate My YOLO26 Model Trained on the COCO8 Dataset?
|
||||
|
||||
To validate your YOLO26 model after training on COCO8, use the model's validation commands in either Python or CLI. This evaluates your model's performance using standard metrics. For step-by-step instructions, visit the [YOLO Validation documentation](../../modes/val.md).
|
||||
@@ -0,0 +1,157 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover Construction-PPE, a specialized dataset for detecting helmets, vests, gloves, boots, and goggles in real-world construction sites. Includes compliant and non-compliant scenarios for AI-powered safety monitoring.
|
||||
keywords: Construction-PPE, PPE dataset, safety compliance, construction workers, object detection, YOLO26, workplace safety, computer vision
|
||||
---
|
||||
|
||||
# Construction-PPE Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-construction-ppe-detection-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Construction-PPE Dataset In Colab"></a>
|
||||
|
||||
The Construction-PPE dataset is designed to improve safety compliance in construction sites by enabling detection of essential protective gear such as helmets, vests, gloves, boots, and goggles, along with annotations for missing equipment. Curated from real construction environments, it includes both compliant and non-compliant cases, making it a valuable resource for training AI models that monitor workplace safety.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lFaVnrhMmaE"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to train Ultralytics YOLO on Personal Protective Equipment Dataset | VisionAI in Construction 👷
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The Construction-PPE dataset is organized into three main subsets:
|
||||
|
||||
- **Training Set**: The primary collection of annotated construction images featuring workers with both complete and partial PPE usage.
|
||||
- **Validation Set**: A designated subset used to fine-tune and assess model performance during PPE detection and compliance monitoring.
|
||||
- **Test Set**: An independent subset reserved for evaluating the final model's effectiveness in detecting PPE and identifying compliance issues.
|
||||
|
||||
Each image is annotated in the [Ultralytics YOLO](../detect/index.md/#what-is-the-ultralytics-yolo-dataset-format-and-how-to-structure-it) format ensuring compatibility with state-of-the-art [object detection](../../tasks/detect.md) and [tracking](../../modes/track.md) pipelines.
|
||||
|
||||
The dataset provides **11 classes** divided into positive (worn PPE) and negative (missing PPE) categories. This dual-positive/negative structure enables models to detect properly worn gear **and** identify safety violations.
|
||||
|
||||
## Business Value
|
||||
|
||||
- Construction remains one of the most hazardous industries in the world, with over 51 out of 123 work related **fatal injuries** in the UK in 2023/2024 happening in construction. However, the issue is no longer an issue with lack of regulation with 42% of construction workers admitting to not always adhering to processes.
|
||||
- Construction is already governed by an extensive framework of health and safety (HSE) standards, but HSE teams are challenged with consistent enforcement. HSE teams are often stretched thin, balancing paperwork and audits and lacking the ability to monitor every corner of a busy and ever-changing environment in real time.
|
||||
- This is where computer vision based personal protective equipment (PPE) detection becomes invaluable. By automatically checking whether workers are wearing **helmets, vests and other personal protective equipment**, you can ensure HSE rules are not just present but effectively enforced consistently across all sites. Beyond compliance, computer vision provides leading indicators of risk by revealing how well crews follow safety practices, enabling organizations to spot downward trends in compliance and prevent incidents before they happen.
|
||||
- As a bonus, personal protective equipment detection has also been known to identify unauthorized site intruders, since **those not equipped with proper safety gear** are the first to trigger a notification. Ultimately, PPE detection is a simple yet powerful computer vision use-case that delivers full oversight, actionable insights and standardized reporting, empowering construction firms to reduce risk, protect workers and safeguard their projects.
|
||||
|
||||
## Applications
|
||||
|
||||
Construction-PPE powers a variety of safety-focused computer vision applications:
|
||||
|
||||
- **Automated compliance monitoring**: Train AI models to instantly check if workers are wearing required safety gear like helmets, vests, or gloves, reducing risks on site.
|
||||
- **Workplace safety analytics**: Track PPE usage over time, spot frequent violations, and generate insights to improve safety culture.
|
||||
- **Smart surveillance systems**: Connect detection models with cameras to send real-time alerts when PPE is missing, preventing accidents before they happen.
|
||||
- **Robotics and autonomous systems**: Enable drones or robots to perform PPE checks across large sites, supporting faster and safer inspections.
|
||||
- **Research and education**: Provide a real-world dataset for students and researchers exploring workplace safety and human-object interactions.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
The Construction-PPE dataset includes a YAML configuration file that defines the training and validation image paths along with the full list of object classes. You can access the `construction-ppe.yaml` file directly in the Ultralytics repository here: [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/construction-ppe.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/construction-ppe.yaml)
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/construction-ppe.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/construction-ppe.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
You can train a YOLO26n model on the Construction-PPE dataset for 100 epochs with an image size of 640. The following examples show how to get started quickly. For more options and advanced configurations, see the [Training guide](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load pretrained model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on Construction-PPE dataset
|
||||
model.train(data="construction-ppe.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=construction-ppe.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The dataset captures construction workers across varied environments, lighting conditions, and postures. Both **compliant** and **non-compliant** cases are included.
|
||||
|
||||
|
||||
|
||||
## License and Attribution
|
||||
|
||||
Construction-PPE is developed and released under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), supporting open-source research and commercial applications with proper attribution.
|
||||
|
||||
If you use this dataset in your research, please cite it:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@dataset{Dalvi_Construction_PPE_Dataset_2025,
|
||||
author = {Mrunmayee Dalvi and Niyati Singh and Sahil Bhingarde and Ketaki Chalke},
|
||||
title = {Construction-PPE: Personal Protective Equipment Detection Dataset},
|
||||
month = {January},
|
||||
year = {2025},
|
||||
version = {1.0.0},
|
||||
license = {AGPL-3.0},
|
||||
url = {https://docs.ultralytics.com/datasets/detect/construction-ppe/},
|
||||
publisher = {Ultralytics}
|
||||
}
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### What makes the Construction-PPE dataset unique?
|
||||
|
||||
Unlike generic construction datasets, Construction-PPE explicitly includes **missing equipment classes**. This dual-labeling approach allows models to not only detect PPE but also flag violations in real-time.
|
||||
|
||||
### Which object categories are included?
|
||||
|
||||
The dataset covers helmets, vests, gloves, boots, goggles, and workers, along with their “missing PPE” counterparts. This ensures comprehensive compliance coverage.
|
||||
|
||||
### How can I train a YOLO model using the Construction-PPE dataset?
|
||||
|
||||
To train a YOLO26 model using the Construction-PPE dataset, you can use the following code snippets:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="construction-ppe.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=construction-ppe.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Is this dataset suitable for real-world applications?
|
||||
|
||||
Yes. Images are curated from real construction sites under diverse conditions. This makes it highly effective for building deployable workplace safety monitoring systems.
|
||||
|
||||
### What are the benefits of using the Construction-PPE dataset in AI projects?
|
||||
|
||||
The dataset enables real-time detection of personal protective equipment, helping monitor worker safety on construction sites. With classes for both worn and missing gear, it supports AI systems that can automatically flag safety violations, generate compliance insights, and reduce risks. It also provides a practical resource for developing computer vision solutions in workplace safety, robotics, and academic research.
|
||||
@@ -0,0 +1,145 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Global Wheat Head Dataset to develop accurate wheat head detection models. Includes training images, annotations, and usage for crop management.
|
||||
keywords: Global Wheat Head Dataset, wheat head detection, wheat phenotyping, crop management, deep learning, object detection, training datasets
|
||||
---
|
||||
|
||||
# Global Wheat Head Dataset
|
||||
|
||||
The [Global Wheat Head Dataset](https://www.global-wheat.com/) is a collection of images designed to support the development of accurate wheat head detection models for applications in wheat phenotyping and crop management. Wheat heads, also known as spikes, are the grain-bearing parts of the wheat plant. Accurate estimation of wheat head density and size is essential for assessing crop health, maturity, and yield potential. The dataset, created by a collaboration of nine research institutes from seven countries, covers multiple growing regions to ensure models generalize well across different environments.
|
||||
|
||||
## Key Features
|
||||
|
||||
- The dataset contains over 3,000 training images from Europe (France, UK, Switzerland) and North America (Canada).
|
||||
- It includes approximately 1,000 test images from Australia, Japan, and China.
|
||||
- Images are outdoor field images, capturing the natural variability in wheat head appearances.
|
||||
- Annotations include wheat head bounding boxes to support [object detection](https://docs.ultralytics.com/tasks/detect/) tasks.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The Global Wheat Head Dataset is organized into two main subsets:
|
||||
|
||||
1. **Training Set**: This subset contains over 3,000 images from Europe and North America. The images are labeled with wheat head bounding boxes, providing ground truth for training object detection models.
|
||||
2. **Test Set**: This subset consists of approximately 1,000 images from Australia, Japan, and China. These images are used for evaluating the performance of trained models on unseen genotypes, environments, and observational conditions.
|
||||
|
||||
## Applications
|
||||
|
||||
The Global Wheat Head Dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in wheat head detection tasks. The dataset's diverse set of images, capturing a wide range of appearances, environments, and conditions, make it a valuable resource for researchers and practitioners in the field of [plant phenotyping](https://www.ultralytics.com/blog/computer-vision-in-agriculture-transforming-fruit-detection-and-precision-farming) and crop management.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Global Wheat Head Dataset, the `GlobalWheat2020.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=GlobalWheat2020.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The Global Wheat Head Dataset contains a diverse set of outdoor field images, capturing the natural variability in wheat head appearances, environments, and conditions. Here are some examples of data from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Wheat Head Detection**: This image demonstrates an example of wheat head detection, where wheat heads are annotated with bounding boxes. The dataset provides a variety of images to facilitate the development of models for this task.
|
||||
|
||||
The example showcases the variety and complexity of the data in the Global Wheat Head Dataset and highlights the importance of accurate wheat head detection for applications in wheat phenotyping and crop management.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the Global Wheat Head Dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{david2020global,
|
||||
title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
|
||||
author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
|
||||
journal={arXiv preprint arXiv:2005.02162},
|
||||
year={2020}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](https://www.global-wheat.com/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Global Wheat Head Dataset used for?
|
||||
|
||||
The Global Wheat Head Dataset is primarily used for developing and training deep learning models aimed at wheat head detection. This is crucial for applications in [wheat phenotyping](https://www.ultralytics.com/blog/from-farm-to-table-how-ai-drives-innovation-in-agriculture) and crop management, allowing for more accurate estimations of wheat head density, size, and overall crop yield potential. Accurate detection methods help in assessing crop health and maturity, essential for efficient crop management.
|
||||
|
||||
### How do I train a YOLO26n model on the Global Wheat Head Dataset?
|
||||
|
||||
To train a YOLO26n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model (recommended for training)
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=GlobalWheat2020.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the key features of the Global Wheat Head Dataset?
|
||||
|
||||
Key features of the Global Wheat Head Dataset include:
|
||||
|
||||
- Over 3,000 training images from Europe (France, UK, Switzerland) and North America (Canada).
|
||||
- Approximately 1,000 test images from Australia, Japan, and China.
|
||||
- High variability in wheat head appearances due to different growing environments.
|
||||
- Detailed annotations with wheat head bounding boxes to aid [object detection](https://www.ultralytics.com/glossary/object-detection) models.
|
||||
|
||||
These features facilitate the development of robust models capable of generalization across multiple regions.
|
||||
|
||||
### Where can I find the configuration YAML file for the Global Wheat Head Dataset?
|
||||
|
||||
The configuration YAML file for the Global Wheat Head Dataset, named `GlobalWheat2020.yaml`, is available on GitHub. You can access it at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml>. This file contains necessary information about dataset paths, classes, and other configuration details needed for model training in [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/).
|
||||
|
||||
### Why is wheat head detection important in crop management?
|
||||
|
||||
Wheat head detection is critical in crop management because it enables accurate estimation of wheat head density and size, which are essential for evaluating crop health, maturity, and yield potential. By leveraging [deep learning models](https://docs.ultralytics.com/models/) trained on datasets like the Global Wheat Head Dataset, farmers and researchers can better monitor and manage crops, leading to improved productivity and optimized resource use in agricultural practices. This technological advancement supports [sustainable agriculture](https://www.ultralytics.com/blog/real-time-crop-health-monitoring-with-ultralytics-yolo11) and food security initiatives.
|
||||
|
||||
For more information on applications of AI in agriculture, visit [AI in Agriculture](https://www.ultralytics.com/solutions/ai-in-agriculture).
|
||||
@@ -0,0 +1,169 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover HomeObjects-3K, a rich indoor object detection dataset with 12 classes like bed, sofa, TV, and laptop. Ideal for computer vision in smart homes, robotics, and AR.
|
||||
keywords: HomeObjects-3K, indoor dataset, household items, object detection, computer vision, YOLO26, smart home AI, robotics dataset
|
||||
---
|
||||
|
||||
# HomeObjects-3K Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-homeobjects-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="HomeObjects-3K Dataset In Colab"></a>
|
||||
|
||||
The HomeObjects-3K dataset is a curated collection of common household object images, designed for training, testing, and [benchmarking](../../modes/benchmark.md) [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models. Featuring ~3,000 images and 12 distinct object classes, this dataset is ideal for research and applications in indoor scene understanding, smart home devices, [robotics](https://www.ultralytics.com/glossary/robotics), and augmented reality.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/v3iqOYoRBFQ"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on HomeObjects-3K Dataset | Detection, Validation & ONNX Export 🚀
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The HomeObjects-3K dataset is organized into the following subsets:
|
||||
|
||||
- **Training Set**: Comprises 2,285 annotated images featuring objects such as sofas, chairs, tables, lamps, and more.
|
||||
- **Validation Set**: Includes 404 annotated images designated for evaluating model performance.
|
||||
|
||||
Each image is labeled using bounding boxes aligned with the [Ultralytics YOLO](../detect/index.md/#what-is-the-ultralytics-yolo-dataset-format-and-how-to-structure-it) format. The diversity of indoor lighting, object scale, and orientations makes it robust for real-world deployment scenarios.
|
||||
|
||||
## Object Classes
|
||||
|
||||
The dataset supports 12 everyday object categories, covering furniture, electronics, and decorative items. These classes are chosen to reflect common items encountered in indoor domestic environments and support vision tasks like [object detection](../../tasks/detect.md) and [object tracking](../../modes/track.md).
|
||||
|
||||
!!! Tip "HomeObjects-3K classes"
|
||||
|
||||
0. bed
|
||||
1. sofa
|
||||
2. chair
|
||||
3. table
|
||||
4. lamp
|
||||
5. tv
|
||||
6. laptop
|
||||
7. wardrobe
|
||||
8. window
|
||||
9. door
|
||||
10. potted plant
|
||||
11. photo frame
|
||||
|
||||
## Applications
|
||||
|
||||
HomeObjects-3K enables a wide spectrum of applications in indoor computer vision, spanning both research and real-world product development:
|
||||
|
||||
- **Indoor object detection**: Use models like [Ultralytics YOLO26](../../models/yolo26.md) to find and locate common home items like beds, chairs, lamps, and laptops in images. This helps with real-time understanding of indoor scenes.
|
||||
|
||||
- **Scene layout parsing**: In robotics and smart home systems, this helps devices understand how rooms are arranged, where objects like doors, windows, and furniture are, so they can navigate safely and interact with their environment properly.
|
||||
|
||||
- **AR applications**: Power [object recognition](http://ultralytics.com/glossary/image-recognition) features in apps that use augmented reality. For example, detect TVs or wardrobes and show extra information or effects on them.
|
||||
|
||||
- **Education and research**: Support learning and academic projects by giving students and researchers a ready-to-use dataset for practicing indoor object detection with real-world examples.
|
||||
|
||||
- **Home inventory and asset tracking**: Automatically detect and list home items in photos or videos, useful for managing belongings, organizing spaces, or visualizing furniture in real estate.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
The configuration for the HomeObjects-3K dataset is provided through a YAML file. This file outlines essential information such as image paths for train and validation directories, and the list of object classes.
|
||||
You can access the `HomeObjects-3K.yaml` file directly from the Ultralytics repository at: [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/HomeObjects-3K.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/HomeObjects-3K.yaml)
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/HomeObjects-3K.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/HomeObjects-3K.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
You can train a YOLO26n model on the HomeObjects-3K dataset for 100 epochs using an image size of 640. The examples below show how to get started. For more training options and detailed settings, check the [Training](../../modes/train.md) guide.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load pretrained model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on HomeObjects-3K dataset
|
||||
model.train(data="HomeObjects-3K.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=HomeObjects-3K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The dataset features a rich collection of indoor scene images that capture a wide range of household objects in natural home environments. Below are sample visuals from the dataset, each paired with its corresponding annotations to illustrate object positions, scales, and spatial relationships.
|
||||
|
||||
|
||||
|
||||
## License and Attribution
|
||||
|
||||
HomeObjects-3K is developed and released by the **[Ultralytics team](https://www.ultralytics.com/about)** under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), supporting open-source research and commercial use with proper attribution.
|
||||
|
||||
If you use this dataset in your research, please cite it using the mentioned details:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@dataset{Jocher_Ultralytics_Datasets_2025,
|
||||
author = {Jocher, Glenn and Rizwan, Muhammad},
|
||||
license = {AGPL-3.0},
|
||||
month = {May},
|
||||
title = {Ultralytics Datasets: HomeObjects-3K Detection Dataset},
|
||||
url = {https://docs.ultralytics.com/datasets/detect/homeobjects-3k/},
|
||||
version = {1.0.0},
|
||||
year = {2025}
|
||||
}
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the HomeObjects-3K dataset designed for?
|
||||
|
||||
HomeObjects-3K is crafted for advancing AI understanding of indoor scenes. It focuses on detecting everyday household items—like beds, sofas, TVs, and lamps—making it ideal for applications in smart homes, robotics, augmented reality, and interior monitoring systems. Whether you're training models for real-time edge devices or academic research, this dataset provides a balanced foundation.
|
||||
|
||||
### Which object categories are included, and why were they selected?
|
||||
|
||||
The dataset includes 12 of the most commonly encountered household items: bed, sofa, chair, table, lamp, tv, laptop, wardrobe, window, door, potted plant, and photo frame. These objects were chosen to reflect realistic indoor environments and to support multipurpose tasks such as robotic navigation, or scene generation in AR/VR applications.
|
||||
|
||||
### How can I train a YOLO model using the HomeObjects-3K dataset?
|
||||
|
||||
To train a YOLO model like YOLO26n, you'll just need the `HomeObjects-3K.yaml` configuration file and the [pretrained model](../../models/index.md) weights. Whether you're using Python or the CLI, training can be launched with a single command. You can customize parameters such as epochs, image size, and batch size depending on your target performance and hardware setup.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load pretrained model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on HomeObjects-3K dataset
|
||||
model.train(data="HomeObjects-3K.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=HomeObjects-3K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Is this dataset suitable for beginner-level projects?
|
||||
|
||||
Absolutely. With clean labeling, and standardized YOLO-compatible annotations, HomeObjects-3K is an excellent entry point for students and hobbyists who want to explore real-world object detection in indoor scenarios. It also scales well for more complex applications in commercial environments.
|
||||
|
||||
### Where can I find the annotation format and YAML?
|
||||
|
||||
Refer to the [Dataset YAML](#dataset-yaml) section. The format is standard YOLO, making it compatible with most object detection pipelines.
|
||||
343
algorithms/dms_yolo/code/docs/en/datasets/detect/index.md
Normal file
343
algorithms/dms_yolo/code/docs/en/datasets/detect/index.md
Normal file
@@ -0,0 +1,343 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn about dataset formats compatible with Ultralytics YOLO for robust object detection. Explore supported datasets and learn how to convert formats.
|
||||
keywords: Ultralytics, YOLO, object detection datasets, dataset formats, COCO, dataset conversion, training datasets
|
||||
---
|
||||
|
||||
# Object Detection Datasets Overview
|
||||
|
||||
Training a robust and accurate [object detection](https://www.ultralytics.com/glossary/object-detection) model requires a comprehensive dataset. This guide introduces various formats of datasets that are compatible with the Ultralytics YOLO model and provides insights into their structure, usage, and how to convert between different formats.
|
||||
|
||||
## Supported Dataset Formats
|
||||
|
||||
### Ultralytics YOLO format
|
||||
|
||||
The Ultralytics YOLO format is a dataset configuration format that allows you to define the dataset root directory, the relative paths to training/validation/testing image directories or `*.txt` files containing image paths, and a dictionary of class names. Here is an example:
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8.yaml"
|
||||
```
|
||||
|
||||
Labels for this format should be exported to YOLO format with one `*.txt` file per image. If there are no objects in an image, no `*.txt` file is required. The `*.txt` file should be formatted with one row per object in `class x_center y_center width height` format. Box coordinates must be in **normalized xywh** format (from 0 to 1). If your boxes are in pixels, you should divide `x_center` and `width` by image width, and `y_center` and `height` by image height. Class numbers should be zero-indexed (start with 0).
|
||||
|
||||
<p align="center"><img width="750" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/two-persons-tie.avif" alt="YOLO labeled image with bounding boxes on persons and tie"></p>
|
||||
|
||||
The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`):
|
||||
|
||||
<p align="center"><img width="428" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/two-persons-tie-1.avif" alt="YOLO format label file with normalized coordinates"></p>
|
||||
|
||||
When using the Ultralytics YOLO format, organize your training and validation images and labels as shown in the [COCO8 dataset](coco8.md) example below.
|
||||
|
||||
<p align="center"><img width="800" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/two-persons-tie-2.avif" alt="YOLO dataset directory structure with train and val folders"></p>
|
||||
|
||||
#### Usage Example
|
||||
|
||||
Here's how you can use YOLO format datasets to train your model:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Ultralytics NDJSON format
|
||||
|
||||
The NDJSON (Newline Delimited JSON) format provides an alternative way to define datasets for Ultralytics YOLO models. This format stores dataset metadata and annotations in a single file where each line contains a separate JSON object.
|
||||
|
||||
An NDJSON dataset file contains:
|
||||
|
||||
1. **Dataset record** (first line): Contains dataset metadata including task type, class names, and general information
|
||||
2. **Image records** (subsequent lines): Contains individual image data including dimensions, annotations, and file paths
|
||||
|
||||
!!! example "NDJSON Example"
|
||||
|
||||
=== "Dataset record (line 1)"
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "dataset",
|
||||
"task": "detect",
|
||||
"name": "Example",
|
||||
"description": "COCO NDJSON example dataset",
|
||||
"url": "https://app.ultralytics.com/user/datasets/example",
|
||||
"class_names": { "0": "person", "1": "bicycle", "2": "car" },
|
||||
"bytes": 426342,
|
||||
"version": 0,
|
||||
"created_at": "2024-01-01T00:00:00Z",
|
||||
"updated_at": "2025-01-01T00:00:00Z"
|
||||
}
|
||||
```
|
||||
|
||||
=== "Detect"
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "image",
|
||||
"file": "image1.jpg",
|
||||
"url": "https://www.url.com/path/to/image1.jpg",
|
||||
"width": 640,
|
||||
"height": 480,
|
||||
"split": "train",
|
||||
"annotations": {
|
||||
"boxes": [
|
||||
[0, 0.525, 0.376, 0.284, 0.418],
|
||||
[1, 0.735, 0.298, 0.193, 0.337]
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Format: `[class_id, x_center, y_center, width, height]`
|
||||
|
||||
=== "Segment"
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "image",
|
||||
"file": "image1.jpg",
|
||||
"url": "https://www.url.com/path/to/image1.jpg",
|
||||
"width": 640,
|
||||
"height": 480,
|
||||
"split": "train",
|
||||
"annotations": {
|
||||
"segments": [
|
||||
[0, 0.681, 0.485, 0.670, 0.487, 0.676, 0.487, 0.688, 0.515],
|
||||
[1, 0.422, 0.315, 0.438, 0.330, 0.445, 0.328, 0.450, 0.320]
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Format: `[class_id, x1, y1, x2, y2, x3, y3, ...]`
|
||||
|
||||
=== "Pose"
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "image",
|
||||
"file": "image1.jpg",
|
||||
"url": "https://www.url.com/path/to/image1.jpg",
|
||||
"width": 640,
|
||||
"height": 480,
|
||||
"split": "train",
|
||||
"annotations": {
|
||||
"pose": [
|
||||
[0, 0.523, 0.376, 0.283, 0.418, 0.374, 0.169, 2, 0.364, 0.178, 2],
|
||||
[0, 0.735, 0.298, 0.193, 0.337, 0.412, 0.225, 2, 0.408, 0.231, 2]
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Format: `[class_id, x_center, y_center, width, height, x1, y1, v1, x2, y2, v2, ...]`
|
||||
|
||||
Keypoints follow bbox as repeated `(x, y, v)` triplets where `v` is visibility: 0=not labeled, 1=labeled but occluded, 2=labeled and visible. The keypoint count is dataset-specific (e.g., COCO pose has 17 keypoints = 51 values after bbox).
|
||||
|
||||
=== "OBB"
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "image",
|
||||
"file": "image1.jpg",
|
||||
"url": "https://www.url.com/path/to/image1.jpg",
|
||||
"width": 640,
|
||||
"height": 480,
|
||||
"split": "train",
|
||||
"annotations": {
|
||||
"obb": [
|
||||
[0, 0.480, 0.352, 0.568, 0.356, 0.572, 0.400, 0.484, 0.396],
|
||||
[1, 0.711, 0.274, 0.759, 0.278, 0.755, 0.322, 0.707, 0.318]
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Format: `[class_id, x1, y1, x2, y2, x3, y3, x4, y4]`
|
||||
|
||||
The four corner points define the oriented bounding box in clockwise order starting from the top-left corner. All coordinates are normalized (0-1).
|
||||
|
||||
=== "Classify"
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "image",
|
||||
"file": "image1.jpg",
|
||||
"url": "https://www.url.com/path/to/image1.jpg",
|
||||
"width": 640,
|
||||
"height": 480,
|
||||
"split": "train",
|
||||
"annotations": {
|
||||
"classification": [0]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Format: `[class_id]`
|
||||
|
||||
#### Usage Example
|
||||
|
||||
To use an NDJSON dataset with YOLO26, simply specify the path to the `.ndjson` file:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train using NDJSON dataset
|
||||
results = model.train(data="path/to/dataset.ndjson", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training with NDJSON dataset
|
||||
yolo detect train data=path/to/dataset.ndjson model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
#### Advantages of NDJSON format
|
||||
|
||||
- **Single file**: All dataset information contained in one file
|
||||
- **Streaming**: Can process large datasets line-by-line without loading everything into memory
|
||||
- **Cloud integration**: Supports remote image URLs for cloud-based training
|
||||
- **Extensible**: Easy to add custom metadata fields
|
||||
- **Version control**: Single file format works well with git and version control systems
|
||||
|
||||
## Supported Datasets
|
||||
|
||||
Here is a list of the supported datasets and a brief description for each:
|
||||
|
||||
- [African-wildlife](african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebras.
|
||||
- [Argoverse](argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
|
||||
- [Brain-tumor](brain-tumor.md): A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics.
|
||||
- [COCO](coco.md): Common Objects in Context (COCO) is a large-scale [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and captioning dataset with 80 object categories.
|
||||
- [COCO8](coco8.md): A smaller subset of the first 4 images from COCO train and COCO val, suitable for quick tests.
|
||||
- [COCO8-Grayscale](coco8-grayscale.md): A grayscale version of COCO8 created by converting RGB to grayscale, useful for single-channel model evaluation.
|
||||
- [COCO8-Multispectral](coco8-multispectral.md): A 10-channel multispectral version of COCO8 created by interpolating RGB wavelengths, useful for spectral-aware model evaluation.
|
||||
- [COCO12-Formats](coco12-formats.md): A test dataset with 12 images covering all supported image formats (AVIF, BMP, DNG, HEIC, JP2, JPEG, JPG, MPO, PNG, TIF, TIFF, WebP) for validating image loading pipelines.
|
||||
- [COCO128](coco128.md): A smaller subset of the first 128 images from COCO train and COCO val, suitable for tests.
|
||||
- [Construction-PPE](construction-ppe.md): A dataset featuring construction site workers with labeled safety gear such as helmets, vests, gloves, boots, and goggles, including missing-equipment annotations like no_helmet, no_googles for real-world compliance monitoring.
|
||||
- [Global Wheat 2020](globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
|
||||
- [HomeObjects-3K](homeobjects-3k.md): A dataset of indoor household items including beds, chairs, TVs, and more—ideal for applications in smart home automation, robotics, augmented reality, and room layout analysis.
|
||||
- [KITTI](kitti.md): A dataset featuring real-world driving scenes with stereo, LiDAR, and GPS/IMU data, used here for **2D object detection** tasks such as identifying cars, pedestrians, and cyclists in urban, rural, and highway environments.
|
||||
- [LVIS](lvis.md): A large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
|
||||
- [Medical-pills](medical-pills.md): A dataset featuring images of medical-pills, annotated for applications such as pharmaceutical quality assurance, pill sorting, and regulatory compliance.
|
||||
- [Objects365](objects365.md): A high-quality, large-scale dataset for object detection with 365 object categories and over 600K annotated images.
|
||||
- [OpenImagesV7](open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
|
||||
- [Roboflow 100](roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
|
||||
- [Signature](signature.md): A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.
|
||||
- [SKU-110K](sku-110k.md): A dataset featuring dense object detection in retail environments with over 11K images and 1.7 million [bounding boxes](https://www.ultralytics.com/glossary/bounding-box).
|
||||
- [TT100K](tt100k.md): Explore the Tsinghua-Tencent 100K (TT100K) traffic sign dataset with 100,000 street view images and 30,000+ annotated traffic signs for robust detection and classification.
|
||||
- [VisDrone](visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
|
||||
- [VOC](voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
|
||||
- [xView](xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
|
||||
|
||||
### Adding your own dataset
|
||||
|
||||
If you have your own dataset and would like to use it for training detection models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.
|
||||
|
||||
## Port or Convert Label Formats
|
||||
|
||||
### COCO Dataset Format to YOLO Format
|
||||
|
||||
You can easily convert labels from the popular [COCO dataset](coco.md) format to the YOLO format using the following code snippet:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_coco
|
||||
|
||||
convert_coco(labels_dir="path/to/coco/annotations/")
|
||||
```
|
||||
|
||||
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The process transforms the JSON-based COCO annotations into the simpler text-based YOLO format, making it compatible with [Ultralytics YOLO models](../../models/yolo26.md).
|
||||
|
||||
Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Ultralytics YOLO dataset format and how to structure it?
|
||||
|
||||
The Ultralytics YOLO format is a structured configuration for defining datasets in your training projects. It involves setting paths to your training, validation, and testing images and corresponding labels. For example:
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8.yaml"
|
||||
```
|
||||
|
||||
Labels are saved in `*.txt` files with one file per image, formatted as `class x_center y_center width height` with normalized coordinates. For a detailed guide, see the [COCO8 dataset example](coco8.md).
|
||||
|
||||
### How do I convert a COCO dataset to the YOLO format?
|
||||
|
||||
You can convert a COCO dataset to the YOLO format using the [Ultralytics conversion tools](../../reference/data/converter.md). Here's a quick method:
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_coco
|
||||
|
||||
convert_coco(labels_dir="path/to/coco/annotations/")
|
||||
```
|
||||
|
||||
This code will convert your COCO annotations to YOLO format, enabling seamless integration with Ultralytics YOLO models. For additional details, visit the [Port or Convert Label Formats](#port-or-convert-label-formats) section.
|
||||
|
||||
### Which datasets are supported by Ultralytics YOLO for object detection?
|
||||
|
||||
Ultralytics YOLO supports a wide range of datasets, including:
|
||||
|
||||
- [Argoverse](argoverse.md)
|
||||
- [COCO](coco.md)
|
||||
- [LVIS](lvis.md)
|
||||
- [COCO8](coco8.md)
|
||||
- [Global Wheat 2020](globalwheat2020.md)
|
||||
- [Objects365](objects365.md)
|
||||
- [OpenImagesV7](open-images-v7.md)
|
||||
|
||||
Each dataset page provides detailed information on the structure and usage tailored for efficient YOLO26 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
|
||||
|
||||
### How do I start training a YOLO26 model using my dataset?
|
||||
|
||||
To start training a YOLO26 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo26n.pt") # Load a pretrained model
|
||||
results = model.train(data="path/to/your_dataset.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=path/to/your_dataset.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
Refer to the [Usage](#usage-example) section for more details on utilizing different modes, including CLI commands.
|
||||
|
||||
### Where can I find practical examples of using Ultralytics YOLO for object detection?
|
||||
|
||||
Ultralytics provides numerous examples and practical guides for using YOLO26 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLO26. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.
|
||||
127
algorithms/dms_yolo/code/docs/en/datasets/detect/kitti.md
Normal file
127
algorithms/dms_yolo/code/docs/en/datasets/detect/kitti.md
Normal file
@@ -0,0 +1,127 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics kitti dataset, a benchmark dataset for computer vision tasks such as 3D object detection, depth estimation, and autonomous driving perception.
|
||||
keywords: kitti, Ultralytics, dataset, object detection, 3D vision, YOLO26, training, validation, self-driving cars, computer vision
|
||||
---
|
||||
|
||||
# KITTI Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-kitti-detection-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open KITTI Dataset In Colab"></a>
|
||||
|
||||
The kitti dataset is one of the most influential benchmark datasets for autonomous driving and computer vision. Released by the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago, it contains stereo camera, LiDAR, and GPS/IMU data collected from real-world driving scenarios.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/NNeDlTbq9pA"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the KITTI Dataset 🚀
|
||||
</p>
|
||||
|
||||
It is widely used for evaluating algorithms in object detection, depth estimation, optical flow, and visual odometry. The dataset is fully compatible with Ultralytics YOLO26 for 2D object detection tasks and can be easily integrated into the Ultralytics platform for training and evaluation.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
!!! warning
|
||||
|
||||
Kitti original test set is excluded here since it does not contain ground-truth annotations.
|
||||
|
||||
In total, the dataset includes 7,481 images, each paired with detailed annotations for objects such as cars, pedestrians, cyclists, and other road elements. The dataset is divided into two main subsets:
|
||||
|
||||
- **Training set:** Contains 5,985 images with annotated labels used for model training.
|
||||
- **Validation set:** Includes 1,496 images with corresponding annotations used for performance evaluation and benchmarking.
|
||||
|
||||
## Applications
|
||||
|
||||
Kitti dataset enables advancements in autonomous driving and robotics, supporting tasks like:
|
||||
|
||||
- **Autonomous vehicle perception**: Training models to detect and track vehicles, pedestrians, and obstacles for safe navigation in self-driving systems.
|
||||
- **3D scene understanding**: Supporting depth estimation, stereo vision, and 3D object localization to help machines understand spatial environments.
|
||||
- **Optical flow and motion prediction**: Enabling motion analysis to predict the movement of objects and improve trajectory planning in dynamic environments.
|
||||
- **Computer vision benchmarking**: Serving as a standard benchmark for evaluating performance across multiple vision tasks, including object detection, and tracking.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
Ultralytics defines the kitti dataset configuration using a YAML file. This file specifies dataset paths, class labels, and metadata required for training. The configuration file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/kitti.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/kitti.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/kitti.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/kitti.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the kitti dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following commands. For more details, refer to the [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained YOLO26 model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train on kitti dataset
|
||||
results = model.train(data="kitti.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=kitti.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
You can also perform evaluation, [inference](../../modes/predict.md), and [export](../../modes/export.md) tasks directly from the command line or Python API using the same configuration file.
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The kitti dataset provides diverse driving scenarios. Each image includes bounding box annotations for 2D object detection tasks. The example showcase the dataset rich variety, enabling robust model generalization across diverse real-world conditions.
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/kitti-dataset-sample.avif" alt="KITTI dataset vehicle detection sample" width="800">
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the kitti dataset in your research, please cite the following paper:
|
||||
|
||||
!!! quote
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{Geiger2013IJRR,
|
||||
author = {Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun},
|
||||
title = {Vision meets Robotics: The KITTI Dataset},
|
||||
journal = {International Journal of Robotics Research (IJRR)},
|
||||
year = {2013}
|
||||
}
|
||||
```
|
||||
|
||||
We acknowledge the KITTI Vision Benchmark Suite for providing this comprehensive dataset that continues to shape progress in computer vision, robotics, and autonomous systems. Visit the [kitti website](https://www.cvlibs.net/datasets/kitti/) for more information.
|
||||
|
||||
## FAQs
|
||||
|
||||
### What is the kitti dataset used for?
|
||||
|
||||
The kitti dataset is primarily used for computer vision research in autonomous driving, supporting tasks like object detection, depth estimation, optical flow, and 3D localization.
|
||||
|
||||
### How many images are included in the kitti dataset?
|
||||
|
||||
The dataset includes 5,985 labeled training images and 1,496 validation images captured across urban, rural, and highway scenes. The original test set is excluded here since it does not contain ground-truth annotations.
|
||||
|
||||
### Which object classes are annotated in the dataset?
|
||||
|
||||
kitti includes annotations for objects such as cars, pedestrians, cyclists, trucks, trams, and miscellaneous road users.
|
||||
|
||||
### Can I train Ultralytics YOLO26 models using the kitti dataset?
|
||||
|
||||
Yes, kitti is fully compatible with Ultralytics YOLO26. You can [train](../../modes/train.md) and [validate](../../modes/val.md), models directly using the provided YAML configuration file.
|
||||
|
||||
### Where can I find the kitti dataset configuration file?
|
||||
|
||||
You can access the YAML file at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/kitti.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/kitti.yaml).
|
||||
159
algorithms/dms_yolo/code/docs/en/datasets/detect/lvis.md
Normal file
159
algorithms/dms_yolo/code/docs/en/datasets/detect/lvis.md
Normal file
@@ -0,0 +1,159 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover the LVIS dataset by Facebook AI Research, a benchmark for object detection and instance segmentation with a large, diverse vocabulary. Learn how to utilize it.
|
||||
keywords: LVIS dataset, object detection, instance segmentation, Facebook AI Research, YOLO, computer vision, model training, LVIS examples
|
||||
---
|
||||
|
||||
# LVIS Dataset
|
||||
|
||||
The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale, fine-grained vocabulary-level annotation dataset developed and released by Facebook AI Research (FAIR). It is primarily used as a research benchmark for object detection and [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) with a large vocabulary of categories, aiming to drive further advancements in computer vision field.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/cfTKj96TjSE"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> YOLO World training workflow with LVIS dataset
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<img width="640" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/lvis-dataset-example-images.avif" alt="LVIS large vocabulary instance segmentation dataset">
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- LVIS contains 160k images and 2M instance annotations for object detection, segmentation, and captioning tasks.
|
||||
- The dataset comprises 1203 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as umbrellas, handbags, and sports equipment.
|
||||
- Annotations include object bounding boxes, segmentation masks, and captions for each image.
|
||||
- LVIS provides standardized evaluation metrics like [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) for object detection, and mean Average [Recall](https://www.ultralytics.com/glossary/recall) (mAR) for segmentation tasks, making it suitable for comparing model performance.
|
||||
- LVIS uses exactly the same images as [COCO](./coco.md) dataset, but with different splits and different annotations.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The LVIS dataset is split into three subsets:
|
||||
|
||||
1. **Train**: This subset contains 100k images for training object detection, segmentation, and captioning models.
|
||||
2. **Val**: This subset has 20k images used for validation purposes during model training.
|
||||
3. **Minival**: This subset is exactly the same as COCO val2017 set which has 5k images used for validation purposes during model training.
|
||||
4. **Test**: This subset consists of 20k images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [LVIS evaluation server](https://eval.ai/web/challenges/challenge-page/675/overview) for performance evaluation.
|
||||
|
||||
## Applications
|
||||
|
||||
The LVIS dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [YOLO](../../models/yolo26.md), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), instance segmentation (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the LVIS dataset, the `lvis.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/lvis.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/lvis.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the LVIS dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=lvis.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The LVIS dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the LVIS dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the LVIS dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@inproceedings{gupta2019lvis,
|
||||
title={LVIS: A Dataset for Large Vocabulary Instance Segmentation},
|
||||
author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
|
||||
booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
|
||||
year={2019}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the LVIS Consortium for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the LVIS dataset and its creators, visit the [LVIS dataset website](https://www.lvisdataset.org/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the LVIS dataset, and how is it used in computer vision?
|
||||
|
||||
The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale dataset with fine-grained vocabulary-level annotations developed by Facebook AI Research (FAIR). It is primarily used for object detection and instance segmentation, featuring over 1203 object categories and 2 million instance annotations. Researchers and practitioners use it to train and benchmark models like Ultralytics YOLO for advanced computer vision tasks. The dataset's extensive size and diversity make it an essential resource for pushing the boundaries of model performance in detection and segmentation.
|
||||
|
||||
### How can I train a YOLO26n model using the LVIS dataset?
|
||||
|
||||
To train a YOLO26n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=lvis.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For detailed training configurations, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### How does the LVIS dataset differ from the COCO dataset?
|
||||
|
||||
The images in the LVIS dataset are the same as those in the [COCO dataset](./coco.md), but the two differ in terms of splitting and annotations. LVIS provides a larger and more detailed vocabulary with 1203 object categories compared to COCO's 80 categories. Additionally, LVIS focuses on annotation completeness and diversity, aiming to push the limits of [object detection](https://www.ultralytics.com/glossary/object-detection) and instance segmentation models by offering more nuanced and comprehensive data.
|
||||
|
||||
### Why should I use Ultralytics YOLO for training on the LVIS dataset?
|
||||
|
||||
Ultralytics YOLO models, including the latest YOLO26, are optimized for real-time object detection with state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
|
||||
|
||||
### Can I see some sample annotations from the LVIS dataset?
|
||||
|
||||
Yes, the LVIS dataset includes a variety of images with diverse object categories and complex scenes. Here is an example of a sample image along with its annotations:
|
||||
|
||||
|
||||
|
||||
This mosaiced image demonstrates a training batch composed of multiple dataset images combined into one. Mosaicing increases the variety of objects and scenes within each training batch, enhancing the model's ability to generalize across different contexts. For more details on the LVIS dataset, explore the [LVIS dataset documentation](#key-features).
|
||||
@@ -0,0 +1,153 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the medical-pills detection dataset with labeled images. Essential for training AI models for pharmaceutical identification and automation.
|
||||
keywords: medical-pills dataset, pill detection, pharmaceutical imaging, AI in healthcare, computer vision, object detection, medical automation, dataset for training
|
||||
---
|
||||
|
||||
# Medical Pills Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Medical Pills Dataset In Colab"></a>
|
||||
|
||||
The medical-pills detection dataset is a proof-of-concept (POC) dataset, carefully curated to demonstrate the potential of AI in pharmaceutical applications. It contains labeled images specifically designed to train [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) [models](https://docs.ultralytics.com/models/) for identifying medical-pills.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/8gePl_Zcs5c"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to train Ultralytics YOLO26 Model on Medical Pills Detection Dataset in <a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb">Google Colab</a>
|
||||
</p>
|
||||
|
||||
This dataset serves as a foundational resource for automating essential [tasks](https://docs.ultralytics.com/tasks/) such as quality control, packaging automation, and efficient sorting in pharmaceutical workflows. By integrating this dataset into projects, researchers and developers can explore innovative [solutions](https://docs.ultralytics.com/solutions/) that enhance [accuracy](https://www.ultralytics.com/glossary/accuracy), streamline operations, and ultimately contribute to improved healthcare outcomes.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The medical-pills dataset is divided into two subsets:
|
||||
|
||||
- **Training set**: Consisting of 92 images, each annotated with the class `pill`.
|
||||
- **Validation set**: Comprising 23 images with corresponding annotations.
|
||||
|
||||
## Applications
|
||||
|
||||
Using computer vision for medical-pills detection enables automation in the pharmaceutical industry, supporting tasks like:
|
||||
|
||||
- **Pharmaceutical Sorting**: Automating the sorting of pills based on size, shape, or color to enhance production efficiency.
|
||||
- **AI Research and Development**: Serving as a benchmark for developing and testing computer vision algorithms in pharmaceutical use cases.
|
||||
- **Digital Inventory Systems**: Powering smart inventory solutions by integrating automated pill recognition for real-time stock monitoring and replenishment planning.
|
||||
- **Quality Control**: Ensuring consistency in pill production by identifying defects, irregularities, or contamination.
|
||||
- **Counterfeit Detection**: Helping identify potentially counterfeit medications by analyzing visual characteristics against known standards.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML configuration file is provided to define the dataset's structure, including paths and classes. For the medical-pills dataset, the `medical-pills.yaml` file can be accessed at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/medical-pills.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/medical-pills.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/medical-pills.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/medical-pills.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the medical-pills dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For detailed arguments, refer to the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="medical-pills.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=medical-pills.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("path/to/best.pt") # load a fine-tuned model
|
||||
|
||||
# Inference using the model
|
||||
results = model.predict("https://ultralytics.com/assets/medical-pills-sample.jpg")
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start prediction with a fine-tuned *.pt model
|
||||
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/medical-pills-sample.jpg"
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The medical-pills dataset features labeled images showcasing the diversity of pills. Below is an example of a labeled image from the dataset:
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: Displayed is a training batch comprising mosaiced dataset images. Mosaicing enhances training diversity by consolidating multiple images into one, improving model generalization.
|
||||
|
||||
## Integration with Other Datasets
|
||||
|
||||
For more comprehensive pharmaceutical analysis, consider combining the medical-pills dataset with other related datasets like [package-seg](../segment/package-seg.md) for packaging identification or medical imaging datasets like [brain-tumor](brain-tumor.md) to develop end-to-end healthcare AI solutions.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
The dataset is available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
|
||||
If you use the Medical-pills dataset in your research or development work, please cite it using the mentioned details:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@dataset{Jocher_Ultralytics_Datasets_2024,
|
||||
author = {Jocher, Glenn and Rizwan, Muhammad},
|
||||
license = {AGPL-3.0},
|
||||
month = {Dec},
|
||||
title = {Ultralytics Datasets: Medical-pills Detection Dataset},
|
||||
url = {https://docs.ultralytics.com/datasets/detect/medical-pills/},
|
||||
version = {1.0.0},
|
||||
year = {2024}
|
||||
}
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the structure of the medical-pills dataset?
|
||||
|
||||
The dataset includes 92 images for training and 23 images for validation. Each image is annotated with the class `pill`, enabling effective training and evaluation of models for pharmaceutical applications.
|
||||
|
||||
### How can I train a YOLO26 model on the medical-pills dataset?
|
||||
|
||||
You can train a YOLO26 model for 100 epochs with an image size of 640px using the Python or CLI methods provided. Refer to the [Training Example](#usage) section for detailed instructions and check the [YOLO26 documentation](../../models/yolo26.md) for more information on model capabilities.
|
||||
|
||||
### What are the benefits of using the medical-pills dataset in AI projects?
|
||||
|
||||
The dataset enables automation in pill detection, contributing to counterfeit prevention, quality assurance, and pharmaceutical process optimization. It also serves as a valuable resource for developing AI solutions that can improve medication safety and supply chain efficiency.
|
||||
|
||||
### How do I perform inference on the medical-pills dataset?
|
||||
|
||||
Inference can be done using Python or CLI methods with a fine-tuned YOLO26 model. Refer to the [Inference Example](#usage) section for code snippets and the [Predict mode documentation](../../modes/predict.md) for additional options.
|
||||
|
||||
### Where can I find the YAML configuration file for the medical-pills dataset?
|
||||
|
||||
The YAML file is available at [medical-pills.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/medical-pills.yaml), containing dataset paths, classes, and additional configuration details essential for training models on this dataset.
|
||||
152
algorithms/dms_yolo/code/docs/en/datasets/detect/objects365.md
Normal file
152
algorithms/dms_yolo/code/docs/en/datasets/detect/objects365.md
Normal file
@@ -0,0 +1,152 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Objects365 Dataset with 2M images and 30M bounding boxes across 365 categories. Enhance your object detection models with diverse, high-quality data.
|
||||
keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLO26, high-resolution images, dataset configuration
|
||||
---
|
||||
|
||||
# Objects365 Dataset
|
||||
|
||||
The [Objects365](https://www.objects365.org/) dataset is a large-scale, high-quality dataset designed to foster object detection research with a focus on diverse objects in the wild. Created by a team of [Megvii](https://en.megvii.com/) researchers, the dataset offers a wide range of high-resolution images with a comprehensive set of annotated bounding boxes covering 365 object categories.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/J-RH22rwx1A"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the Objects365 Dataset with Ultralytics | 2M Annotations 🚀
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- Objects365 contains 365 object categories, with 2 million images and over 30 million bounding boxes.
|
||||
- The dataset includes diverse objects in various scenarios, providing a rich and challenging benchmark for object detection tasks.
|
||||
- Annotations include bounding boxes for objects, making it suitable for training and evaluating object detection models.
|
||||
- Objects365 pretrained models significantly outperform ImageNet pretrained models, leading to better generalization on various tasks.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The Objects365 dataset is organized into a single set of images with corresponding annotations:
|
||||
|
||||
- **Images**: The dataset includes 2 million high-resolution images, each containing a variety of objects across 365 categories.
|
||||
- **Annotations**: The images are annotated with over 30 million bounding boxes, providing comprehensive ground truth information for [object detection](https://docs.ultralytics.com/tasks/detect/) tasks.
|
||||
|
||||
## Applications
|
||||
|
||||
The Objects365 dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection tasks. The dataset's diverse set of object categories and high-quality annotations make it a valuable resource for researchers and practitioners in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Objects365 Dataset, the `Objects365.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/Objects365.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/Objects365.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the Objects365 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Objects365.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The Objects365 dataset contains a diverse set of high-resolution images with objects from 365 categories, providing rich context for [object detection](https://www.ultralytics.com/glossary/object-detection) tasks. Here are some examples of the images in the dataset:
|
||||
|
||||
|
||||
|
||||
- **Objects365**: This image demonstrates an example of object detection, where objects are annotated with bounding boxes. The dataset provides a wide range of images to facilitate the development of models for this task.
|
||||
|
||||
The example showcases the variety and complexity of the data in the Objects365 dataset and highlights the importance of accurate object detection for computer vision applications.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the Objects365 dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@inproceedings{shao2019objects365,
|
||||
title={Objects365: A Large-scale, High-quality Dataset for Object Detection},
|
||||
author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Li, Jing and Zhang, Xiangyu and Sun, Jian},
|
||||
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
|
||||
pages={8425--8434},
|
||||
year={2019}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Objects365 dataset used for?
|
||||
|
||||
The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision. It provides a large-scale, high-quality dataset with 2 million annotated images and 30 million bounding boxes across 365 categories. Leveraging such a diverse dataset helps improve the performance and generalization of object detection models, making it invaluable for research and development in the field.
|
||||
|
||||
### How can I train a YOLO26 model on the Objects365 dataset?
|
||||
|
||||
To train a YOLO26n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=Objects365.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.
|
||||
|
||||
### Why should I use the Objects365 dataset for my object detection projects?
|
||||
|
||||
The Objects365 dataset offers several advantages for object detection tasks:
|
||||
|
||||
1. **Diversity**: It includes 2 million images with objects in diverse scenarios, covering 365 categories.
|
||||
2. **High-quality Annotations**: Over 30 million bounding boxes provide comprehensive ground truth data.
|
||||
3. **Performance**: Models pretrained on Objects365 significantly outperform those trained on datasets like [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), leading to better generalization.
|
||||
|
||||
### Where can I find the YAML configuration file for the Objects365 dataset?
|
||||
|
||||
The YAML configuration file for the Objects365 dataset is available at [Objects365.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml). This file contains essential information such as dataset paths and class labels, crucial for setting up your training environment.
|
||||
|
||||
### How does the dataset structure of Objects365 enhance object detection modeling?
|
||||
|
||||
The [Objects365 dataset](https://www.objects365.org/) is organized with 2 million high-resolution images and comprehensive annotations of over 30 million bounding boxes. This structure ensures a robust dataset for training [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection, offering a wide variety of objects and scenarios. Such diversity and volume help in developing models that are more accurate and capable of generalizing well to real-world applications. For more details on the dataset structure, refer to the [Dataset YAML](#dataset-yaml) section.
|
||||
@@ -0,0 +1,237 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLO26 pretrained models for computer vision tasks.
|
||||
keywords: Open Images V7, Google dataset, computer vision, YOLO26 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
|
||||
---
|
||||
|
||||
# Open Images V7 Dataset
|
||||
|
||||
[Open Images V7](https://storage.googleapis.com/openimages/web/index.html) is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/u3pLlgzUeV8"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> <a href="https://www.ultralytics.com/glossary/object-detection">Object Detection</a> using OpenImagesV7 Pretrained Model
|
||||
</p>
|
||||
|
||||
## Open Images V7 Pretrained Models
|
||||
|
||||
| Model | size<br><sup>(pixels)</sup> | mAP<sup>val<br>50-95</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
|
||||
| ----------------------------------------------------------------------------------------- | --------------------------- | -------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |
|
||||
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
|
||||
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
|
||||
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
|
||||
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
|
||||
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
|
||||
|
||||
You can use these pretrained models for inference or fine-tuning as follows.
|
||||
|
||||
!!! example "Pretrained Model Usage Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load an Open Images Dataset V7 pretrained YOLOv8n model
|
||||
model = YOLO("yolov8n-oiv7.pt")
|
||||
|
||||
# Run prediction
|
||||
results = model.predict(source="image.jpg")
|
||||
|
||||
# Start training from the pretrained checkpoint
|
||||
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Predict using an Open Images Dataset V7 pretrained model
|
||||
yolo detect predict source=image.jpg model=yolov8n-oiv7.pt
|
||||
|
||||
# Start training from an Open Images Dataset V7 pretrained checkpoint
|
||||
yolo detect train data=coco8.yaml model=yolov8n-oiv7.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Key Features
|
||||
|
||||
- Encompasses ~9M images annotated in various ways to suit multiple computer vision tasks.
|
||||
- Houses a staggering 16M bounding boxes across 600 object classes in 1.9M images. These boxes are primarily hand-drawn by experts ensuring high [precision](https://www.ultralytics.com/glossary/precision).
|
||||
- Visual relationship annotations totaling 3.3M are available, detailing 1,466 unique relationship triplets, object properties, and human activities.
|
||||
- V5 introduced segmentation masks for 2.8M objects across 350 classes.
|
||||
- V6 introduced 675k localized narratives that amalgamate voice, text, and mouse traces highlighting described objects.
|
||||
- V7 introduced 66.4M point-level labels on 1.4M images, spanning 5,827 classes.
|
||||
- Encompasses 61.4M image-level labels across a diverse set of 20,638 classes.
|
||||
- Provides a unified platform for [image classification](https://www.ultralytics.com/glossary/image-classification), object detection, relationship detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and multimodal image descriptions.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
Open Images V7 is structured in multiple components catering to varied computer vision challenges:
|
||||
|
||||
- **Images**: About 9 million images, often showcasing intricate scenes with an average of 8.3 objects per image.
|
||||
- **Bounding Boxes**: Over 16 million boxes that demarcate objects across 600 categories.
|
||||
- **Segmentation Masks**: These detail the exact boundary of 2.8M objects across 350 classes.
|
||||
- **Visual Relationships**: 3.3M annotations indicating object relationships, properties, and actions.
|
||||
- **Localized Narratives**: 675k descriptions combining voice, text, and mouse traces.
|
||||
- **Point-Level Labels**: 66.4M labels across 1.4M images, suitable for zero/few-shot [semantic segmentation](https://www.ultralytics.com/glossary/semantic-segmentation).
|
||||
|
||||
## Applications
|
||||
|
||||
Open Images V7 is a cornerstone for training and evaluating state-of-the-art models in various computer vision tasks. The dataset's broad scope and high-quality annotations make it indispensable for researchers and developers specializing in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
|
||||
Some key applications include:
|
||||
|
||||
- **Advanced Object Detection**: Train models to identify and locate multiple objects in complex scenes with high accuracy.
|
||||
- **Semantic Understanding**: Develop systems that comprehend visual relationships between objects.
|
||||
- **Image Segmentation**: Create precise pixel-level masks for objects, enabling detailed scene analysis.
|
||||
- **Multi-modal Learning**: Combine visual data with text descriptions for richer AI understanding.
|
||||
- **Zero-shot Learning**: Leverage the extensive class coverage to identify objects not seen during training.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
Ultralytics maintains an `open-images-v7.yaml` file that specifies the dataset paths, class names, and other configuration details required for training.
|
||||
|
||||
!!! example "OpenImagesV7.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/open-images-v7.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the Open Images V7 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! warning
|
||||
|
||||
The complete Open Images V7 dataset comprises 1,743,042 training images and 41,620 validation images, requiring approximately **561 GB of storage space** upon download.
|
||||
|
||||
Executing the commands provided below will trigger an automatic download of the full dataset if it's not already present locally. Before running the below example it's crucial to:
|
||||
|
||||
- Verify that your device has enough storage capacity.
|
||||
- Ensure a robust and speedy internet connection.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a COCO-pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on the Open Images V7 dataset
|
||||
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a COCO-pretrained YOLO26n model on the Open Images V7 dataset
|
||||
yolo detect train data=open-images-v7.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
Illustrations of the dataset help provide insights into its richness:
|
||||
|
||||
|
||||
|
||||
- **Open Images V7**: This image exemplifies the depth and detail of annotations available, including bounding boxes, relationships, and segmentation masks.
|
||||
|
||||
Researchers can gain invaluable insights into the array of computer vision challenges that the dataset addresses, from basic object detection to intricate relationship identification. The [diversity of annotations](https://docs.ultralytics.com/datasets/explorer/) makes Open Images V7 particularly valuable for developing models that can understand complex visual scenes.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{OpenImages,
|
||||
author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
|
||||
title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
|
||||
year = {2020},
|
||||
journal = {IJCV}
|
||||
}
|
||||
```
|
||||
|
||||
A heartfelt acknowledgment goes out to the Google AI team for creating and maintaining the Open Images V7 dataset. For a deep dive into the dataset and its offerings, navigate to the [official Open Images V7 website](https://storage.googleapis.com/openimages/web/index.html).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Open Images V7 dataset?
|
||||
|
||||
Open Images V7 is an extensive and versatile dataset created by Google, designed to advance research in computer vision. It includes image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives, making it ideal for various computer vision tasks such as object detection, segmentation, and relationship detection.
|
||||
|
||||
### How do I train a YOLO26 model on the Open Images V7 dataset?
|
||||
|
||||
To train a YOLO26 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a COCO-pretrained YOLO26n model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model on the Open Images V7 dataset
|
||||
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a COCO-pretrained YOLO26n model on the Open Images V7 dataset
|
||||
yolo detect train data=open-images-v7.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details on arguments and settings, refer to the [Training](../../modes/train.md) page.
|
||||
|
||||
### What are some key features of the Open Images V7 dataset?
|
||||
|
||||
The Open Images V7 dataset includes approximately 9 million images with various annotations:
|
||||
|
||||
- **Bounding Boxes**: 16 million bounding boxes across 600 object classes.
|
||||
- **Segmentation Masks**: Masks for 2.8 million objects across 350 classes.
|
||||
- **Visual Relationships**: 3.3 million annotations indicating relationships, properties, and actions.
|
||||
- **Localized Narratives**: 675,000 descriptions combining voice, text, and mouse traces.
|
||||
- **Point-Level Labels**: 66.4 million labels across 1.4 million images.
|
||||
- **Image-Level Labels**: 61.4 million labels across 20,638 classes.
|
||||
|
||||
### What pretrained models are available for the Open Images V7 dataset?
|
||||
|
||||
Ultralytics provides several YOLOv8 pretrained models for the Open Images V7 dataset, each with different sizes and performance metrics:
|
||||
|
||||
| Model | size<br><sup>(pixels)</sup> | mAP<sup>val<br>50-95</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
|
||||
| ----------------------------------------------------------------------------------------- | --------------------------- | -------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |
|
||||
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
|
||||
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
|
||||
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
|
||||
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
|
||||
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
|
||||
|
||||
### What applications can the Open Images V7 dataset be used for?
|
||||
|
||||
The Open Images V7 dataset supports a variety of computer vision tasks including:
|
||||
|
||||
- **[Image Classification](https://www.ultralytics.com/glossary/image-classification)**
|
||||
- **Object Detection**
|
||||
- **Instance Segmentation**
|
||||
- **Visual Relationship Detection**
|
||||
- **Multimodal Image Descriptions**
|
||||
|
||||
Its comprehensive annotations and broad scope make it suitable for training and evaluating advanced [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models, as highlighted in practical use cases detailed in our [applications](#applications) section.
|
||||
182
algorithms/dms_yolo/code/docs/en/datasets/detect/roboflow-100.md
Normal file
182
algorithms/dms_yolo/code/docs/en/datasets/detect/roboflow-100.md
Normal file
@@ -0,0 +1,182 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Roboflow 100 dataset featuring 100 diverse datasets designed to test object detection models across various domains, from healthcare to video games.
|
||||
keywords: Roboflow 100, Ultralytics, object detection, dataset, benchmarking, machine learning, computer vision, diverse datasets, model evaluation
|
||||
---
|
||||
|
||||
# Roboflow 100 Dataset
|
||||
|
||||
Roboflow 100, sponsored by [Intel](https://www.intel.com/), is a groundbreaking [object detection](../../tasks/detect.md) benchmark dataset. It includes 100 diverse datasets sampled from over 90,000 public datasets available on Roboflow Universe. This benchmark is specifically designed to test the adaptability of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, like [Ultralytics YOLO models](../../models/yolo26.md), to various domains, including healthcare, aerial imagery, and video games.
|
||||
|
||||
!!! question "Licensing"
|
||||
|
||||
Ultralytics offers two licensing options to accommodate different use cases:
|
||||
|
||||
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details and visit our [AGPL-3.0 License page](https://www.ultralytics.com/legal/agpl-3-0-software-license).
|
||||
- **Enterprise License**: Designed for commercial use, this license allows for the seamless integration of Ultralytics software and AI models into commercial products and services. If your scenario involves commercial applications, please reach out via [Ultralytics Licensing](https://www.ultralytics.com/license).
|
||||
|
||||
<p align="center">
|
||||
<img width="640" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/roboflow-100-overview.avif" alt="Roboflow 100 diverse object detection benchmark">
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- **Diverse Domains**: Includes 100 datasets across seven distinct domains: Aerial, Video games, Microscopic, Underwater, Documents, Electromagnetic, and Real World.
|
||||
- **Scale**: The benchmark comprises 224,714 images across 805 classes, representing over 11,170 hours of [data labeling](https://www.ultralytics.com/glossary/data-labeling) effort.
|
||||
- **Standardization**: All images are [preprocessed](https://www.ultralytics.com/glossary/data-preprocessing) and resized to 640x640 pixels for consistent evaluation.
|
||||
- **Clean Evaluation**: Focuses on eliminating class ambiguity and filters out underrepresented classes to ensure cleaner [model evaluation](../../guides/model-evaluation-insights.md).
|
||||
- **Annotations**: Includes [bounding boxes](https://www.ultralytics.com/glossary/bounding-box) for objects, suitable for [training](../../modes/train.md) and evaluating object detection models using metrics like [mAP](https://www.ultralytics.com/glossary/mean-average-precision-map).
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The Roboflow 100 dataset is organized into seven categories, each containing a unique collection of datasets, images, and classes:
|
||||
|
||||
- **Aerial**: 7 datasets, 9,683 images, 24 classes.
|
||||
- **Video Games**: 7 datasets, 11,579 images, 88 classes.
|
||||
- **Microscopic**: 11 datasets, 13,378 images, 28 classes.
|
||||
- **Underwater**: 5 datasets, 18,003 images, 39 classes.
|
||||
- **Documents**: 8 datasets, 24,813 images, 90 classes.
|
||||
- **Electromagnetic**: 12 datasets, 36,381 images, 41 classes.
|
||||
- **Real World**: 50 datasets, 110,615 images, 495 classes.
|
||||
|
||||
This structure provides a diverse and extensive testing ground for [object detection](https://www.ultralytics.com/glossary/object-detection) models, reflecting a wide array of real-world application scenarios found in various [Ultralytics Solutions](https://www.ultralytics.com/solutions).
|
||||
|
||||
## Benchmarking
|
||||
|
||||
Dataset [benchmarking](../../modes/benchmark.md) involves evaluating the performance of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models on specific datasets using standardized metrics. Common metrics include [accuracy](https://www.ultralytics.com/glossary/accuracy), mean Average Precision (mAP), and [F1-score](https://www.ultralytics.com/glossary/f1-score). You can learn more about these in our [YOLO Performance Metrics guide](../../guides/yolo-performance-metrics.md).
|
||||
|
||||
!!! tip "Benchmarking Results"
|
||||
|
||||
Benchmarking results using the provided script will be stored in the `ultralytics-benchmarks/` directory, specifically in `evaluation.txt`.
|
||||
|
||||
!!! example "Benchmarking Example"
|
||||
|
||||
The following script demonstrates how to programmatically benchmark an Ultralytics YOLO model (e.g., YOLO26n) on all 100 datasets within the Roboflow 100 benchmark using the `RF100Benchmark` class.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import os
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.utils.benchmarks import RF100Benchmark
|
||||
|
||||
# Initialize RF100Benchmark and set API key
|
||||
benchmark = RF100Benchmark()
|
||||
benchmark.set_key(api_key="YOUR_ROBOFLOW_API_KEY")
|
||||
|
||||
# Parse dataset and define file paths
|
||||
names, cfg_yamls = benchmark.parse_dataset()
|
||||
val_log_file = Path("ultralytics-benchmarks") / "validation.txt"
|
||||
eval_log_file = Path("ultralytics-benchmarks") / "evaluation.txt"
|
||||
|
||||
# Run benchmarks on each dataset in RF100
|
||||
for ind, path in enumerate(cfg_yamls):
|
||||
path = Path(path)
|
||||
if path.exists():
|
||||
# Fix YAML file and run training
|
||||
benchmark.fix_yaml(str(path))
|
||||
os.system(f"yolo detect train data={path} model=yolo26s.pt epochs=1 batch=16")
|
||||
|
||||
# Run validation and evaluate
|
||||
os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")
|
||||
benchmark.evaluate(str(path), str(val_log_file), str(eval_log_file), ind)
|
||||
|
||||
# Remove the 'runs' directory
|
||||
runs_dir = Path.cwd() / "runs"
|
||||
shutil.rmtree(runs_dir)
|
||||
else:
|
||||
print("YAML file path does not exist")
|
||||
continue
|
||||
|
||||
print("RF100 Benchmarking completed!")
|
||||
```
|
||||
|
||||
## Applications
|
||||
|
||||
Roboflow 100 is invaluable for various applications related to [computer vision](https://www.ultralytics.com/blog/everything-you-need-to-know-about-computer-vision-in-2025) and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl). Researchers and engineers can leverage this benchmark to:
|
||||
|
||||
- Evaluate the performance of object detection models in a multi-domain context.
|
||||
- Test the adaptability and [robustness](<https://en.wikipedia.org/wiki/Robustness_(computer_science)>) of models to real-world scenarios beyond common [benchmark datasets](https://www.ultralytics.com/glossary/benchmark-dataset) like [COCO](https://cocodataset.org/#home) or [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).
|
||||
- Benchmark the capabilities of object detection models across diverse datasets, including specialized areas like healthcare, aerial imagery, and video games.
|
||||
- Compare model performance across different [neural network](https://www.ultralytics.com/glossary/neural-network-nn) architectures and [optimization](https://www.ultralytics.com/glossary/optimization-algorithm) techniques.
|
||||
- Identify domain-specific challenges that may require specialized [model training tips](../../guides/model-training-tips.md) or [fine-tuning](https://www.ultralytics.com/glossary/fine-tuning) approaches like [transfer learning](https://www.ultralytics.com/glossary/transfer-learning).
|
||||
|
||||
For more ideas and inspiration on real-world applications, explore [our guides on practical projects](../../guides/index.md) or check out [Ultralytics Platform](https://platform.ultralytics.com) for streamlined [model training](../../modes/train.md) and [deployment](../../guides/model-deployment-options.md).
|
||||
|
||||
## Usage
|
||||
|
||||
The Roboflow 100 dataset, including metadata and download links, is available on the official [Roboflow 100 GitHub repository](https://github.com/roboflow/roboflow-100-benchmark). You can access and utilize the dataset directly from there for your benchmarking needs. The Ultralytics `RF100Benchmark` utility simplifies the process of downloading and preparing these datasets for use with Ultralytics models.
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
Roboflow 100 consists of datasets with diverse images captured from various angles and domains. Below are examples of annotated images included in the RF100 benchmark, showcasing the variety of objects and scenes. Techniques like [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) can further enhance the diversity during training.
|
||||
|
||||
<p align="center">
|
||||
<img width="640" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/sample-data-annotations.avif" alt="Roboflow 100 sample images with annotations">
|
||||
</p>
|
||||
|
||||
The diversity seen in the Roboflow 100 benchmark represents a significant advancement from traditional benchmarks, which often focus on optimizing a single metric within a limited domain. This comprehensive approach aids in developing more robust and versatile [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models capable of performing well across a multitude of different scenarios.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the Roboflow 100 dataset in your research or development work, please cite the original paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{rf100benchmark,
|
||||
Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
|
||||
Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
|
||||
Year = {2022},
|
||||
Eprint = {arXiv:2211.13523},
|
||||
url = {https://arxiv.org/abs/2211.13523}
|
||||
}
|
||||
```
|
||||
|
||||
We extend our gratitude to the Roboflow team and all contributors for their significant efforts in creating and maintaining the Roboflow 100 dataset as a valuable resource for the computer vision community.
|
||||
|
||||
If you are interested in exploring more datasets to enhance your object detection and machine learning projects, feel free to visit [our comprehensive dataset collection](../index.md), which includes a variety of other [detection datasets](../detect/index.md).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Roboflow 100 dataset, and why is it significant for object detection?
|
||||
|
||||
The **Roboflow 100** dataset is a benchmark for [object detection](../../tasks/detect.md) models. It comprises 100 diverse datasets sourced from Roboflow Universe, covering domains like healthcare, aerial imagery, and video games. Its significance lies in providing a standardized way to test model adaptability and robustness across a wide range of real-world scenarios, moving beyond traditional, often domain-limited, benchmarks.
|
||||
|
||||
### Which domains are covered by the Roboflow 100 dataset?
|
||||
|
||||
The **Roboflow 100** dataset spans seven diverse domains, offering unique challenges for [object detection](https://www.ultralytics.com/glossary/object-detection) models:
|
||||
|
||||
1. **Aerial**: 7 datasets (e.g., satellite imagery, drone views).
|
||||
2. **Video Games**: 7 datasets (e.g., objects from various game environments).
|
||||
3. **Microscopic**: 11 datasets (e.g., cells, particles).
|
||||
4. **Underwater**: 5 datasets (e.g., marine life, submerged objects).
|
||||
5. **Documents**: 8 datasets (e.g., text regions, form elements).
|
||||
6. **Electromagnetic**: 12 datasets (e.g., radar signatures, spectral data visualizations).
|
||||
7. **Real World**: 50 datasets (a broad category including everyday objects, scenes, retail, etc.).
|
||||
|
||||
This variety makes RF100 an excellent resource for assessing the [generalizability](<https://en.wikipedia.org/wiki/Generalization_(learning)>) of computer vision models.
|
||||
|
||||
### What should I include when citing the Roboflow 100 dataset in my research?
|
||||
|
||||
When using the Roboflow 100 dataset, please cite the original paper to give credit to the creators. Here is the recommended BibTeX citation:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{rf100benchmark,
|
||||
Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
|
||||
Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
|
||||
Year = {2022},
|
||||
Eprint = {arXiv:2211.13523},
|
||||
url = {https://arxiv.org/abs/2211.13523}
|
||||
}
|
||||
```
|
||||
|
||||
For further exploration, consider visiting our [comprehensive dataset collection](../index.md) or browsing other [detection datasets](../detect/index.md) compatible with Ultralytics models.
|
||||
179
algorithms/dms_yolo/code/docs/en/datasets/detect/signature.md
Normal file
179
algorithms/dms_yolo/code/docs/en/datasets/detect/signature.md
Normal file
@@ -0,0 +1,179 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover the Signature Detection Dataset for training models to identify and verify human signatures in various documents. Perfect for document verification and fraud prevention.
|
||||
keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLO26, Ultralytics, annotated signatures, training dataset
|
||||
---
|
||||
|
||||
# Signature Detection Dataset
|
||||
|
||||
This dataset focuses on detecting human written signatures within documents. It includes a variety of document types with annotated signatures, providing valuable insights for applications in document verification and fraud detection. Essential for training [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) algorithms, this dataset aids in identifying signatures in various document formats, supporting research and practical applications in document analysis.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The signature detection dataset is split into two subsets:
|
||||
|
||||
- **Training set**: Contains 143 images, each with corresponding annotations.
|
||||
- **Validation set**: Includes 35 images, each with paired annotations.
|
||||
|
||||
## Applications
|
||||
|
||||
This dataset can be applied in various computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection), [object tracking](https://docs.ultralytics.com/modes/track/), and document analysis. Specifically, it can be used to train and evaluate models for identifying signatures in documents, which has significant applications in:
|
||||
|
||||
- **Document Verification**: Automating the verification process for legal and financial documents
|
||||
- **Fraud Detection**: Identifying potentially forged or unauthorized signatures
|
||||
- **Digital Document Processing**: Streamlining workflows in administrative and legal sectors
|
||||
- **Banking and Finance**: Enhancing security in check processing and loan document verification
|
||||
- **Archival Research**: Supporting historical document analysis and cataloging
|
||||
|
||||
Additionally, it serves as a valuable resource for educational purposes, enabling students and researchers to study signature characteristics across different document types.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file defines the dataset configuration, including paths and classes information. For the signature detection dataset, the `signature.yaml` file is located at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/signature.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/signature.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the signature detection dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="signature.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=signature.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("path/to/best.pt") # load a signature-detection fine-tuned model
|
||||
|
||||
# Inference using the model
|
||||
results = model.predict("https://ultralytics.com/assets/signature-s.mp4", conf=0.75)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start prediction with a finetuned *.pt model
|
||||
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/signature-s.mp4" conf=0.75
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The signature detection dataset comprises a wide variety of images showcasing different document types and annotated signatures. Below are examples of images from the dataset, each accompanied by its corresponding annotations.
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different signature sizes, aspect ratios, and contexts.
|
||||
|
||||
This example illustrates the variety and complexity of images in the signature Detection Dataset, emphasizing the benefits of including mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Signature Detection Dataset, and how can it be used?
|
||||
|
||||
The Signature Detection Dataset is a collection of annotated images aimed at detecting human signatures within various document types. It can be applied in computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking, primarily for document verification, fraud detection, and archival research. This dataset helps train models to recognize signatures in different contexts, making it valuable for both research and practical applications in [smart document analysis](https://www.ultralytics.com/blog/using-ultralytics-yolo11-for-smart-document-analysis).
|
||||
|
||||
### How do I train a YOLO26n model on the Signature Detection Dataset?
|
||||
|
||||
To train a YOLO26n model on the Signature Detection Dataset, follow these steps:
|
||||
|
||||
1. Download the `signature.yaml` dataset configuration file from [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
|
||||
2. Use the following Python script or CLI command to start training:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="signature.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect train data=signature.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details, refer to the [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the main applications of the Signature Detection Dataset?
|
||||
|
||||
The Signature Detection Dataset can be used for:
|
||||
|
||||
1. **Document Verification**: Automatically verifying the presence and authenticity of human signatures in documents.
|
||||
2. **Fraud Detection**: Identifying forged or fraudulent signatures in legal and financial documents.
|
||||
3. **Archival Research**: Assisting historians and archivists in the digital analysis and cataloging of historical documents.
|
||||
4. **Education**: Supporting academic research and teaching in the fields of computer vision and [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml).
|
||||
5. **Financial Services**: Enhancing security in banking transactions and loan processing by verifying signature authenticity.
|
||||
|
||||
### How can I perform inference using a model trained on the Signature Detection Dataset?
|
||||
|
||||
To perform inference using a model trained on the Signature Detection Dataset, follow these steps:
|
||||
|
||||
1. Load your fine-tuned model.
|
||||
2. Use the below Python script or CLI command to perform inference:
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load the fine-tuned model
|
||||
model = YOLO("path/to/best.pt")
|
||||
|
||||
# Perform inference
|
||||
results = model.predict("https://ultralytics.com/assets/signature-s.mp4", conf=0.75)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/signature-s.mp4" conf=0.75
|
||||
```
|
||||
|
||||
### What is the structure of the Signature Detection Dataset, and where can I find more information?
|
||||
|
||||
The Signature Detection Dataset is divided into two subsets:
|
||||
|
||||
- **Training Set**: Contains 143 images with annotations.
|
||||
- **Validation Set**: Includes 35 images with annotations.
|
||||
|
||||
For detailed information, you can refer to the [Dataset Structure](#dataset-structure) section. Additionally, view the complete dataset configuration in the `signature.yaml` file located at [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
|
||||
189
algorithms/dms_yolo/code/docs/en/datasets/detect/sku-110k.md
Normal file
189
algorithms/dms_yolo/code/docs/en/datasets/detect/sku-110k.md
Normal file
@@ -0,0 +1,189 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the SKU-110k dataset of densely packed retail shelf images, perfect for training and evaluating deep learning models in object detection tasks.
|
||||
keywords: SKU-110k, dataset, object detection, retail shelf images, deep learning, computer vision, model training
|
||||
---
|
||||
|
||||
# SKU-110k Dataset
|
||||
|
||||
The [SKU-110k](https://github.com/eg4000/SKU110K_CVPR19) dataset is a collection of densely packed retail shelf images, designed to support research in [object detection](https://www.ultralytics.com/glossary/object-detection) tasks. Developed by Eran Goldman et al., the dataset contains over 110,000 unique store keeping unit (SKU) categories with densely packed objects, often looking similar or even identical, positioned in proximity.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/_gRqR-miFPE"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train YOLOv10 on SKU-110k Dataset using Ultralytics | Retail Dataset
|
||||
</p>
|
||||
|
||||
|
||||
|
||||
## Key Features
|
||||
|
||||
- SKU-110k contains images of store shelves from around the world, featuring densely packed objects that pose challenges for state-of-the-art object detectors.
|
||||
- The dataset includes over 110,000 unique SKU categories, providing a diverse range of object appearances.
|
||||
- Annotations include bounding boxes for objects and SKU category labels.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The SKU-110k dataset is organized into three main subsets:
|
||||
|
||||
1. **Training set**: This subset contains 8,219 images and annotations used for training object detection models.
|
||||
2. **Validation set**: This subset consists of 588 images and annotations used for model validation during training.
|
||||
3. **Test set**: This subset includes 2,936 images designed for the final evaluation of trained object detection models.
|
||||
|
||||
## Applications
|
||||
|
||||
The SKU-110k dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection tasks, especially in densely packed scenes such as retail shelf displays. Its applications include:
|
||||
|
||||
- Retail inventory management and automation
|
||||
- Product recognition in e-commerce platforms
|
||||
- Planogram compliance verification
|
||||
- Self-checkout systems in stores
|
||||
- Robotic picking and sorting in warehouses
|
||||
|
||||
The dataset's diverse set of SKU categories and densely packed object arrangements make it a valuable resource for researchers and practitioners in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the SKU-110K dataset, the `SKU-110K.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/SKU-110K.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/SKU-110K.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the SKU-110K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=SKU-110K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The SKU-110k dataset contains a diverse set of retail shelf images with densely packed objects, providing rich context for object detection tasks. Here are some examples of data from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Densely packed retail shelf image**: This image demonstrates an example of densely packed objects in a retail shelf setting. Objects are annotated with bounding boxes and SKU category labels.
|
||||
|
||||
The example showcases the variety and complexity of the data in the SKU-110k dataset and highlights the importance of high-quality data for object detection tasks. The dense arrangement of products presents unique challenges for detection algorithms, making this dataset particularly valuable for developing robust retail-focused computer vision solutions.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the SKU-110k dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@inproceedings{goldman2019dense,
|
||||
author = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
|
||||
title = {Precise Detection in Densely Packed Scenes},
|
||||
booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
|
||||
year = {2019}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the SKU-110k dataset and why is it important for object detection?
|
||||
|
||||
The SKU-110k dataset consists of densely packed retail shelf images designed to aid research in object detection tasks. Developed by Eran Goldman et al., it includes over 110,000 unique SKU categories. Its importance lies in its ability to challenge state-of-the-art object detectors with diverse object appearances and proximity, making it an invaluable resource for researchers and practitioners in computer vision. Learn more about the dataset's structure and applications in our [SKU-110k Dataset](#sku-110k-dataset) section.
|
||||
|
||||
### How do I train a YOLO26 model using the SKU-110k dataset?
|
||||
|
||||
Training a YOLO26 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=SKU-110K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the main subsets of the SKU-110k dataset?
|
||||
|
||||
The SKU-110k dataset is organized into three main subsets:
|
||||
|
||||
1. **Training set**: Contains 8,219 images and annotations used for training object detection models.
|
||||
2. **Validation set**: Consists of 588 images and annotations used for model validation during training.
|
||||
3. **Test set**: Includes 2,936 images designed for the final evaluation of trained object detection models.
|
||||
|
||||
Refer to the [Dataset Structure](#dataset-structure) section for more details.
|
||||
|
||||
### How do I configure the SKU-110k dataset for training?
|
||||
|
||||
The SKU-110k dataset configuration is defined in a YAML file, which includes details about the dataset's paths, classes, and other relevant information. The `SKU-110K.yaml` file is maintained at [SKU-110K.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml). For example, you can train a model using this configuration as shown in our [Usage](#usage) section.
|
||||
|
||||
### What are the key features of the SKU-110k dataset in the context of [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl)?
|
||||
|
||||
The SKU-110k dataset features images of store shelves from around the world, showcasing densely packed objects that pose significant challenges for object detectors:
|
||||
|
||||
- Over 110,000 unique SKU categories
|
||||
- Diverse object appearances
|
||||
- Annotations include bounding boxes and SKU category labels
|
||||
|
||||
These features make the SKU-110k dataset particularly valuable for training and evaluating deep learning models in object detection tasks. For more details, see the [Key Features](#key-features) section.
|
||||
|
||||
### How do I cite the SKU-110k dataset in my research?
|
||||
|
||||
If you use the SKU-110k dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@inproceedings{goldman2019dense,
|
||||
author = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
|
||||
title = {Precise Detection in Densely Packed Scenes},
|
||||
booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
|
||||
year = {2019}
|
||||
}
|
||||
```
|
||||
|
||||
More information about the dataset can be found in the [Citations and Acknowledgments](#citations-and-acknowledgments) section.
|
||||
234
algorithms/dms_yolo/code/docs/en/datasets/detect/tt100k.md
Normal file
234
algorithms/dms_yolo/code/docs/en/datasets/detect/tt100k.md
Normal file
@@ -0,0 +1,234 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Tsinghua-Tencent 100K (TT100K) traffic sign dataset with 100,000 street view images and 30,000+ annotated traffic signs for robust detection and classification.
|
||||
keywords: TT100K, Tsinghua-Tencent 100K, traffic sign detection, YOLO26, dataset, object detection, street view, traffic signs, Chinese traffic signs
|
||||
---
|
||||
|
||||
# TT100K Dataset
|
||||
|
||||
The [Tsinghua-Tencent 100K (TT100K)](https://cg.cs.tsinghua.edu.cn/traffic-sign/) is a large-scale traffic sign benchmark dataset created from 100,000 Tencent Street View panoramas. This dataset is specifically designed for traffic sign detection and classification in real-world conditions, providing researchers and developers with a comprehensive resource for building robust traffic sign recognition systems.
|
||||
|
||||
The dataset contains **100,000 images** with over **30,000 traffic sign instances** across **221 different categories**. These images capture large variations in illuminance, weather conditions, viewing angles, and distances, making it ideal for training models that need to perform reliably in diverse real-world scenarios.
|
||||
|
||||
This dataset is particularly valuable for:
|
||||
|
||||
- Autonomous driving systems
|
||||
- Advanced driver assistance systems (ADAS)
|
||||
- Traffic monitoring applications
|
||||
- Urban planning and traffic analysis
|
||||
- Computer vision research in real-world conditions
|
||||
|
||||
## Key Features
|
||||
|
||||
The TT100K dataset provides several key advantages:
|
||||
|
||||
- **Scale**: 100,000 high-resolution images (2048×2048 pixels)
|
||||
- **Diversity**: 221 traffic sign categories covering Chinese traffic signs
|
||||
- **Real-world conditions**: Large variations in weather, illumination, and viewing angles
|
||||
- **Rich annotations**: Each sign includes class label, bounding box, and pixel mask
|
||||
- **Comprehensive coverage**: Includes prohibitory, warning, mandatory, and informative signs
|
||||
- **Train/Test split**: Pre-defined splits for consistent evaluation
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The TT100K dataset is split into three subsets:
|
||||
|
||||
1. **Training Set**:
|
||||
The primary collection of traffic-scene images used to train models for detecting and classifying different types of traffic signs.
|
||||
2. **Validation Set**:
|
||||
A subset used during model development to monitor performance and tune hyperparameters.
|
||||
3. **Test Set**:
|
||||
A held-out collection of images used to evaluate the final model's ability to detect and classify traffic signs in real-world scenarios.
|
||||
|
||||
The TT100K dataset includes 221 traffic sign categories organized into several major groups:
|
||||
|
||||
**Speed Limit Signs (pl*, pm*)**
|
||||
|
||||
1. **pl\_**: Prohibitory speed limits (pl5, pl10, pl20, pl30, pl40, pl50, pl60, pl70, pl80, pl100, pl120)
|
||||
2. **pm\_**: Minimum speed limits (pm5, pm10, pm20, pm30, pm40, pm50, pm55)
|
||||
|
||||
**Prohibitory Signs (p*, pn*, pr\_)**
|
||||
|
||||
1. **p1-p28**: General prohibitory signs (no entry, no parking, no stopping, etc.)
|
||||
2. **pn/pne**: No entry and no parking signs
|
||||
3. **pr**: Various restriction signs (pr10, pr20, pr30, pr40, pr50, etc.)
|
||||
|
||||
**Warning Signs (w\_)**
|
||||
|
||||
1. **w1-w66**: Warning signs for various road hazards, conditions, and situations
|
||||
2. Includes pedestrian crossings, sharp turns, slippery roads, animals, construction, etc.
|
||||
|
||||
**Height/Width Limit Signs (ph*, pb*)**
|
||||
|
||||
1. **ph\_**: Height limit signs (ph2, ph2.5, ph3, ph3.5, ph4, ph4.5, ph5, etc.)
|
||||
2. **pb\_**: Width limit signs
|
||||
|
||||
**Informative Signs (i*, il*, io, ip)**
|
||||
|
||||
1. **i1-i15**: General informative signs
|
||||
2. **il\_**: Speed limit information (il60, il80, il100, il110)
|
||||
3. **io**: Other informative signs
|
||||
4. **ip**: Information plates
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the TT100K dataset, the `TT100K.yaml` file includes automatic download and conversion functionality.
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/TT100K.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/TT100K.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26 model on the TT100K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. The dataset will be automatically downloaded and converted to YOLO format on first use.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model - dataset will auto-download on first run
|
||||
results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
# Dataset will auto-download and convert on first run
|
||||
yolo detect train data=TT100K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are typical examples from the TT100K dataset:
|
||||
|
||||
1. **Urban environments**: Street scenes with multiple traffic signs at various distances
|
||||
2. **Highway scenes**: High-speed road signs including speed limits and direction indicators
|
||||
3. **Complex intersections**: Multiple signs in close proximity with varying orientations
|
||||
4. **Challenging conditions**: Signs under different lighting (day/night), weather (rain/fog), and viewing angles
|
||||
|
||||
The dataset includes:
|
||||
|
||||
1. **Close-up signs**: Large, clearly visible signs occupying significant image area
|
||||
2. **Distant signs**: Small signs requiring fine-grained detection capabilities
|
||||
3. **Partially occluded signs**: Signs partially blocked by vehicles, trees, or other objects
|
||||
4. **Multiple signs per image**: Images containing several different sign types
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the TT100K dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@InProceedings{Zhu_2016_CVPR,
|
||||
author = {Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},
|
||||
title = {Traffic-Sign Detection and Classification in the Wild},
|
||||
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
month = {June},
|
||||
year = {2016}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the Tsinghua University and Tencent collaboration for creating and maintaining this valuable resource for the computer vision and autonomous driving communities. For more information about the TT100K dataset, visit the [official dataset website](https://cg.cs.tsinghua.edu.cn/traffic-sign/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the TT100K dataset used for?
|
||||
|
||||
The Tsinghua-Tencent 100K (TT100K) dataset is specifically designed for traffic sign detection and classification in real-world conditions. It's primarily used for:
|
||||
|
||||
1. Training autonomous driving perception systems
|
||||
2. Developing Advanced Driver Assistance Systems (ADAS)
|
||||
3. Research in robust object detection under varying conditions
|
||||
4. Benchmarking traffic sign recognition algorithms
|
||||
5. Testing model performance on small objects in large images
|
||||
|
||||
With 100,000 diverse street view images and 221 traffic sign categories, it provides a comprehensive testbed for real-world traffic sign detection.
|
||||
|
||||
### How many traffic sign categories are in TT100K?
|
||||
|
||||
The TT100K dataset contains **221 different traffic sign categories**, including:
|
||||
|
||||
1. **Speed limits**: pl5 through pl120 (prohibitory limits) and pm5 through pm55 (minimum speeds)
|
||||
2. **Prohibitory signs**: 28+ general prohibition types (p1-p28) plus restrictions (pr\*, pn, pne)
|
||||
3. **Warning signs**: 60+ warning categories (w1-w66)
|
||||
4. **Height/width limits**: ph* and pb* series for physical restrictions
|
||||
5. **Informative signs**: i1-i15, il\*, io, ip for guidance and information
|
||||
|
||||
This comprehensive coverage includes most traffic signs found in Chinese road networks.
|
||||
|
||||
### How can I train a YOLO26n model using the TT100K dataset?
|
||||
|
||||
To train a YOLO26n model on the TT100K dataset for 100 epochs with an image size of 640, use the example below.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=TT100K.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For detailed training configurations, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### What makes TT100K challenging compared to other datasets?
|
||||
|
||||
TT100K presents several unique challenges:
|
||||
|
||||
1. **Scale variation**: Signs range from very small (distant highway signs) to large (close-up urban signs)
|
||||
2. **Real-world conditions**: Extreme variations in lighting, weather, and viewing angles
|
||||
3. **High resolution**: 2048×2048 pixel images require significant processing power
|
||||
4. **Class imbalance**: Some sign types are much more common than others
|
||||
5. **Dense scenes**: Multiple signs may appear in a single image
|
||||
6. **Partial occlusion**: Signs may be partially blocked by vehicles, vegetation, or structures
|
||||
|
||||
These challenges make TT100K a valuable benchmark for developing robust detection algorithms.
|
||||
|
||||
### How do I handle the large image sizes in TT100K?
|
||||
|
||||
The TT100K dataset uses 2048×2048 pixel images, which can be resource-intensive. Here are recommended strategies:
|
||||
|
||||
**For Training:**
|
||||
|
||||
```python
|
||||
# Option 1: Resize to standard YOLO size
|
||||
model.train(data="TT100K.yaml", imgsz=640, batch=16)
|
||||
|
||||
# Option 2: Use larger size for better small object detection
|
||||
model.train(data="TT100K.yaml", imgsz=1280, batch=4)
|
||||
|
||||
# Option 3: Multi-scale training
|
||||
model.train(data="TT100K.yaml", imgsz=640, scale=0.5) # trains at varying scales
|
||||
```
|
||||
|
||||
**Recommendations:**
|
||||
|
||||
- Start with `imgsz=640` for initial experiments
|
||||
- Use `imgsz=1280` if you have sufficient GPU memory (24GB+)
|
||||
- Consider tiling strategies for very small signs
|
||||
- Use gradient accumulation to simulate larger batch sizes
|
||||
179
algorithms/dms_yolo/code/docs/en/datasets/detect/visdrone.md
Normal file
179
algorithms/dms_yolo/code/docs/en/datasets/detect/visdrone.md
Normal file
@@ -0,0 +1,179 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the VisDrone Dataset, a large-scale benchmark for drone-based image and video analysis with over 2.6 million annotations for objects like pedestrians and vehicles.
|
||||
keywords: VisDrone, drone dataset, computer vision, object detection, object tracking, crowd counting, machine learning, deep learning
|
||||
---
|
||||
|
||||
# VisDrone Dataset
|
||||
|
||||
The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-scale benchmark created by the AISKYEYE team at the Lab of [Machine Learning](https://www.ultralytics.com/glossary/machine-learning-ml) and Data Mining, Tianjin University, China. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/9ymyH4H1fG4"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the VisDrone Dataset | Aerial Detection | Complete Tutorial 🚀
|
||||
</p>
|
||||
|
||||
VisDrone is composed of 288 video clips with 261,908 frames and 10,209 static images, captured by various drone-mounted cameras. The dataset covers a wide range of aspects, including location (14 different cities across China), environment (urban and rural), objects (pedestrians, vehicles, bicycles, etc.), and density (sparse and crowded scenes). The dataset was collected using various drone platforms under different scenarios and weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets such as pedestrians, cars, bicycles, and tricycles. Attributes like scene visibility, object class, and occlusion are also provided for better data utilization.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The VisDrone dataset is organized into five main subsets, each focusing on a specific task:
|
||||
|
||||
1. **Task 1**: Object detection in images
|
||||
2. **Task 2**: Object detection in videos
|
||||
3. **Task 3**: Single-object tracking
|
||||
4. **Task 4**: [Multi-object tracking](../index.md#multi-object-tracking)
|
||||
5. **Task 5**: Crowd counting
|
||||
|
||||
## Applications
|
||||
|
||||
The VisDrone dataset is widely used for training and evaluating deep learning models in drone-based [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks such as object detection, object tracking, and crowd counting. The dataset's diverse set of sensor data, object annotations, and attributes make it a valuable resource for researchers and practitioners in the field of drone-based computer vision.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Visdrone dataset, the `VisDrone.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/VisDrone.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/VisDrone.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the VisDrone dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VisDrone.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The VisDrone dataset contains a diverse set of images and videos captured by drone-mounted cameras. Here are some examples of data from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Task 1**: [Object detection](https://www.ultralytics.com/glossary/object-detection) in images - This image demonstrates an example of object detection in images, where objects are annotated with [bounding boxes](https://www.ultralytics.com/glossary/bounding-box). The dataset provides a wide variety of images taken from different locations, environments, and densities to facilitate the development of models for this task.
|
||||
|
||||
The example showcases the variety and complexity of the data in the VisDrone dataset and highlights the importance of high-quality sensor data for drone-based computer vision tasks.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the VisDrone dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@ARTICLE{9573394,
|
||||
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={Detection and Tracking Meet Drones Challenge},
|
||||
year={2021},
|
||||
volume={},
|
||||
number={},
|
||||
pages={1-1},
|
||||
doi={10.1109/TPAMI.2021.3119563}}
|
||||
```
|
||||
|
||||
We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and [Data Mining](https://www.ultralytics.com/glossary/data-mining), Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the VisDrone Dataset and what are its key features?
|
||||
|
||||
The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-scale benchmark created by the AISKYEYE team at Tianjin University, China. It is designed for various computer vision tasks related to drone-based image and video analysis. Key features include:
|
||||
|
||||
- **Composition**: 288 video clips with 261,908 frames and 10,209 static images.
|
||||
- **Annotations**: Over 2.6 million bounding boxes for objects like pedestrians, cars, bicycles, and tricycles.
|
||||
- **Diversity**: Collected across 14 cities, in urban and rural settings, under different weather and lighting conditions.
|
||||
- **Tasks**: Split into five main tasks—object detection in images and videos, single-object and multi-object tracking, and crowd counting.
|
||||
|
||||
### How can I use the VisDrone Dataset to train a YOLO26 model with Ultralytics?
|
||||
|
||||
To train a YOLO26 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VisDrone.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For additional configuration options, please refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the main subsets of the VisDrone dataset and their applications?
|
||||
|
||||
The VisDrone dataset is divided into five main subsets, each tailored for a specific computer vision task:
|
||||
|
||||
1. **Task 1**: Object detection in images.
|
||||
2. **Task 2**: Object detection in videos.
|
||||
3. **Task 3**: Single-object tracking.
|
||||
4. **Task 4**: Multi-object tracking.
|
||||
5. **Task 5**: Crowd counting.
|
||||
|
||||
These subsets are widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in drone-based applications such as surveillance, traffic monitoring, and public safety.
|
||||
|
||||
### Where can I find the configuration file for the VisDrone dataset in Ultralytics?
|
||||
|
||||
The configuration file for the VisDrone dataset, `VisDrone.yaml`, can be found in the Ultralytics repository at the following link:
|
||||
[VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
|
||||
|
||||
### How can I cite the VisDrone dataset if I use it in my research?
|
||||
|
||||
If you use the VisDrone dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@ARTICLE{9573394,
|
||||
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={Detection and Tracking Meet Drones Challenge},
|
||||
year={2021},
|
||||
volume={},
|
||||
number={},
|
||||
pages={1-1},
|
||||
doi={10.1109/TPAMI.2021.3119563}
|
||||
}
|
||||
```
|
||||
148
algorithms/dms_yolo/code/docs/en/datasets/detect/voc.md
Normal file
148
algorithms/dms_yolo/code/docs/en/datasets/detect/voc.md
Normal file
@@ -0,0 +1,148 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover the PASCAL VOC dataset, essential for object detection, segmentation, and classification. Learn key features, applications, and usage tips.
|
||||
keywords: PASCAL VOC, VOC dataset, object detection, segmentation, classification, YOLO, Faster R-CNN, Mask R-CNN, image annotations, computer vision
|
||||
---
|
||||
|
||||
# VOC Dataset
|
||||
|
||||
The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a well-known object detection, segmentation, and classification dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and classification tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/yrHzL8RyY6g"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the Pascal VOC Dataset | Object Detection 🚀
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- VOC dataset includes two main challenges: VOC2007 and VOC2012.
|
||||
- The dataset comprises 20 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as boats, sofas, and dining tables.
|
||||
- Annotations include object bounding boxes and class labels for object detection and classification tasks, and segmentation masks for the segmentation tasks.
|
||||
- VOC provides standardized evaluation metrics like [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) for object detection and classification, making it suitable for comparing model performance.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The VOC dataset is split into three subsets:
|
||||
|
||||
1. **Train**: This subset contains images for training object detection, segmentation, and classification models.
|
||||
2. **Validation**: This subset has images used for validation purposes during model training.
|
||||
3. **Test**: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results were historically submitted to the PASCAL VOC evaluation server for performance evaluation.
|
||||
|
||||
## Applications
|
||||
|
||||
The VOC dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object detection (such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/), [Faster R-CNN](https://arxiv.org/abs/1506.01497), and [SSD](https://arxiv.org/abs/1512.02325)), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) (such as [Mask R-CNN](https://arxiv.org/abs/1703.06870)), and [image classification](https://www.ultralytics.com/glossary/image-classification). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) researchers and practitioners.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the VOC dataset, the `VOC.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/VOC.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/VOC.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n model on the VOC dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VOC.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The VOC dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the VOC dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the VOC dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{everingham2010pascal,
|
||||
title={The PASCAL Visual Object Classes (VOC) Challenge},
|
||||
author={Mark Everingham and Luc Van Gool and Christopher K. I. Williams and John Winn and Andrew Zisserman},
|
||||
year={2010},
|
||||
eprint={0909.5206},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the PASCAL VOC dataset and why is it important for computer vision tasks?
|
||||
|
||||
The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a renowned benchmark for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in computer vision. It includes comprehensive annotations like bounding boxes, class labels, and segmentation masks across 20 different object categories. Researchers use it widely to evaluate the performance of models like Faster R-CNN, YOLO, and Mask R-CNN due to its standardized evaluation metrics such as mean Average Precision (mAP).
|
||||
|
||||
### How do I train a YOLO26 model using the VOC dataset?
|
||||
|
||||
To train a YOLO26 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLO26n model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=VOC.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are the primary challenges included in the VOC dataset?
|
||||
|
||||
The VOC dataset includes two main challenges: VOC2007 and VOC2012. These challenges test object detection, segmentation, and classification across 20 diverse object categories. Each image is meticulously annotated with bounding boxes, class labels, and segmentation masks. The challenges provide standardized metrics like mAP, facilitating the comparison and benchmarking of different computer vision models.
|
||||
|
||||
### How does the PASCAL VOC dataset enhance model benchmarking and evaluation?
|
||||
|
||||
The PASCAL VOC dataset enhances model benchmarking and evaluation through its detailed annotations and standardized metrics like mean Average [Precision](https://www.ultralytics.com/glossary/precision) (mAP). These metrics are crucial for assessing the performance of object detection and classification models. The dataset's diverse and complex images ensure comprehensive model evaluation across various real-world scenarios.
|
||||
|
||||
### How do I use the VOC dataset for [semantic segmentation](https://www.ultralytics.com/glossary/semantic-segmentation) in YOLO models?
|
||||
|
||||
To use the VOC dataset for semantic segmentation tasks with YOLO models, you need to configure the dataset properly in a YAML file. The YAML file defines paths and classes needed for training segmentation models. Check the VOC dataset YAML configuration file at [VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml) for detailed setups. For segmentation tasks, you would use a segmentation-specific model like `yolo26n-seg.pt` instead of the detection model.
|
||||
188
algorithms/dms_yolo/code/docs/en/datasets/detect/xview.md
Normal file
188
algorithms/dms_yolo/code/docs/en/datasets/detect/xview.md
Normal file
@@ -0,0 +1,188 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the xView dataset, a rich resource of 1M+ object instances in high-resolution satellite imagery. Enhance detection, learning efficiency, and more.
|
||||
keywords: xView dataset, overhead imagery, satellite images, object detection, high resolution, bounding boxes, computer vision, TensorFlow, PyTorch, dataset structure
|
||||
---
|
||||
|
||||
# xView Dataset
|
||||
|
||||
The [xView](http://xviewdataset.org/) dataset is one of the largest publicly available datasets of overhead imagery, containing images from complex scenes around the world annotated using bounding boxes. The goal of the xView dataset is to accelerate progress in four [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) frontiers:
|
||||
|
||||
1. Reduce minimum resolution for detection.
|
||||
2. Improve learning efficiency.
|
||||
3. Enable discovery of more object classes.
|
||||
4. Improve detection of fine-grained classes.
|
||||
|
||||
xView builds on the success of challenges like [Common Objects in Context (COCO)](../detect/coco.md) and aims to leverage computer vision to analyze the growing amount of available imagery from space in order to understand the visual world in new ways and address a range of important applications.
|
||||
|
||||
!!! warning "Manual Download Required"
|
||||
|
||||
The xView dataset is **not** automatically downloaded by Ultralytics scripts. You **must** manually download the dataset first from the official source:
|
||||
|
||||
- **Source:** DIUx xView 2018 Challenge by U.S. National Geospatial-Intelligence Agency (NGA)
|
||||
- **URL:** [https://challenge.xviewdataset.org](https://challenge.xviewdataset.org)
|
||||
|
||||
**Important:** After downloading the necessary files (e.g., `train_images.tif`, `val_images.tif`, `xView_train.geojson`), you need to extract them and place them into the correct directory structure, typically expected under a `datasets/xView/` folder, **before** running the training commands provided below. Ensure the dataset is properly set up as per the challenge instructions.
|
||||
|
||||
## Key Features
|
||||
|
||||
- xView contains over 1 million object instances across 60 classes.
|
||||
- The dataset has a resolution of 0.3 meters, providing higher resolution imagery than most public satellite imagery datasets.
|
||||
- xView features a diverse collection of small, rare, fine-grained, and multi-type objects with [bounding box](https://www.ultralytics.com/glossary/bounding-box) annotation.
|
||||
- Comes with a pretrained baseline model using the [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) object detection API and an example for [PyTorch](https://www.ultralytics.com/glossary/pytorch).
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The xView dataset is composed of satellite images collected from WorldView-3 satellites at a 0.3m ground sample distance. It contains over 1 million objects across 60 classes in over 1,400 km² of imagery. The dataset is particularly valuable for [remote sensing](https://www.ultralytics.com/blog/using-computer-vision-to-analyze-satellite-imagery) applications and environmental monitoring.
|
||||
|
||||
## Applications
|
||||
|
||||
The xView dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models for object detection in overhead imagery. The dataset's diverse set of object classes and high-resolution imagery make it a valuable resource for researchers and practitioners in the field of computer vision, especially for satellite imagery analysis. Applications include:
|
||||
|
||||
- Military and defense reconnaissance
|
||||
- Urban planning and development
|
||||
- Environmental monitoring
|
||||
- Disaster response and assessment
|
||||
- Infrastructure mapping and management
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the xView dataset, the `xView.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/xView.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/xView.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="xView.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=xView.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The xView dataset contains high-resolution satellite images with a diverse set of objects annotated using bounding boxes. Here are some examples of data from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Overhead Imagery**: This image demonstrates an example of [object detection](https://www.ultralytics.com/glossary/object-detection) in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task.
|
||||
|
||||
The example showcases the variety and complexity of the data in the xView dataset and highlights the importance of high-quality satellite imagery for object detection tasks.
|
||||
|
||||
## Related Datasets
|
||||
|
||||
If you're working with satellite imagery, you might also be interested in exploring these related datasets:
|
||||
|
||||
- [DOTA-v2](../obb/dota-v2.md): A dataset for oriented object detection in aerial images
|
||||
- [VisDrone](../detect/visdrone.md): A dataset for object detection and tracking in drone-captured imagery
|
||||
- [Argoverse](../detect/argoverse.md): A dataset for autonomous driving with 3D tracking annotations
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the xView dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lam2018xview,
|
||||
title={xView: Objects in Context in Overhead Imagery},
|
||||
author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
|
||||
year={2018},
|
||||
eprint={1802.07856},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the xView dataset and how does it benefit computer vision research?
|
||||
|
||||
The [xView](http://xviewdataset.org/) dataset is one of the largest publicly available collections of high-resolution overhead imagery, containing over 1 million object instances across 60 classes. It is designed to enhance various facets of computer vision research such as reducing the minimum resolution for detection, improving learning efficiency, discovering more object classes, and advancing fine-grained object detection.
|
||||
|
||||
### How can I use Ultralytics YOLO to train a model on the xView dataset?
|
||||
|
||||
To train a model on the xView dataset using [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo26/), follow these steps:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="xView.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo detect train data=xView.yaml model=yolo26n.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For detailed arguments and settings, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the key features of the xView dataset?
|
||||
|
||||
The xView dataset stands out due to its comprehensive set of features:
|
||||
|
||||
- Over 1 million object instances across 60 distinct classes.
|
||||
- High-resolution imagery at 0.3 meters.
|
||||
- Diverse object types including small, rare, and fine-grained objects, all annotated with bounding boxes.
|
||||
- Availability of a pretrained baseline model and examples in [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and PyTorch.
|
||||
|
||||
### What is the dataset structure of xView, and how is it annotated?
|
||||
|
||||
The xView dataset contains high-resolution satellite imagery captured by WorldView-3 satellites at a 0.3m ground sample distance, covering over 1 million objects across 60 distinct classes within approximately 1,400 km² of annotated imagery. Each object is labeled with bounding boxes, making the dataset highly suitable for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models for object detection in overhead views. For a detailed breakdown, refer to the [Dataset Structure section](#dataset-structure).
|
||||
|
||||
### How do I cite the xView dataset in my research?
|
||||
|
||||
If you utilize the xView dataset in your research, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lam2018xview,
|
||||
title={xView: Objects in Context in Overhead Imagery},
|
||||
author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
|
||||
year={2018},
|
||||
eprint={1802.07856},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
For more information about the xView dataset, visit the official [xView dataset website](http://xviewdataset.org/).
|
||||
395
algorithms/dms_yolo/code/docs/en/datasets/explorer/api.md
Normal file
395
algorithms/dms_yolo/code/docs/en/datasets/explorer/api.md
Normal file
@@ -0,0 +1,395 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics Explorer API for dataset exploration with SQL queries, vector similarity search, and semantic search. Learn installation and usage tips.
|
||||
keywords: Ultralytics, Explorer API, dataset exploration, SQL queries, similarity search, semantic search, Python API, embeddings, data analysis
|
||||
---
|
||||
|
||||
# Ultralytics Explorer API
|
||||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
## Introduction
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
The Explorer API is a Python API for exploring your datasets. It supports filtering and searching your dataset using SQL queries, vector similarity search, and semantic search.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/3VryynorQeo?start=279"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics Explorer API Overview
|
||||
</p>
|
||||
|
||||
## Installation
|
||||
|
||||
Explorer depends on external libraries for some of its functionality. These are automatically installed when you use Explorer. To manually install these dependencies, use the following command:
|
||||
|
||||
```bash
|
||||
pip install ultralytics[explorer]
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# Create an Explorer object
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
|
||||
# Create embeddings for your dataset
|
||||
explorer.create_embeddings_table()
|
||||
|
||||
# Search for similar images to a given image/images
|
||||
df = explorer.get_similar(img="path/to/image.jpg")
|
||||
|
||||
# Or search for similar images to a given index/indices
|
||||
df = explorer.get_similar(idx=0)
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
[Embeddings](https://www.ultralytics.com/glossary/embeddings) table for a given dataset and model pair is only created once and reused. These use [LanceDB](https://lancedb.github.io/lancedb/) under the hood, which scales on-disk, so you can create and reuse embeddings for large datasets like COCO without running out of memory.
|
||||
|
||||
In case you want to force update the embeddings table, you can pass `force=True` to `create_embeddings_table` method.
|
||||
|
||||
You can directly access the LanceDB table object to perform advanced analysis. Learn more about it in the [Working with Embeddings Table section](#4-working-with-embeddings-table)
|
||||
|
||||
## 1. Similarity Search
|
||||
|
||||
Similarity search is a technique for finding similar images to a given image. It is based on the idea that similar images will have similar embeddings. Once the embeddings table is built, you can get run semantic search in any of the following ways:
|
||||
|
||||
- On a given index or list of indices in the dataset: `exp.get_similar(idx=[1,10], limit=10)`
|
||||
- On any image or list of images not in the dataset: `exp.get_similar(img=["path/to/img1", "path/to/img2"], limit=10)`
|
||||
|
||||
In case of multiple inputs, the aggregate of their embeddings is used.
|
||||
|
||||
You get a pandas DataFrame with the `limit` number of most similar data points to the input, along with their distance in the embedding space. You can use this dataset to perform further filtering.
|
||||
|
||||
!!! example "Semantic Search"
|
||||
|
||||
=== "Using Images"
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
|
||||
print(similar.head())
|
||||
|
||||
# Search using multiple indices
|
||||
similar = exp.get_similar(
|
||||
img=["https://ultralytics.com/images/bus.jpg", "https://ultralytics.com/images/bus.jpg"],
|
||||
limit=10,
|
||||
)
|
||||
print(similar.head())
|
||||
```
|
||||
|
||||
=== "Using Dataset Indices"
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
similar = exp.get_similar(idx=1, limit=10)
|
||||
print(similar.head())
|
||||
|
||||
# Search using multiple indices
|
||||
similar = exp.get_similar(idx=[1, 10], limit=10)
|
||||
print(similar.head())
|
||||
```
|
||||
|
||||
### Plotting Similar Images
|
||||
|
||||
You can also plot the similar images using the `plot_similar` method. This method takes the same arguments as `get_similar` and plots the similar images in a grid.
|
||||
|
||||
!!! example "Plotting Similar Images"
|
||||
|
||||
=== "Using Images"
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
plt = exp.plot_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
=== "Using Dataset Indices"
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
plt = exp.plot_similar(idx=1, limit=10)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## 2. Ask AI (Natural Language Querying)
|
||||
|
||||
This feature lets you filter your dataset using natural language, without writing SQL. The AI-powered query generator converts your prompt into a query and returns matching results. For example, you can ask: "show me 100 images with exactly one person and 2 dogs. There can be other objects too" and it will generate the query and show you those results.
|
||||
Note: This feature uses LLMs, so results are probabilistic and may be inaccurate.
|
||||
|
||||
!!! example "Ask AI"
|
||||
|
||||
```python
|
||||
from ultralytics.data.explorer import plot_query_result
|
||||
|
||||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
df = exp.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
|
||||
print(df.head())
|
||||
|
||||
# plot the results
|
||||
plt = plot_query_result(df)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## 3. SQL Querying
|
||||
|
||||
You can run SQL queries on your dataset using the `sql_query` method. This method takes a SQL query as input and returns a pandas DataFrame with the results.
|
||||
|
||||
!!! example "SQL Query"
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'")
|
||||
print(df.head())
|
||||
```
|
||||
|
||||
### Plotting SQL Query Results
|
||||
|
||||
You can also plot the results of a SQL query using the `plot_sql_query` method. This method takes the same arguments as `sql_query` and plots the results in a grid.
|
||||
|
||||
!!! example "Plotting SQL Query Results"
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# create an Explorer object
|
||||
exp = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
|
||||
# plot the SQL Query
|
||||
exp.plot_sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%' LIMIT 10")
|
||||
```
|
||||
|
||||
## 4. Working with Embeddings Table
|
||||
|
||||
You can also work with the embeddings table directly. Once the embeddings table is created, you can access it using the `Explorer.table`
|
||||
|
||||
!!! tip
|
||||
|
||||
Explorer works on [LanceDB](https://lancedb.github.io/lancedb/) tables internally. You can access this table directly, using `Explorer.table` object and run raw queries, push down pre- and post-filters, etc.
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
exp = Explorer()
|
||||
exp.create_embeddings_table()
|
||||
table = exp.table
|
||||
```
|
||||
|
||||
Here are some examples of what you can do with the table:
|
||||
|
||||
### Get raw Embeddings
|
||||
|
||||
!!! example
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
exp = Explorer()
|
||||
exp.create_embeddings_table()
|
||||
table = exp.table
|
||||
|
||||
embeddings = table.to_pandas()["vector"]
|
||||
print(embeddings)
|
||||
```
|
||||
|
||||
### Advanced Querying with pre- and post-filters
|
||||
|
||||
!!! example
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
exp = Explorer(model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
table = exp.table
|
||||
|
||||
# Dummy embedding
|
||||
embedding = [i for i in range(256)]
|
||||
rs = table.search(embedding).metric("cosine").where("").limit(10)
|
||||
```
|
||||
|
||||
### Create Vector Index
|
||||
|
||||
When using large datasets, you can also create a dedicated vector index for faster querying. This is done using the `create_index` method on LanceDB table.
|
||||
|
||||
```python
|
||||
table.create_index(num_partitions=..., num_sub_vectors=...)
|
||||
```
|
||||
|
||||
## 5. Embeddings Applications
|
||||
|
||||
You can use the embeddings table to perform a variety of exploratory analysis. Here are some examples:
|
||||
|
||||
### Similarity Index
|
||||
|
||||
Explorer comes with a `similarity_index` operation:
|
||||
|
||||
- It tries to estimate how similar each data point is with the rest of the dataset.
|
||||
- It does that by counting how many image embeddings lie closer than `max_dist` to the current image in the generated embedding space, considering `top_k` similar images at a time.
|
||||
|
||||
It returns a pandas DataFrame with the following columns:
|
||||
|
||||
- `idx`: Index of the image in the dataset
|
||||
- `im_file`: Path to the image file
|
||||
- `count`: Number of images in the dataset that are closer than `max_dist` to the current image
|
||||
- `sim_im_files`: List of paths to the `count` similar images
|
||||
|
||||
!!! tip
|
||||
|
||||
For a given dataset, model, `max_dist` & `top_k` the similarity index once generated will be reused. In case, your dataset has changed, or you simply need to regenerate the similarity index, you can pass `force=True`.
|
||||
|
||||
!!! example "Similarity Index"
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
exp = Explorer()
|
||||
exp.create_embeddings_table()
|
||||
|
||||
sim_idx = exp.similarity_index()
|
||||
```
|
||||
|
||||
You can use similarity index to build custom conditions to filter out the dataset. For example, you can filter out images that are not similar to any other image in the dataset using the following code:
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
|
||||
sim_count = np.array(sim_idx["count"])
|
||||
sim_idx["im_file"][sim_count > 30]
|
||||
```
|
||||
|
||||
### Visualize Embedding Space
|
||||
|
||||
You can also visualize the embedding space using the plotting tool of your choice. For example here is a simple example using matplotlib:
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.decomposition import PCA
|
||||
|
||||
# Reduce dimensions using PCA to 3 components for visualization in 3D
|
||||
pca = PCA(n_components=3)
|
||||
reduced_data = pca.fit_transform(embeddings)
|
||||
|
||||
# Create a 3D scatter plot using Matplotlib Axes3D
|
||||
fig = plt.figure(figsize=(8, 6))
|
||||
ax = fig.add_subplot(111, projection="3d")
|
||||
|
||||
# Scatter plot
|
||||
ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], alpha=0.5)
|
||||
ax.set_title("3D Scatter Plot of Reduced 256-Dimensional Data (PCA)")
|
||||
ax.set_xlabel("Component 1")
|
||||
ax.set_ylabel("Component 2")
|
||||
ax.set_zlabel("Component 3")
|
||||
|
||||
plt.show()
|
||||
```
|
||||
|
||||
Start creating your own CV dataset exploration reports using the Explorer API. For inspiration, check out the [VOC Exploration Example](explorer.md).
|
||||
|
||||
## Apps Built Using Ultralytics Explorer
|
||||
|
||||
Try our [GUI Demo](dashboard.md) based on Explorer API
|
||||
|
||||
## Coming Soon
|
||||
|
||||
- [ ] Merge specific labels from datasets. Example - Import all `person` labels from COCO and `car` labels from Cityscapes
|
||||
- [ ] Remove images that have a higher similarity index than the given threshold
|
||||
- [ ] Automatically persist new datasets after merging/removing entries
|
||||
- [ ] Advanced Dataset Visualizations
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Ultralytics Explorer API used for?
|
||||
|
||||
The Ultralytics Explorer API is designed for comprehensive dataset exploration. It allows users to filter and search datasets using SQL queries, vector similarity search, and semantic search. This powerful Python API can handle large datasets, making it ideal for various [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks using Ultralytics models.
|
||||
|
||||
### How do I install the Ultralytics Explorer API?
|
||||
|
||||
To install the Ultralytics Explorer API along with its dependencies, use the following command:
|
||||
|
||||
```bash
|
||||
pip install ultralytics[explorer]
|
||||
```
|
||||
|
||||
This will automatically install all necessary external libraries for the Explorer API functionality. For additional setup details, refer to the [installation section](#installation) of our documentation.
|
||||
|
||||
### How can I use the Ultralytics Explorer API for similarity search?
|
||||
|
||||
You can use the Ultralytics Explorer API to perform similarity searches by creating an embeddings table and querying it for similar images. Here's a basic example:
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# Create an Explorer object
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
explorer.create_embeddings_table()
|
||||
|
||||
# Search for similar images to a given image
|
||||
similar_images_df = explorer.get_similar(img="path/to/image.jpg")
|
||||
print(similar_images_df.head())
|
||||
```
|
||||
|
||||
For more details, please visit the [Similarity Search section](#1-similarity-search).
|
||||
|
||||
### What are the benefits of using LanceDB with Ultralytics Explorer?
|
||||
|
||||
LanceDB, used under the hood by Ultralytics Explorer, provides scalable, on-disk embeddings tables. This ensures that you can create and reuse embeddings for large datasets like COCO without running out of memory. These tables are only created once and can be reused, enhancing efficiency in data handling.
|
||||
|
||||
### How does the Ask AI feature work in the Ultralytics Explorer API?
|
||||
|
||||
The Ask AI feature allows users to filter datasets using natural language queries. This feature leverages LLMs to convert these queries into SQL queries behind the scenes. Here's an example:
|
||||
|
||||
```python
|
||||
from ultralytics import Explorer
|
||||
|
||||
# Create an Explorer object
|
||||
explorer = Explorer(data="coco128.yaml", model="yolo26n.pt")
|
||||
explorer.create_embeddings_table()
|
||||
|
||||
# Query with natural language
|
||||
query_result = explorer.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
|
||||
print(query_result.head())
|
||||
```
|
||||
|
||||
For more examples, check out the [Ask AI section](#2-ask-ai-natural-language-querying).
|
||||
127
algorithms/dms_yolo/code/docs/en/datasets/explorer/dashboard.md
Normal file
127
algorithms/dms_yolo/code/docs/en/datasets/explorer/dashboard.md
Normal file
@@ -0,0 +1,127 @@
|
||||
---
|
||||
comments: true
|
||||
description: Unlock advanced data exploration with Ultralytics Explorer GUI. Utilize semantic search, run SQL queries, and ask AI for natural language data insights.
|
||||
keywords: Ultralytics Explorer GUI, semantic search, vector similarity, SQL queries, AI, natural language search, data exploration, machine learning, OpenAI, LLMs
|
||||
---
|
||||
|
||||
# Explorer GUI
|
||||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
Explorer GUI is built on the [Ultralytics Explorer API](api.md). It allows you to run semantic/vector similarity search, SQL queries, and natural language queries using the Ask AI feature powered by LLMs.
|
||||
|
||||
<p>
|
||||
<img width="1709" alt="Ultralytics Explorer GUI main dashboard interface" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/explorer-dashboard-screenshot-1.avif">
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/3VryynorQeo?start=306"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics Explorer Dashboard Overview
|
||||
</p>
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
pip install ultralytics[explorer]
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
The Ask AI feature uses OpenAI, so you will be prompted to set the OpenAI API key when you first run the GUI.
|
||||
Set it with `yolo settings openai_api_key="..."`.
|
||||
|
||||
## Vector Semantic Similarity Search
|
||||
|
||||
[Semantic search](https://www.ultralytics.com/glossary/semantic-search) is a technique for finding similar images to a given image. It is based on the idea that similar images will have similar [embeddings](https://www.ultralytics.com/glossary/embeddings). In the UI, you can select one or more images and search for the images similar to them. This can be useful when you want to find images similar to a given image or a set of images that don't perform as expected.
|
||||
|
||||
For example, in this VOC Exploration dashboard, the user selects a few airplane images:
|
||||
|
||||
<p>
|
||||
<img width="1710" alt="Explorer selecting airplane images for similarity search" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/explorer-dashboard-screenshot-2.avif">
|
||||
</p>
|
||||
|
||||
After running the similarity search, you should see similar results:
|
||||
|
||||
<p>
|
||||
<img width="1710" alt="Ultralytics Explorer semantic similarity search" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/explorer-dashboard-screenshot-3.avif">
|
||||
</p>
|
||||
|
||||
## Ask AI
|
||||
|
||||
This feature lets you filter your dataset using natural language, without writing SQL. The AI-powered query generator converts your prompt into a query and returns matching results. For example, you can ask: "show me 100 images with exactly one person and 2 dogs. There can be other objects too" and it will generate the query and show you those results. Here is an example output when asked: "Show 10 images with exactly 5 persons":
|
||||
|
||||
<p>
|
||||
<img width="1709" alt="Explorer Ask AI results for images with 5 persons" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/explorer-dashboard-screenshot-4.avif">
|
||||
</p>
|
||||
|
||||
Note: This feature uses [Large Language Models](https://www.ultralytics.com/glossary/large-language-model-llm), so results are probabilistic and may be inaccurate.
|
||||
|
||||
## Run SQL queries on your CV datasets
|
||||
|
||||
You can run SQL queries on your dataset to filter it. It also works if you only provide the WHERE clause. For example, the following WHERE clause returns images that contain at least one person and one dog:
|
||||
|
||||
```sql
|
||||
WHERE labels LIKE '%person%' AND labels LIKE '%dog%'
|
||||
```
|
||||
|
||||
<p>
|
||||
<img width="1707" alt="Explorer SQL query filtering images with person and dog" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/explorer-dashboard-screenshot-5.avif">
|
||||
</p>
|
||||
|
||||
This demo was built using the Explorer API, which you can use to create your own exploratory notebooks or scripts for gaining insights into your datasets. To get started, check out the [Explorer API documentation](api.md).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is Ultralytics Explorer GUI and how do I install it?
|
||||
|
||||
Ultralytics Explorer GUI is a powerful interface that unlocks advanced data exploration capabilities using the [Ultralytics Explorer API](api.md). It allows you to run semantic/vector similarity search, SQL queries, and natural language queries using the Ask AI feature powered by [Large Language Models](https://www.ultralytics.com/glossary/large-language-model-llm) (LLMs).
|
||||
|
||||
To install the Explorer GUI, you can use pip:
|
||||
|
||||
```bash
|
||||
pip install ultralytics[explorer]
|
||||
```
|
||||
|
||||
Note: To use the Ask AI feature, you'll need to set the OpenAI API key: `yolo settings openai_api_key="..."`.
|
||||
|
||||
### How does the semantic search feature in Ultralytics Explorer GUI work?
|
||||
|
||||
The semantic search feature in Ultralytics Explorer GUI allows you to find images similar to a given image based on their embeddings. This technique is useful for identifying and exploring images that share visual similarities. To use this feature, select one or more images in the UI and execute a search for similar images. The result will display images that closely resemble the selected ones, facilitating efficient dataset exploration and [anomaly detection](https://www.ultralytics.com/glossary/anomaly-detection).
|
||||
|
||||
Learn more about semantic search and other features by visiting the [Feature Overview](#vector-semantic-similarity-search) section.
|
||||
|
||||
### Can I use natural language to filter datasets in Ultralytics Explorer GUI?
|
||||
|
||||
Yes, with the Ask AI feature powered by large language models (LLMs), you can filter your datasets using natural language queries. You don't need to be proficient in SQL. For instance, you can ask "Show me 100 images with exactly one person and 2 dogs. There can be other objects too," and the AI will generate the appropriate query under the hood to deliver the desired results.
|
||||
|
||||
### How do I run SQL queries on datasets using Ultralytics Explorer GUI?
|
||||
|
||||
Ultralytics Explorer GUI allows you to run SQL queries directly on your dataset to filter and manage data efficiently. To run a query, navigate to the SQL query section in the GUI and write your query. For example, to show images with at least one person and one dog, you could use:
|
||||
|
||||
```sql
|
||||
WHERE labels LIKE '%person%' AND labels LIKE '%dog%'
|
||||
```
|
||||
|
||||
You can also provide only the WHERE clause, making the querying process more flexible.
|
||||
|
||||
For more details, refer to the [SQL Queries Section](#run-sql-queries-on-your-cv-datasets).
|
||||
|
||||
### What are the benefits of using Ultralytics Explorer GUI for data exploration?
|
||||
|
||||
Ultralytics Explorer GUI enhances data exploration with features like semantic search, SQL querying, and natural language interactions through the Ask AI feature. These capabilities allow users to:
|
||||
|
||||
- Efficiently find visually similar images.
|
||||
- Filter datasets using complex SQL queries.
|
||||
- Utilize AI to perform natural language searches, eliminating the need for advanced SQL expertise.
|
||||
|
||||
These features make it a versatile tool for developers, researchers, and data scientists looking to gain deeper insights into their datasets.
|
||||
|
||||
Explore more about these features in the [Explorer GUI Documentation](#explorer-gui).
|
||||
277
algorithms/dms_yolo/code/docs/en/datasets/explorer/explorer.md
Normal file
277
algorithms/dms_yolo/code/docs/en/datasets/explorer/explorer.md
Normal file
@@ -0,0 +1,277 @@
|
||||
---
|
||||
comments: true
|
||||
description: Dive into advanced data exploration with Ultralytics Explorer. Perform semantic searches, execute SQL queries, and leverage AI-powered natural language insights for seamless data analysis.
|
||||
keywords: Ultralytics Explorer, data exploration, semantic search, vector similarity, SQL queries, AI, natural language queries, machine learning, OpenAI, LLMs, Ultralytics Platform
|
||||
---
|
||||
|
||||
# VOC Exploration Example
|
||||
|
||||
<div align="center">
|
||||
|
||||
<a href="https://www.ultralytics.com/events/yolovision" target="_blank"><img width="100%" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/ultralytics-yolov8-banner.avif" alt="Ultralytics YOLO banner"></a>
|
||||
<a href="https://docs.ultralytics.com/zh/">中文</a> |
|
||||
<a href="https://docs.ultralytics.com/ko/">한국어</a> |
|
||||
<a href="https://docs.ultralytics.com/ja/">日本語</a> |
|
||||
<a href="https://docs.ultralytics.com/ru/">Русский</a> |
|
||||
<a href="https://docs.ultralytics.com/de/">Deutsch</a> |
|
||||
<a href="https://docs.ultralytics.com/fr/">Français</a> |
|
||||
<a href="https://docs.ultralytics.com/es">Español</a> |
|
||||
<a href="https://docs.ultralytics.com/pt/">Português</a> |
|
||||
<a href="https://docs.ultralytics.com/tr/">Türkçe</a> |
|
||||
<a href="https://docs.ultralytics.com/vi/">Tiếng Việt</a> |
|
||||
<a href="https://docs.ultralytics.com/ar/">العربية</a>
|
||||
<br>
|
||||
|
||||
<br>
|
||||
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
|
||||
<a href="https://clickpy.clickhouse.com/dashboard/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
|
||||
<a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
|
||||
<a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
|
||||
<a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
|
||||
<br>
|
||||
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics on Gradient"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Ultralytics In Colab"></a>
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolo26"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
|
||||
<a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
|
||||
<br>
|
||||
</div>
|
||||
|
||||
Welcome to the Ultralytics Explorer API notebook. This notebook introduces the resources available for exploring datasets with semantic search, vector search, and SQL queries.
|
||||
|
||||
Try `yolo explorer` (powered by the Explorer API)
|
||||
|
||||
Install `ultralytics` and run `yolo explorer` in your terminal to run custom queries and semantic search in your browser.
|
||||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
## Setup
|
||||
|
||||
Install `ultralytics` and the required [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml), then check software and hardware.
|
||||
|
||||
```bash
|
||||
!uv pip install ultralytics[explorer] openai
|
||||
yolo checks
|
||||
```
|
||||
|
||||
## Similarity Search
|
||||
|
||||
Utilize the power of vector similarity search to find the similar data points in your dataset along with their distance in the embedding space. Simply create an embeddings table for the given dataset-model pair. It is only needed once, and it is reused automatically.
|
||||
|
||||
```python
|
||||
exp = Explorer("VOC.yaml", model="yolo26n.pt")
|
||||
exp.create_embeddings_table()
|
||||
```
|
||||
|
||||
Once the embeddings table is built, you can run semantic search in any of the following ways:
|
||||
|
||||
- On a given index/list of indices in the dataset, e.g., `exp.get_similar(idx=[1, 10], limit=10)`
|
||||
- On any image/ list of images not in the dataset - exp.get_similar(img=["path/to/img1", "path/to/img2"], limit=10) In case of multiple inputs, the aggregate of their embeddings is used.
|
||||
|
||||
You get a pandas DataFrame with the limit number of most similar data points to the input, along with their distance in the embedding space. You can use this dataset to perform further filtering.
|
||||
|
||||
|
||||
|
||||
```python
|
||||
# Search dataset by index
|
||||
similar = exp.get_similar(idx=1, limit=10)
|
||||
similar.head()
|
||||
```
|
||||
|
||||
You can use the also plot the similar samples directly using the `plot_similar` util
|
||||
|
||||
|
||||
|
||||
```python
|
||||
exp.plot_similar(idx=6500, limit=20)
|
||||
exp.plot_similar(idx=[100, 101], limit=10) # Can also pass list of idxs or imgs
|
||||
|
||||
exp.plot_similar(img="https://ultralytics.com/images/bus.jpg", limit=10, labels=False) # Can also pass external images
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Ask AI: Search or Filter with Natural Language
|
||||
|
||||
You can prompt the Explorer object with the kind of data points you want to see, and it will try to return a DataFrame with those results. Because it is powered by LLMs, it does not always get it right. In that case, it will return `None`.
|
||||
|
||||
|
||||
|
||||
```python
|
||||
df = exp.ask_ai("show me images containing more than 10 objects with at least 2 persons")
|
||||
df.head(5)
|
||||
```
|
||||
|
||||
To plot these results, you can use the `plot_query_result` utility. Example:
|
||||
|
||||
```python
|
||||
plt = plot_query_result(exp.ask_ai("show me 10 images containing exactly 2 persons"))
|
||||
Image.fromarray(plt)
|
||||
```
|
||||
|
||||
|
||||
|
||||
```python
|
||||
# plot
|
||||
from PIL import Image
|
||||
from ultralytics.data.explorer import plot_query_result
|
||||
|
||||
plt = plot_query_result(exp.ask_ai("show me 10 images containing exactly 2 persons"))
|
||||
Image.fromarray(plt)
|
||||
```
|
||||
|
||||
## Run SQL Queries on Your Dataset
|
||||
|
||||
Sometimes you might want to investigate certain entries in your dataset. For this, Explorer allows you to execute SQL queries. It accepts either of the following formats:
|
||||
|
||||
- Queries beginning with "WHERE" will automatically select all columns. This can be thought of as a shorthand query.
|
||||
- You can also write full queries where you can specify which columns to select.
|
||||
|
||||
This can be used to investigate model performance and specific data points. For example:
|
||||
|
||||
- let's say your model struggles on images that have humans and dogs. You can write a query like this to select the points that have at least 2 humans AND at least one dog.
|
||||
|
||||
You can combine SQL query and semantic search to filter down to specific type of results
|
||||
|
||||
```python
|
||||
table = exp.sql_query("WHERE labels LIKE '%person, person%' AND labels LIKE '%dog%' LIMIT 10")
|
||||
exp.plot_sql_query("WHERE labels LIKE '%person, person%' AND labels LIKE '%dog%' LIMIT 10", labels=True)
|
||||
```
|
||||
|
||||
|
||||
|
||||
```python
|
||||
table = exp.sql_query("WHERE labels LIKE '%person, person%' AND labels LIKE '%dog%' LIMIT 10")
|
||||
print(table)
|
||||
```
|
||||
|
||||
Just like similarity search, you also get a util to directly plot the sql queries using `exp.plot_sql_query`
|
||||
|
||||
|
||||
|
||||
```python
|
||||
exp.plot_sql_query("WHERE labels LIKE '%person, person%' AND labels LIKE '%dog%' LIMIT 10", labels=True)
|
||||
```
|
||||
|
||||
## Working with embeddings Table (Advanced)
|
||||
|
||||
Explorer works on [LanceDB](https://lancedb.github.io/lancedb/) tables internally. You can access this table directly, using `Explorer.table` object and run raw queries, push down pre- and post-filters, etc.
|
||||
|
||||
```python
|
||||
table = exp.table
|
||||
print(table.schema)
|
||||
```
|
||||
|
||||
### Run raw queries¶
|
||||
|
||||
Vector Search finds the nearest vectors from the database. In a recommendation system or search engine, you can find similar products from the one you searched. In LLM and other AI applications, each data point can be presented by the embeddings generated from some models, it returns the most relevant features.
|
||||
|
||||
A search in high-dimensional vector space, is to find K-Nearest-Neighbors (KNN) of the query vector.
|
||||
|
||||
Metric In LanceDB, a Metric is the way to describe the distance between a pair of vectors. Currently, it supports the following metrics:
|
||||
|
||||
- L2
|
||||
- Cosine
|
||||
- Dot Explorer's similarity search uses L2 by default. You can run queries on tables directly, or use the lance format to build custom utilities to manage datasets. More details on available LanceDB table ops in the [docs](https://lancedb.github.io/lancedb/)
|
||||
|
||||
|
||||
|
||||
```python
|
||||
dummy_img_embedding = [i for i in range(256)]
|
||||
table.search(dummy_img_embedding).limit(5).to_pandas()
|
||||
```
|
||||
|
||||
### Interconversion to popular data formats
|
||||
|
||||
```python
|
||||
df = table.to_pandas()
|
||||
pa_table = table.to_arrow()
|
||||
```
|
||||
|
||||
### Work with Embeddings
|
||||
|
||||
You can access the raw embedding from lancedb Table and analyze it. The image embeddings are stored in column `vector`
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
|
||||
embeddings = table.to_pandas()["vector"].tolist()
|
||||
embeddings = np.array(embeddings)
|
||||
```
|
||||
|
||||
### Scatterplot
|
||||
|
||||
One of the preliminary steps in analyzing embeddings is by plotting them in 2D space via dimensionality reduction. Let's try an example
|
||||
|
||||
|
||||
|
||||
```python
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.decomposition import PCA # pip install scikit-learn
|
||||
|
||||
# Reduce dimensions using PCA to 3 components for visualization in 3D
|
||||
pca = PCA(n_components=3)
|
||||
reduced_data = pca.fit_transform(embeddings)
|
||||
|
||||
# Create a 3D scatter plot using Matplotlib's Axes3D
|
||||
fig = plt.figure(figsize=(8, 6))
|
||||
ax = fig.add_subplot(111, projection="3d")
|
||||
|
||||
# Scatter plot
|
||||
ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], alpha=0.5)
|
||||
ax.set_title("3D Scatter Plot of Reduced 256-Dimensional Data (PCA)")
|
||||
ax.set_xlabel("Component 1")
|
||||
ax.set_ylabel("Component 2")
|
||||
ax.set_zlabel("Component 3")
|
||||
|
||||
plt.show()
|
||||
```
|
||||
|
||||
### Similarity Index
|
||||
|
||||
Here's a simple example of an operation powered by the embeddings table. Explorer comes with a `similarity_index` operation-
|
||||
|
||||
- It tries to estimate how similar each data point is with the rest of the dataset.
|
||||
- It does that by counting how many image embeddings lie closer than max_dist to the current image in the generated embedding space, considering top_k similar images at a time.
|
||||
|
||||
For a given dataset, model, `max_dist` & `top_k` the similarity index once generated will be reused. In case, your dataset has changed, or you simply need to regenerate the similarity index, you can pass `force=True`. Similar to vector and SQL search, this also comes with a util to directly plot it. Let's look
|
||||
|
||||
```python
|
||||
sim_idx = exp.similarity_index(max_dist=0.2, top_k=0.01)
|
||||
exp.plot_similarity_index(max_dist=0.2, top_k=0.01)
|
||||
```
|
||||
|
||||
|
||||
|
||||
at the plot first
|
||||
|
||||
```python
|
||||
exp.plot_similarity_index(max_dist=0.2, top_k=0.01)
|
||||
```
|
||||
|
||||
Now let's look at the output of the operation
|
||||
|
||||
```python
|
||||
sim_idx = exp.similarity_index(max_dist=0.2, top_k=0.01, force=False)
|
||||
|
||||
sim_idx
|
||||
```
|
||||
|
||||
Let's create a query to see what data points have similarity count of more than 30 and plot images similar to them.
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
|
||||
sim_count = np.array(sim_idx["count"])
|
||||
sim_idx["im_file"][sim_count > 30]
|
||||
```
|
||||
|
||||
You should see something like this
|
||||
|
||||
|
||||
|
||||
```python
|
||||
exp.plot_similar(idx=[7146, 14035]) # Using avg embeddings of 2 images
|
||||
```
|
||||
113
algorithms/dms_yolo/code/docs/en/datasets/explorer/index.md
Normal file
113
algorithms/dms_yolo/code/docs/en/datasets/explorer/index.md
Normal file
@@ -0,0 +1,113 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover Ultralytics Explorer for semantic search, SQL queries, vector similarity, and natural language dataset exploration.
|
||||
keywords: Ultralytics Explorer, CV datasets, semantic search, SQL queries, vector similarity, dataset visualization, python API, machine learning, computer vision
|
||||
---
|
||||
|
||||
# Ultralytics Explorer
|
||||
|
||||
!!! warning "Community Note ⚠️"
|
||||
|
||||
As of **`ultralytics>=8.3.10`**, Ultralytics Explorer support is deprecated. Similar (and expanded) dataset exploration features are available in [Ultralytics Platform](https://platform.ultralytics.com/).
|
||||
|
||||
<p>
|
||||
<img width="1709" alt="Ultralytics Explorer dataset visualization GUI" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/explorer-dashboard-screenshot-1.avif">
|
||||
</p>
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
|
||||
Ultralytics Explorer is a tool for exploring CV datasets using semantic search, SQL queries, vector similarity search, and natural language prompts. It also provides a Python API for accessing the same functionality.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/3VryynorQeo"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics Explorer API | Semantic Search, SQL Queries & Ask AI Features
|
||||
</p>
|
||||
|
||||
## Installation of Optional Dependencies
|
||||
|
||||
Explorer depends on external libraries for some of its functionality. These are automatically installed when you use Explorer. To manually install these dependencies, use the following command:
|
||||
|
||||
```bash
|
||||
pip install ultralytics[explorer]
|
||||
```
|
||||
|
||||
!!! tip
|
||||
|
||||
Explorer works on embedding/semantic search & SQL querying and is powered by [LanceDB](https://lancedb.com/) serverless vector database. Unlike traditional in-memory DBs, it is persisted on disk without sacrificing performance, so you can scale locally to large datasets like COCO without running out of memory.
|
||||
|
||||
## Explorer API
|
||||
|
||||
This is a Python API for exploring your datasets. It also powers the GUI Explorer. You can use this to create your own exploratory notebooks or scripts to get insights into your datasets.
|
||||
|
||||
Explore the full capabilities and usage examples in the [Explorer API documentation](api.md).
|
||||
|
||||
## GUI Explorer Usage
|
||||
|
||||
The GUI demo runs in your browser allowing you to create [embeddings](https://www.ultralytics.com/glossary/embeddings) for your dataset and search for similar images, run SQL queries and perform semantic search. It can be run using the following command:
|
||||
|
||||
```bash
|
||||
yolo explorer
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
The Ask AI feature uses OpenAI, so you'll be prompted to set the API key for OpenAI when you first run the GUI.
|
||||
You can set it like this - `yolo settings openai_api_key="..."`
|
||||
|
||||
<p>
|
||||
<img width="1709" alt="Ultralytics Explorer OpenAI Integration" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/ultralytics-explorer-openai-integration.avif">
|
||||
</p>
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is Ultralytics Explorer and how can it help with CV datasets?
|
||||
|
||||
Ultralytics Explorer is a powerful tool designed for exploring [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) (CV) datasets through semantic search, SQL queries, vector similarity search, and even natural language. This versatile tool provides both a GUI and a Python API, allowing users to seamlessly interact with their datasets. By leveraging technologies like [LanceDB](https://lancedb.com/), Ultralytics Explorer ensures efficient, scalable access to large datasets without excessive memory usage. Whether you're performing detailed dataset analysis or exploring data patterns, Ultralytics Explorer streamlines the entire process.
|
||||
|
||||
Learn more about the [Explorer API](api.md).
|
||||
|
||||
### How do I install the dependencies for Ultralytics Explorer?
|
||||
|
||||
To manually install the optional dependencies needed for Ultralytics Explorer, you can use the following `pip` command:
|
||||
|
||||
```bash
|
||||
pip install ultralytics[explorer]
|
||||
```
|
||||
|
||||
These dependencies are essential for the full functionality of semantic search and SQL querying. By including libraries powered by [LanceDB](https://lancedb.com/), the installation ensures that the database operations remain efficient and scalable, even for large datasets like [COCO](../detect/coco.md).
|
||||
|
||||
### How can I use the GUI version of Ultralytics Explorer?
|
||||
|
||||
Using the GUI version of Ultralytics Explorer is straightforward. After installing the necessary dependencies, you can launch the GUI with the following command:
|
||||
|
||||
```bash
|
||||
yolo explorer
|
||||
```
|
||||
|
||||
The GUI provides a user-friendly interface for creating dataset embeddings, searching for similar images, running SQL queries, and conducting semantic searches. Additionally, the integration with OpenAI's Ask AI feature allows you to query datasets using natural language, enhancing the flexibility and ease of use.
|
||||
|
||||
For storage and scalability information, check out our [installation instructions](#installation-of-optional-dependencies).
|
||||
|
||||
### What is the Ask AI feature in Ultralytics Explorer?
|
||||
|
||||
The Ask AI feature in Ultralytics Explorer allows users to interact with their datasets using natural language queries. Powered by [OpenAI](https://www.ultralytics.com/blog/openai-gpt-4o-showcases-ai-potential), this feature enables you to ask complex questions and receive insightful answers without needing to write SQL queries or similar commands. To use this feature, you'll need to set your OpenAI API key the first time you run the GUI:
|
||||
|
||||
```bash
|
||||
yolo settings openai_api_key="YOUR_API_KEY"
|
||||
```
|
||||
|
||||
For more on this feature and how to integrate it, see our [GUI Explorer Usage](#gui-explorer-usage) section.
|
||||
|
||||
### Can I run Ultralytics Explorer in Google Colab?
|
||||
|
||||
Yes, Ultralytics Explorer can be run in Google Colab, providing a convenient and powerful environment for dataset exploration. You can start by opening the provided Colab notebook, which is pre-configured with all the necessary settings:
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
|
||||
This setup allows you to explore your datasets fully, taking advantage of Google's cloud resources. Learn more in our [Google Colab Guide](../../integrations/google-colab.md).
|
||||
239
algorithms/dms_yolo/code/docs/en/datasets/index.md
Normal file
239
algorithms/dms_yolo/code/docs/en/datasets/index.md
Normal file
@@ -0,0 +1,239 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore Ultralytics' diverse datasets for vision tasks like detection, segmentation, classification, and more. Enhance your projects with high-quality annotated data.
|
||||
keywords: Ultralytics, datasets, computer vision, object detection, instance segmentation, pose estimation, image classification, multi-object tracking
|
||||
---
|
||||
|
||||
# Datasets Overview
|
||||
|
||||
Ultralytics provides support for various datasets to facilitate computer vision tasks such as detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose estimation, classification, and multi-object tracking. Below is a list of the main Ultralytics datasets, followed by a summary of each computer vision task and the respective datasets.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/YDXKa1EljmU"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics Datasets Overview
|
||||
</p>
|
||||
|
||||
## [Object Detection](detect/index.md)
|
||||
|
||||
[Bounding box](https://www.ultralytics.com/glossary/bounding-box) object detection is a computer vision technique that involves detecting and localizing objects in an image by drawing a bounding box around each object.
|
||||
|
||||
- [African-wildlife](detect/african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephants, rhinos, and zebras.
|
||||
- [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
|
||||
- [Brain-tumor](detect/brain-tumor.md): A dataset for detecting brain tumors that includes MRI or CT scan images with details on tumor presence, location, and characteristics.
|
||||
- [COCO](detect/coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
|
||||
- [COCO8](detect/coco8.md): A smaller subset of the first 4 images from COCO train and COCO val, suitable for quick tests.
|
||||
- [COCO8-Grayscale](detect/coco8-grayscale.md): A grayscale version of COCO8 created by converting RGB to grayscale, useful for single-channel model evaluation.
|
||||
- [COCO8-Multispectral](detect/coco8-multispectral.md): A 10-channel multispectral version of COCO8 created by interpolating RGB wavelengths, useful for spectral-aware model evaluation.
|
||||
- [COCO128](detect/coco128.md): A smaller subset of the first 128 images from COCO train and COCO val, suitable for tests.
|
||||
- [Construction-PPE](detect/construction-ppe.md): A dataset of construction site imagery annotated with key safety gear such as helmets, vests, gloves, boots, and goggles, along with labels for missing equipment, supporting the development of AI models for compliance and worker protection.
|
||||
- [Global Wheat 2020](detect/globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
|
||||
- [HomeObjects-3K](detect/homeobjects-3k.md): A dataset of annotated indoor scenes featuring 12 common household items, ideal for developing and testing computer vision models in smart home systems, robotics, and augmented reality.
|
||||
- [KITTI](detect/kitti.md) New: A well-known autonomous driving dataset featuring stereo, LiDAR, and GPS/IMU inputs, used for 2D object detection in varied road scenes.
|
||||
- [LVIS](detect/lvis.md): A large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
|
||||
- [Medical-pills](detect/medical-pills.md): A dataset containing labeled images of medical pills, designed to aid in tasks like pharmaceutical quality control, sorting, and ensuring compliance with industry standards.
|
||||
- [Objects365](detect/objects365.md): A high-quality, large-scale dataset for object detection with 365 object categories and over 600K annotated images.
|
||||
- [OpenImagesV7](detect/open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
|
||||
- [RF100](detect/roboflow-100.md): A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
|
||||
- [Signature](detect/signature.md): A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.
|
||||
- [SKU-110K](detect/sku-110k.md): A dataset featuring dense object detection in retail environments with over 11K images and 1.7 million bounding boxes.
|
||||
- [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
|
||||
- [VOC](detect/voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
|
||||
- [xView](detect/xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
|
||||
|
||||
## [Instance Segmentation](segment/index.md)
|
||||
|
||||
Instance segmentation is a computer vision technique that involves identifying and localizing objects in an image at the pixel level. Unlike semantic segmentation which only classifies each pixel, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) distinguishes between different instances of the same class.
|
||||
|
||||
- [Carparts-seg](segment/carparts-seg.md): Purpose-built dataset for identifying vehicle parts, catering to design, manufacturing, and research needs. It serves for both object detection and segmentation tasks.
|
||||
- [COCO](segment/coco.md): A large-scale dataset designed for object detection, segmentation, and captioning tasks with over 200K labeled images.
|
||||
- [COCO8-seg](segment/coco8-seg.md): A smaller dataset for instance segmentation tasks, containing a subset of 8 COCO images with segmentation annotations.
|
||||
- [COCO128-seg](segment/coco128-seg.md): A smaller dataset for instance segmentation tasks, containing a subset of 128 COCO images with segmentation annotations.
|
||||
- [Crack-seg](segment/crack-seg.md): Specifically crafted dataset for detecting cracks on roads and walls, applicable for both object detection and segmentation tasks.
|
||||
- [Package-seg](segment/package-seg.md): Tailored dataset for identifying packages in warehouses or industrial settings, suitable for both object detection and segmentation applications.
|
||||
|
||||
## [Pose Estimation](pose/index.md)
|
||||
|
||||
Pose estimation is a technique used to determine the pose of the object relative to the camera or the world coordinate system. This involves identifying key points or joints on objects, particularly humans or animals.
|
||||
|
||||
- [COCO](pose/coco.md): A large-scale dataset with human pose annotations designed for pose estimation tasks.
|
||||
- [COCO8-pose](pose/coco8-pose.md): A smaller dataset for pose estimation tasks, containing a subset of 8 COCO images with human pose annotations.
|
||||
- [Dog-pose](pose/dog-pose.md): A comprehensive dataset featuring approximately 6,000 images focused on dogs, annotated with 24 keypoints per dog, tailored for pose estimation tasks.
|
||||
- [Hand-Keypoints](pose/hand-keypoints.md): A concise dataset featuring over 26,000 images centered on human hands, annotated with 21 keypoints per hand, designed for pose estimation tasks.
|
||||
- [Tiger-pose](pose/tiger-pose.md): A compact dataset consisting of 263 images focused on tigers, annotated with 12 keypoints per tiger for pose estimation tasks.
|
||||
|
||||
## [Classification](classify/index.md)
|
||||
|
||||
[Image classification](https://www.ultralytics.com/glossary/image-classification) is a computer vision task that involves categorizing an image into one or more predefined classes or categories based on its visual content.
|
||||
|
||||
- [Caltech 101](classify/caltech101.md): A dataset containing images of 101 object categories for image classification tasks.
|
||||
- [Caltech 256](classify/caltech256.md): An extended version of Caltech 101 with 256 object categories and more challenging images.
|
||||
- [CIFAR-10](classify/cifar10.md): A dataset of 60K 32x32 color images in 10 classes, with 6K images per class.
|
||||
- [CIFAR-100](classify/cifar100.md): An extended version of CIFAR-10 with 100 object categories and 600 images per class.
|
||||
- [Fashion-MNIST](classify/fashion-mnist.md): A dataset consisting of 70,000 grayscale images of 10 fashion categories for image classification tasks.
|
||||
- [ImageNet](classify/imagenet.md): A large-scale dataset for object detection and image classification with over 14 million images and 20,000 categories.
|
||||
- [ImageNet-10](classify/imagenet10.md): A smaller subset of ImageNet with 10 categories for faster experimentation and testing.
|
||||
- [Imagenette](classify/imagenette.md): A smaller subset of ImageNet that contains 10 easily distinguishable classes for quicker training and testing.
|
||||
- [Imagewoof](classify/imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
|
||||
- [MNIST](classify/mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
|
||||
- [MNIST160](classify/mnist.md): First 8 images of each MNIST category from the MNIST dataset. Dataset contains 160 images total.
|
||||
|
||||
## [Oriented Bounding Boxes (OBB)](obb/index.md)
|
||||
|
||||
Oriented Bounding Boxes (OBB) is a method in computer vision for detecting angled objects in images using rotated bounding boxes, often applied to aerial and satellite imagery. Unlike traditional bounding boxes, OBB can better fit objects at various orientations.
|
||||
|
||||
- [DOTA-v2](obb/dota-v2.md): A popular OBB aerial imagery dataset with 1.7 million instances and 11,268 images.
|
||||
- [DOTA8](obb/dota8.md): A smaller subset of the first 8 images from the DOTAv1 split set, 4 for training and 4 for validation, suitable for quick tests.
|
||||
|
||||
## [Multi-Object Tracking](track/index.md)
|
||||
|
||||
Multi-object tracking is a computer vision technique that involves detecting and tracking multiple objects over time in a video sequence. This task extends object detection by maintaining consistent identities of objects across frames.
|
||||
|
||||
- [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations for multi-object tracking tasks.
|
||||
- [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
|
||||
|
||||
## Contribute New Datasets
|
||||
|
||||
Contributing a new dataset involves several steps to ensure that it aligns well with the existing infrastructure. Below are the necessary steps:
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/yMR7BgwHQ3g?start=427"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Contribute to Ultralytics Datasets
|
||||
</p>
|
||||
|
||||
### Steps to Contribute a New Dataset
|
||||
|
||||
1. **Collect Images**: Gather the images that belong to the dataset. These could be collected from various sources, such as public databases or your own collection.
|
||||
2. **Annotate Images**: Annotate these images with bounding boxes, segments, or keypoints, depending on the task.
|
||||
3. **Export Annotations**: Convert these annotations into the YOLO `*.txt` file format which Ultralytics supports.
|
||||
4. **Organize Dataset**: Arrange your dataset into the correct folder structure. You should have `images/` and `labels/` top-level directories, and within each, a `train/` and `val/` subdirectory.
|
||||
|
||||
```
|
||||
dataset/
|
||||
├── images/
|
||||
│ ├── train/
|
||||
│ └── val/
|
||||
└── labels/
|
||||
├── train/
|
||||
└── val/
|
||||
```
|
||||
|
||||
5. **Create a `data.yaml` File**: In your dataset's root directory, create a `data.yaml` file that describes the dataset, classes, and other necessary information.
|
||||
6. **Optimize Images (Optional)**: If you want to reduce the size of the dataset for more efficient processing, you can optimize the images using the code below. This is not required, but recommended for smaller dataset sizes and faster download speeds.
|
||||
7. **Zip Dataset**: Compress the entire dataset folder into a zip file.
|
||||
8. **Document and PR**: Create a documentation page describing your dataset and how it fits into the existing framework. After that, submit a Pull Request (PR). Refer to [Ultralytics Contribution Guidelines](https://docs.ultralytics.com/help/contributing/) for more details on how to submit a PR.
|
||||
|
||||
### Example Code to Optimize and Zip a Dataset
|
||||
|
||||
!!! example "Optimize and Zip a Dataset"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.data.utils import compress_one_image
|
||||
from ultralytics.utils.downloads import zip_directory
|
||||
|
||||
# Define dataset directory
|
||||
path = Path("path/to/dataset")
|
||||
|
||||
# Optimize images in dataset (optional)
|
||||
for f in path.rglob("*.jpg"):
|
||||
compress_one_image(f)
|
||||
|
||||
# Zip dataset into 'path/to/dataset.zip'
|
||||
zip_directory(path)
|
||||
```
|
||||
|
||||
By following these steps, you can contribute a new dataset that integrates well with Ultralytics' existing structure.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What datasets does Ultralytics support for object detection?
|
||||
|
||||
Ultralytics supports a wide variety of datasets for [object detection](https://www.ultralytics.com/glossary/object-detection), including:
|
||||
|
||||
- [COCO](detect/coco.md): A large-scale object detection, segmentation, and captioning dataset with 80 object categories.
|
||||
- [LVIS](detect/lvis.md): An extensive dataset with 1203 object categories, designed for more fine-grained object detection and segmentation.
|
||||
- [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
|
||||
- [VisDrone](detect/visdrone.md): A dataset with object detection and multi-object tracking data from drone-captured imagery.
|
||||
- [SKU-110K](detect/sku-110k.md): Featuring dense object detection in retail environments with over 11K images.
|
||||
|
||||
These datasets facilitate training robust [Ultralytics YOLO](https://docs.ultralytics.com/models/) models for various object detection applications.
|
||||
|
||||
### How do I contribute a new dataset to Ultralytics?
|
||||
|
||||
Contributing a new dataset involves several steps:
|
||||
|
||||
1. **Collect Images**: Gather images from public databases or personal collections.
|
||||
2. **Annotate Images**: Apply bounding boxes, segments, or keypoints, depending on the task.
|
||||
3. **Export Annotations**: Convert annotations into the YOLO `*.txt` format.
|
||||
4. **Organize Dataset**: Use the folder structure with `train/` and `val/` directories, each containing `images/` and `labels/` subdirectories.
|
||||
5. **Create a `data.yaml` File**: Include dataset descriptions, classes, and other relevant information.
|
||||
6. **Optimize Images (Optional)**: Reduce dataset size for efficiency.
|
||||
7. **Zip Dataset**: Compress the dataset into a zip file.
|
||||
8. **Document and PR**: Describe your dataset and submit a Pull Request following [Ultralytics Contribution Guidelines](https://docs.ultralytics.com/help/contributing/).
|
||||
|
||||
Visit [Contribute New Datasets](#contribute-new-datasets) for a comprehensive guide.
|
||||
|
||||
### Why should I use Ultralytics Platform for my dataset?
|
||||
|
||||
[Ultralytics Platform](https://platform.ultralytics.com/) offers powerful features for dataset management and analysis, including:
|
||||
|
||||
- **Seamless Dataset Management**: Upload, organize, and manage your datasets in one place.
|
||||
- **Immediate Training Integration**: Use uploaded datasets directly for model training without additional setup.
|
||||
- **Visualization Tools**: Explore and visualize your dataset images and annotations.
|
||||
- **Dataset Analysis**: Get insights into your dataset distribution and characteristics.
|
||||
|
||||
The platform streamlines the transition from dataset management to model training, making the entire process more efficient. Learn more about [Ultralytics Platform Datasets](https://docs.ultralytics.com/platform/data/).
|
||||
|
||||
### What are the unique features of Ultralytics YOLO models for computer vision?
|
||||
|
||||
Ultralytics YOLO models provide several unique features for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks:
|
||||
|
||||
- **Real-time Performance**: High-speed inference and training capabilities for time-sensitive applications.
|
||||
- **Versatility**: Support for detection, segmentation, classification, and pose estimation tasks in a unified framework.
|
||||
- **Pretrained Models**: Access to high-performing, pretrained models for various applications, reducing training time.
|
||||
- **Extensive Community Support**: Active community and comprehensive documentation for troubleshooting and development.
|
||||
- **Easy Integration**: Simple API for integrating with existing projects and workflows.
|
||||
|
||||
Discover more about YOLO models on the [Ultralytics Models](https://docs.ultralytics.com/models/) page.
|
||||
|
||||
### How can I optimize and zip a dataset using Ultralytics tools?
|
||||
|
||||
To optimize and zip a dataset using Ultralytics tools, follow this example code:
|
||||
|
||||
!!! example "Optimize and Zip a Dataset"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.data.utils import compress_one_image
|
||||
from ultralytics.utils.downloads import zip_directory
|
||||
|
||||
# Define dataset directory
|
||||
path = Path("path/to/dataset")
|
||||
|
||||
# Optimize images in dataset (optional)
|
||||
for f in path.rglob("*.jpg"):
|
||||
compress_one_image(f)
|
||||
|
||||
# Zip dataset into 'path/to/dataset.zip'
|
||||
zip_directory(path)
|
||||
```
|
||||
|
||||
This process helps reduce dataset size for more efficient storage and faster download speeds. Learn more on how to [Optimize and Zip a Dataset](#example-code-to-optimize-and-zip-a-dataset).
|
||||
250
algorithms/dms_yolo/code/docs/en/datasets/obb/dota-v2.md
Normal file
250
algorithms/dms_yolo/code/docs/en/datasets/obb/dota-v2.md
Normal file
@@ -0,0 +1,250 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the DOTA dataset for object detection in aerial images, featuring 1.7M Oriented Bounding Boxes across 18 categories. Ideal for aerial image analysis.
|
||||
keywords: DOTA dataset, object detection, aerial images, oriented bounding boxes, OBB, DOTA v1.0, DOTA v1.5, DOTA v2.0, multiscale detection, Ultralytics
|
||||
---
|
||||
|
||||
# DOTA Dataset with OBB
|
||||
|
||||
[DOTA](https://captain-whu.github.io/DOTA/index.html) stands as a specialized dataset, emphasizing [object detection](https://www.ultralytics.com/glossary/object-detection) in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with [Oriented Bounding Boxes (OBB)](https://docs.ultralytics.com/datasets/obb/).
|
||||
|
||||
|
||||
|
||||
## Key Features
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/JjQ-URE0LJE"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the DOTA Dataset for Oriented Bounding Boxes in Google Colab
|
||||
</p>
|
||||
|
||||
- Collection from various sensors and platforms, with image sizes ranging from 800 × 800 to 20,000 × 20,000 pixels.
|
||||
- Features more than 1.7M oriented bounding boxes across 18 categories.
|
||||
- Encompasses multiscale object detection thanks to the wide spread of object sizes per image.
|
||||
- Instances are annotated by experts using arbitrary (8 d.o.f.) quadrilaterals, capturing objects of different scales, orientations, and shapes.
|
||||
|
||||
## Dataset Versions
|
||||
|
||||
### DOTA-v1.0
|
||||
|
||||
- Contains 15 common categories.
|
||||
- Comprises 2,806 images with 188,282 instances.
|
||||
- Split ratios: 1/2 for training, 1/6 for validation, and 1/3 for testing.
|
||||
|
||||
### DOTA-v1.5
|
||||
|
||||
- Incorporates the same images as DOTA-v1.0.
|
||||
- Very small instances (less than 10 pixels) are also annotated.
|
||||
- Addition of a new category: "container crane".
|
||||
- A total of 403,318 instances.
|
||||
- Released for the [DOAI Challenge 2019 on Object Detection in Aerial Images](https://captain-whu.github.io/DOAI2019/challenge.html).
|
||||
|
||||
### DOTA-v2.0
|
||||
|
||||
- Collections from Google Earth, GF-2 Satellite, and other aerial images.
|
||||
- Contains 18 common categories.
|
||||
- Comprises 11,268 images with a whopping 1,793,658 instances.
|
||||
- New categories introduced: "airport" and "helipad".
|
||||
- Image splits:
|
||||
- Training: 1,830 images with 268,627 instances.
|
||||
- Validation: 593 images with 81,048 instances.
|
||||
- Test-dev: 2,792 images with 353,346 instances.
|
||||
- Test-challenge: 6,053 images with 1,090,637 instances.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
DOTA exhibits a structured layout tailored for OBB object detection challenges:
|
||||
|
||||
- **Images**: A vast collection of high-resolution aerial images capturing diverse terrains and structures.
|
||||
- **Oriented Bounding Boxes**: Annotations in the form of rotated rectangles encapsulating objects irrespective of their orientation, ideal for capturing objects like airplanes, ships, and buildings.
|
||||
|
||||
## Applications
|
||||
|
||||
DOTA serves as a benchmark for training and evaluating models specifically tailored for aerial image analysis. With the inclusion of OBB annotations, it provides a unique challenge, enabling the development of specialized [object detection](https://docs.ultralytics.com/tasks/detect/) models that cater to aerial imagery's nuances. The dataset is particularly valuable for applications in remote sensing, surveillance, and environmental monitoring.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A dataset YAML (Yet Another Markup Language) file specifies image/label roots, class names, and other important metadata. Ultralytics maintains official YAML files for the two most commonly used releases:
|
||||
|
||||
- [`DOTAv1.yaml`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml)
|
||||
- [`DOTAv1.5.yaml`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.5.yaml)
|
||||
|
||||
Use the YAML that matches the release you downloaded, or author a custom YAML if you are working with DOTA-v2 or another derivative.
|
||||
|
||||
!!! example "DOTAv1.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/DOTAv1.yaml"
|
||||
```
|
||||
|
||||
## Split DOTA images
|
||||
|
||||
The raw imagery routinely exceeds 10,000 pixels on a side, so tiling is required before feeding the data to YOLO. Use the helper below to slice the source imagery into overlapping 1024 × 1024 crops at multiple scales while keeping the annotations in sync.
|
||||
|
||||
!!! example "Split images"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics.data.split_dota import split_test, split_trainval
|
||||
|
||||
# Split train and val set, with labels.
|
||||
split_trainval(
|
||||
data_root="path/to/DOTAv1.0/",
|
||||
save_dir="path/to/DOTAv1.0-split/",
|
||||
rates=[0.5, 1.0, 1.5], # multiscale
|
||||
gap=500,
|
||||
)
|
||||
# Split test set, without labels.
|
||||
split_test(
|
||||
data_root="path/to/DOTAv1.0/",
|
||||
save_dir="path/to/DOTAv1.0-split/",
|
||||
rates=[0.5, 1.0, 1.5], # multiscale
|
||||
gap=500,
|
||||
)
|
||||
```
|
||||
|
||||
!!! tip
|
||||
|
||||
Keep the output directory organized in the standard YOLO layout (`images/train`, `labels/train`, etc.) so you can reference it directly from the dataset YAML.
|
||||
|
||||
## Usage
|
||||
|
||||
To train a model on the DOTA v1 dataset, you can utilize the following code snippets. Always refer to your model's documentation for a thorough list of available arguments. For those looking to experiment with a smaller subset first, consider using the [DOTA8 dataset](https://docs.ultralytics.com/datasets/obb/dota8/), which contains just 8 images for quick testing.
|
||||
|
||||
!!! warning
|
||||
|
||||
Please note that all images and associated annotations in the DOTAv1 dataset can be used for academic purposes, but commercial use is prohibited. Your understanding and respect for the dataset creators' wishes are greatly appreciated!
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the DOTAv1 dataset
|
||||
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO26n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=100 imgsz=1024
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
Having a glance at the dataset illustrates its depth:
|
||||
|
||||
|
||||
|
||||
- **DOTA examples**: This snapshot underlines the complexity of aerial scenes and the significance of Oriented [Bounding Box](https://www.ultralytics.com/glossary/bounding-box) annotations, capturing objects in their natural orientation.
|
||||
|
||||
The dataset's richness offers invaluable insights into object detection challenges exclusive to aerial imagery. The [DOTA-v2.0 dataset](https://www.ultralytics.com/blog/exploring-the-best-computer-vision-datasets-in-2025) has become particularly popular for remote sensing and aerial surveillance projects due to its comprehensive annotations and diverse object categories.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use DOTA in your work, please cite the relevant research papers:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{9560031,
|
||||
author={Ding, Jian and Xue, Nan and Xia, Gui-Song and Bai, Xiang and Yang, Wen and Yang, Michael and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges},
|
||||
year={2021},
|
||||
volume={},
|
||||
number={},
|
||||
pages={1-1},
|
||||
doi={10.1109/TPAMI.2021.3117983}
|
||||
}
|
||||
```
|
||||
|
||||
A special note of gratitude to the team behind the DOTA datasets for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the [official DOTA website](https://captain-whu.github.io/DOTA/index.html).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the DOTA dataset and why is it important for object detection in aerial images?
|
||||
|
||||
The [DOTA dataset](https://captain-whu.github.io/DOTA/index.html) is a specialized dataset focused on object detection in aerial images. It features Oriented Bounding Boxes (OBB), providing annotated images from diverse aerial scenes. DOTA's diversity in object orientation, scale, and shape across its 1.7M annotations and 18 categories makes it ideal for developing and evaluating models tailored for aerial imagery analysis, such as those used in surveillance, environmental monitoring, and disaster management.
|
||||
|
||||
### How does the DOTA dataset handle different scales and orientations in images?
|
||||
|
||||
DOTA utilizes Oriented Bounding Boxes (OBB) for annotation, which are represented by rotated rectangles encapsulating objects regardless of their orientation. This method ensures that objects, whether small or at different angles, are accurately captured. The dataset's multiscale images, ranging from 800 × 800 to 20,000 × 20,000 pixels, further allow for the detection of both small and large objects effectively. This approach is particularly valuable for aerial imagery where objects appear at various angles and scales.
|
||||
|
||||
### How can I train a model using the DOTA dataset?
|
||||
|
||||
To train a model on the DOTA dataset, you can use the following example with [Ultralytics YOLO](https://docs.ultralytics.com/tasks/obb/):
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the DOTAv1 dataset
|
||||
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO26n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=100 imgsz=1024
|
||||
```
|
||||
|
||||
For more details on how to split and preprocess the DOTA images, refer to the [split DOTA images section](#split-dota-images).
|
||||
|
||||
### What are the differences between DOTA-v1.0, DOTA-v1.5, and DOTA-v2.0?
|
||||
|
||||
- **DOTA-v1.0**: Includes 15 common categories across 2,806 images with 188,282 instances. The dataset is split into training, validation, and testing sets.
|
||||
- **DOTA-v1.5**: Builds upon DOTA-v1.0 by annotating very small instances (less than 10 pixels) and adding a new category, "container crane," totaling 403,318 instances.
|
||||
- **DOTA-v2.0**: Expands further with annotations from Google Earth and GF-2 Satellite, featuring 11,268 images and 1,793,658 instances. It includes new categories like "airport" and "helipad."
|
||||
|
||||
For a detailed comparison and additional specifics, check the [dataset versions section](#dataset-versions).
|
||||
|
||||
### How can I prepare high-resolution DOTA images for training?
|
||||
|
||||
DOTA images, which can be very large, are split into smaller resolutions for manageable training. Here's a Python snippet to split images:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics.data.split_dota import split_test, split_trainval
|
||||
|
||||
# split train and val set, with labels.
|
||||
split_trainval(
|
||||
data_root="path/to/DOTAv1.0/",
|
||||
save_dir="path/to/DOTAv1.0-split/",
|
||||
rates=[0.5, 1.0, 1.5], # multiscale
|
||||
gap=500,
|
||||
)
|
||||
# split test set, without labels.
|
||||
split_test(
|
||||
data_root="path/to/DOTAv1.0/",
|
||||
save_dir="path/to/DOTAv1.0-split/",
|
||||
rates=[0.5, 1.0, 1.5], # multiscale
|
||||
gap=500,
|
||||
)
|
||||
```
|
||||
|
||||
This process facilitates better training efficiency and model performance. For detailed instructions, visit the [split DOTA images section](#split-dota-images).
|
||||
141
algorithms/dms_yolo/code/docs/en/datasets/obb/dota8.md
Normal file
141
algorithms/dms_yolo/code/docs/en/datasets/obb/dota8.md
Normal file
@@ -0,0 +1,141 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLO26.
|
||||
keywords: DOTA8 dataset, Ultralytics, YOLO26, object detection, debugging, training models, oriented object detection, dataset YAML
|
||||
---
|
||||
|
||||
# DOTA8 Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
[Ultralytics](https://www.ultralytics.com/) DOTA8 is a small but versatile oriented [object detection](https://www.ultralytics.com/glossary/object-detection) dataset composed of the first 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
- **Images**: 8 aerial tiles (4 train, 4 val) sourced from DOTAv1.
|
||||
- **Classes**: Inherits the 15 DOTAv1 categories such as plane, ship, and large vehicle.
|
||||
- **Labels**: YOLO-format oriented bounding boxes saved as `.txt` files beside each image.
|
||||
- **Recommended layout**:
|
||||
|
||||
```
|
||||
datasets/dota8/
|
||||
├── images/
|
||||
│ ├── train/
|
||||
│ └── val/
|
||||
└── labels/
|
||||
├── train/
|
||||
└── val/
|
||||
```
|
||||
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the DOTA8 dataset, the `dota8.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/dota8.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/dota8.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-obb.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo obb train data=dota8.yaml model=yolo26n-obb.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are some examples of images from the DOTA8 dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch.avif" alt="DOTA8 oriented bounding box dataset training mosaic" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the DOTA8 dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the DOTA dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@article{9560031,
|
||||
author={Ding, Jian and Xue, Nan and Xia, Gui-Song and Bai, Xiang and Yang, Wen and Yang, Michael and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
|
||||
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
|
||||
title={Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges},
|
||||
year={2021},
|
||||
volume={},
|
||||
number={},
|
||||
pages={1-1},
|
||||
doi={10.1109/TPAMI.2021.3117983}
|
||||
}
|
||||
```
|
||||
|
||||
A special note of gratitude to the team behind the DOTA datasets for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the [official DOTA website](https://captain-whu.github.io/DOTA/index.html).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the DOTA8 dataset and how can it be used?
|
||||
|
||||
The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLO26. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
### How do I train a YOLO26 model using the DOTA8 dataset?
|
||||
|
||||
To train a YOLO26n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-obb.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo obb train data=dota8.yaml model=yolo26n-obb.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are the key features of the DOTA dataset and where can I access the YAML file?
|
||||
|
||||
The DOTA dataset is known for its large-scale benchmark and the challenges it presents for object detection in aerial images. The DOTA8 subset is a smaller, manageable dataset ideal for initial tests. You can access the `dota8.yaml` file, which contains paths, classes, and configuration details, at this [GitHub link](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml).
|
||||
|
||||
### How does mosaicing enhance model training with the DOTA8 dataset?
|
||||
|
||||
Mosaicing combines multiple images into one during training, increasing the variety of objects and contexts within each batch. This improves a model's ability to generalize to different object sizes, aspect ratios, and scenes. This technique can be visually demonstrated through a training batch composed of mosaiced DOTA8 dataset images, helping in robust model development. Explore more about mosaicing and training techniques on our [Training](../../modes/train.md) page.
|
||||
|
||||
### Why should I use Ultralytics YOLO26 for object detection tasks?
|
||||
|
||||
Ultralytics YOLO26 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLO26 documentation](https://github.com/ultralytics/ultralytics).
|
||||
157
algorithms/dms_yolo/code/docs/en/datasets/obb/index.md
Normal file
157
algorithms/dms_yolo/code/docs/en/datasets/obb/index.md
Normal file
@@ -0,0 +1,157 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover OBB dataset formats for Ultralytics YOLO models. Learn about their structure, application, and format conversions to enhance your object detection training.
|
||||
keywords: Oriented Bounding Box, OBB Datasets, YOLO, Ultralytics, Object Detection, Dataset Formats
|
||||
---
|
||||
|
||||
# Oriented Bounding Box (OBB) Datasets Overview
|
||||
|
||||
Training a precise [object detection](https://www.ultralytics.com/glossary/object-detection) model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
|
||||
|
||||
## Supported OBB Dataset Formats
|
||||
|
||||
### YOLO OBB Format
|
||||
|
||||
The YOLO OBB format designates bounding boxes by their four corner points with coordinates normalized between 0 and 1. It follows this format:
|
||||
|
||||
```bash
|
||||
class_index x1 y1 x2 y2 x3 y3 x4 y4
|
||||
```
|
||||
|
||||
Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the [bounding box](https://www.ultralytics.com/glossary/bounding-box)'s center point (xy), width, height, and rotation.
|
||||
|
||||
<p align="center"><img width="800" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/obb-format-examples.avif" alt="Oriented bounding box annotation format examples"></p>
|
||||
|
||||
An example of a `*.txt` label file for the above image, which contains an object of class `0` in OBB format, could look like:
|
||||
|
||||
```bash
|
||||
0 0.780811 0.743961 0.782371 0.74686 0.777691 0.752174 0.776131 0.749758
|
||||
```
|
||||
|
||||
### Dataset YAML format
|
||||
|
||||
The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training OBB models. Here is an example of the YAML format used for defining an OBB dataset:
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/dota8.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/dota8.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a model using these OBB formats:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the DOTAv1 dataset
|
||||
results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=1024)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO26n-OBB model on the DOTAv1 dataset
|
||||
yolo obb train data=DOTAv1.yaml model=yolo26n-obb.pt epochs=100 imgsz=1024
|
||||
```
|
||||
|
||||
## Supported Datasets
|
||||
|
||||
Currently, the following datasets with oriented bounding boxes are supported:
|
||||
|
||||
- [DOTA-v1](dota-v2.md#dota-v10): The first version of the DOTA dataset, providing a comprehensive set of aerial images with oriented bounding boxes for object detection.
|
||||
- [DOTA-v1.5](dota-v2.md#dota-v15): An intermediate version of the DOTA dataset, offering additional annotations and improvements over DOTA-v1 for enhanced object detection tasks.
|
||||
- [DOTA-v2](dota-v2.md#dota-v20): DOTA (A Large-scale Dataset for Object Detection in Aerial Images) version 2, emphasizes detection from aerial perspectives and contains oriented bounding boxes with 1.7 million instances and 11,268 images.
|
||||
- [DOTA8](dota8.md): A small, 8-image subset of the full DOTA dataset suitable for testing workflows and Continuous Integration (CI) checks of OBB training in the `ultralytics` repository.
|
||||
|
||||
### Incorporating your own OBB dataset
|
||||
|
||||
For those looking to introduce their own datasets with oriented bounding boxes, ensure compatibility with the "YOLO OBB format" mentioned above. Convert your annotations to this required format and detail the paths, classes, and class names in a corresponding YAML configuration file.
|
||||
|
||||
## Convert Label Formats
|
||||
|
||||
### DOTA Dataset Format to YOLO OBB Format
|
||||
|
||||
Transitioning labels from the DOTA dataset format to the YOLO OBB format can be achieved with this script:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_dota_to_yolo_obb
|
||||
|
||||
convert_dota_to_yolo_obb("path/to/DOTA")
|
||||
```
|
||||
|
||||
This conversion mechanism is instrumental for datasets in the DOTA format, ensuring alignment with the [Ultralytics YOLO](../../models/yolo26.md) OBB format.
|
||||
|
||||
It's imperative to validate the compatibility of the dataset with your model and adhere to the necessary format conventions. Properly structured datasets are pivotal for training efficient object detection models with oriented bounding boxes.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What are Oriented Bounding Boxes (OBB) and how are they used in Ultralytics YOLO models?
|
||||
|
||||
Oriented Bounding Boxes (OBB) are a type of bounding box annotation where the box can be rotated to align more closely with the object being detected, rather than just being axis-aligned. This is particularly useful in aerial or satellite imagery where objects might not be aligned with the image axes. In [Ultralytics YOLO](../../tasks/obb.md) models, OBBs are represented by their four corner points in the YOLO OBB format. This allows for more accurate object detection since the bounding boxes can rotate to fit the objects better.
|
||||
|
||||
### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLO26?
|
||||
|
||||
You can convert DOTA dataset labels to YOLO OBB format using the [`convert_dota_to_yolo_obb`](../../reference/data/converter.md) function from Ultralytics. This conversion ensures compatibility with the Ultralytics YOLO models, enabling you to leverage the OBB capabilities for enhanced object detection. Here's a quick example:
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_dota_to_yolo_obb
|
||||
|
||||
convert_dota_to_yolo_obb("path/to/DOTA")
|
||||
```
|
||||
|
||||
This script will reformat your DOTA annotations into a YOLO-compatible format.
|
||||
|
||||
### How do I train a YOLO26 model with oriented bounding boxes (OBB) on my dataset?
|
||||
|
||||
Training a YOLO26 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the [Ultralytics API](../../usage/python.md) to train the model. Here's an example in both Python and CLI:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Create a new YOLO26n-OBB model from scratch
|
||||
model = YOLO("yolo26n-obb.yaml")
|
||||
|
||||
# Train the model on the custom dataset
|
||||
results = model.train(data="your_dataset.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Train a new YOLO26n-OBB model on the custom dataset
|
||||
yolo obb train data=your_dataset.yaml model=yolo26n-obb.yaml epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
This ensures your model leverages the detailed OBB annotations for improved detection [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
|
||||
### What datasets are currently supported for OBB training in Ultralytics YOLO models?
|
||||
|
||||
Currently, Ultralytics supports the following datasets for OBB training:
|
||||
|
||||
- [DOTA-v1](dota-v2.md): The first version of the DOTA dataset, providing a comprehensive set of aerial images with oriented bounding boxes for object detection.
|
||||
- [DOTA-v1.5](dota-v2.md): An intermediate version of the DOTA dataset, offering additional annotations and improvements over DOTA-v1 for enhanced object detection tasks.
|
||||
- [DOTA-v2](dota-v2.md): This dataset includes 1.7 million instances with oriented bounding boxes and 11,268 images, primarily focusing on aerial object detection.
|
||||
- [DOTA8](dota8.md): A smaller, 8-image subset of the DOTA dataset used for testing and [continuous integration](../../help/CI.md) (CI) checks.
|
||||
|
||||
These datasets are tailored for scenarios where OBBs offer a significant advantage, such as aerial and satellite image analysis.
|
||||
|
||||
### Can I use my own dataset with oriented bounding boxes for YOLO26 training, and if so, how?
|
||||
|
||||
Yes, you can use your own dataset with oriented bounding boxes for YOLO26 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a [YAML configuration file](../../usage/cfg.md) specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.
|
||||
152
algorithms/dms_yolo/code/docs/en/datasets/pose/coco.md
Normal file
152
algorithms/dms_yolo/code/docs/en/datasets/pose/coco.md
Normal file
@@ -0,0 +1,152 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the COCO-Pose dataset for advanced pose estimation. Learn about datasets, pretrained models, metrics, and applications for training with YOLO.
|
||||
keywords: COCO-Pose, pose estimation, dataset, keypoints, COCO Keypoints 2017, YOLO, deep learning, computer vision
|
||||
---
|
||||
|
||||
# COCO-Pose Dataset
|
||||
|
||||
The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset, designed for pose estimation tasks. It leverages the COCO Keypoints 2017 images and labels to enable the training of models like YOLO for pose estimation tasks.
|
||||
|
||||
|
||||
|
||||
## COCO-Pose Pretrained Models
|
||||
|
||||
{% include "macros/yolo-pose-perf.md" %}
|
||||
|
||||
## Key Features
|
||||
|
||||
- COCO-Pose builds upon the COCO Keypoints 2017 dataset which contains 200K images labeled with keypoints for pose estimation tasks.
|
||||
- The dataset supports 17 keypoints for human figures, facilitating detailed pose estimation.
|
||||
- Like COCO, it provides standardized evaluation metrics, including Object Keypoint Similarity (OKS) for pose estimation tasks, making it suitable for comparing model performance.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The COCO-Pose dataset is split into three subsets:
|
||||
|
||||
1. **Train2017**: This subset contains 56599 images from the COCO dataset, annotated for training pose estimation models.
|
||||
2. **Val2017**: This subset has 2346 images used for validation purposes during model training.
|
||||
3. **Test2017**: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
|
||||
|
||||
## Applications
|
||||
|
||||
The COCO-Pose dataset is specifically used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in keypoint detection and pose estimation tasks, such as OpenPose. The dataset's large number of annotated images and standardized evaluation metrics make it an essential resource for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) researchers and practitioners focused on pose estimation.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Pose dataset, the `coco-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco-pose.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco-pose.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-pose model on the COCO-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The COCO-Pose dataset contains a diverse set of images with human figures annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the COCO-Pose dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO-Pose dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?
|
||||
|
||||
The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO26n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### How can I train a YOLO26 model on the COCO-Pose dataset?
|
||||
|
||||
Training a YOLO26 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO26n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For more details on the training process and available arguments, check the [training page](../../modes/train.md).
|
||||
|
||||
### What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
|
||||
|
||||
The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the [accuracy](https://www.ultralytics.com/glossary/accuracy) of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO26n-pose, YOLO26s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
|
||||
|
||||
### How is the dataset structured and split for the COCO-Pose dataset?
|
||||
|
||||
The COCO-Pose dataset is split into three subsets:
|
||||
|
||||
1. **Train2017**: Contains 56599 COCO images, annotated for training pose estimation models.
|
||||
2. **Val2017**: 2346 images for validation purposes during model training.
|
||||
3. **Test2017**: Images used for testing and benchmarking trained models. Ground truth annotations for this subset are not publicly available; results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7403) for performance evaluation.
|
||||
|
||||
These subsets help organize the training, validation, and testing phases effectively. For configuration details, explore the `coco-pose.yaml` file available on [GitHub](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml).
|
||||
|
||||
### What are the key features and applications of the COCO-Pose dataset?
|
||||
|
||||
The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO26n-pose) can significantly streamline the process ([Key Features](#key-features)).
|
||||
|
||||
If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following [BibTeX entry](#citations-and-acknowledgments).
|
||||
137
algorithms/dms_yolo/code/docs/en/datasets/pose/coco8-pose.md
Normal file
137
algorithms/dms_yolo/code/docs/en/datasets/pose/coco8-pose.md
Normal file
@@ -0,0 +1,137 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLO26.
|
||||
keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLO26, machine learning, computer vision, training data
|
||||
---
|
||||
|
||||
# COCO8-Pose Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
[Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging [object detection](https://www.ultralytics.com/glossary/object-detection) models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
- **Total images**: 8 (4 train / 4 val).
|
||||
- **Classes**: 1 (person) with 17 keypoints per annotation.
|
||||
- **Recommended directory layout**: `datasets/coco8-pose/images/{train,val}` and `datasets/coco8-pose/labels/{train,val}` with YOLO-format keypoints stored as `.txt` files.
|
||||
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Pose dataset, the `coco8-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8-pose.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-pose.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-pose model on the COCO8-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco8-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are some examples of images from the COCO8-Pose dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-5.avif" alt="COCO8-pose keypoint estimation dataset mosaic" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the COCO8-Pose dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLO26?
|
||||
|
||||
The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLO26](../../models/yolo26.md). For more details on dataset configuration, check out the [dataset YAML file](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
|
||||
|
||||
### How do I train a YOLO26 model using the COCO8-Pose dataset in Ultralytics?
|
||||
|
||||
To train a YOLO26n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo pose train data=coco8-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the benefits of using the COCO8-Pose dataset?
|
||||
|
||||
The COCO8-Pose dataset offers several benefits:
|
||||
|
||||
- **Compact Size**: With only 8 images, it is easy to manage and perfect for quick experiments.
|
||||
- **Diverse Data**: Despite its small size, it includes a variety of scenes, useful for thorough pipeline testing.
|
||||
- **Error Debugging**: Ideal for identifying training errors and performing sanity checks before scaling up to larger datasets.
|
||||
|
||||
For more about its features and usage, see the [Dataset Introduction](#introduction) section.
|
||||
|
||||
### How does mosaicing benefit the YOLO26 training process using the COCO8-Pose dataset?
|
||||
|
||||
Mosaicing, demonstrated in the sample images of the COCO8-Pose dataset, combines multiple images into one, increasing the variety of objects and scenes within each training batch. This technique helps improve the model's ability to generalize across various object sizes, aspect ratios, and contexts, ultimately enhancing model performance. See the [Sample Images and Annotations](#sample-images-and-annotations) section for example images.
|
||||
|
||||
### Where can I find the COCO8-Pose dataset YAML file and how do I use it?
|
||||
|
||||
The COCO8-Pose dataset YAML file can be found at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml>. This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLO26 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolo26-model-using-the-coco8-pose-dataset-in-ultralytics) section.
|
||||
|
||||
For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).
|
||||
175
algorithms/dms_yolo/code/docs/en/datasets/pose/dog-pose.md
Normal file
175
algorithms/dms_yolo/code/docs/en/datasets/pose/dog-pose.md
Normal file
@@ -0,0 +1,175 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover the Dog-Pose dataset for pose detection. Featuring 6,773 training and 1,703 test images, it is a robust dataset for training YOLO26 models.
|
||||
keywords: Dog-Pose, Ultralytics, pose detection dataset, YOLO26, machine learning, computer vision, training data
|
||||
---
|
||||
|
||||
# Dog-Pose Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The [Ultralytics](https://www.ultralytics.com/) Dog-Pose dataset is a high-quality and extensive dataset specifically curated for dog keypoint estimation. With 6,773 training images and 1,703 test images, this dataset provides a solid foundation for training robust pose estimation models.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ZhjO32tZUek"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the Stanford Dog Pose Estimation Dataset | Step-by-Step Tutorial
|
||||
</p>
|
||||
|
||||
Each annotated image includes 24 keypoints with 3 dimensions per keypoint (x, y, visibility), making it a valuable resource for advanced research and development in computer vision.
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/ultralytics-dogs.avif" alt="Ultralytics Dog-pose display image" width="800">
|
||||
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
- **Split**: 6,773 train / 1,703 test images with matching YOLO-format label files.
|
||||
- **Keypoints**: 24 per dog with `(x, y, visibility)` triplets.
|
||||
- **Layout**:
|
||||
|
||||
```
|
||||
datasets/dog-pose/
|
||||
├── images/{train,test}
|
||||
└── labels/{train,test}
|
||||
```
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It includes paths, keypoint details, and other relevant information. In the case of the Dog-pose dataset, The `dog-pose.yaml` is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dog-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dog-pose.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/dog-pose.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/dog-pose.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-pose model on the Dog-pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dog-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=dog-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are some examples of images from the Dog-pose dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-2-dog-pose.avif" alt="Dog pose estimation dataset mosaic training batch" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the Dog-pose dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the Dog-pose dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@inproceedings{khosla2011fgvc,
|
||||
title={Novel dataset for Fine-Grained Image Categorization},
|
||||
author={Aditya Khosla and Nityananda Jayadevaprakash and Bangpeng Yao and Li Fei-Fei},
|
||||
booktitle={First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year={2011}
|
||||
}
|
||||
@inproceedings{deng2009imagenet,
|
||||
title={ImageNet: A Large-Scale Hierarchical Image Database},
|
||||
author={Jia Deng and Wei Dong and Richard Socher and Li-Jia Li and Kai Li and Li Fei-Fei},
|
||||
booktitle={IEEE Computer Vision and Pattern Recognition (CVPR)},
|
||||
year={2009}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the Stanford team for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the Dog-pose dataset and its creators, visit the [Stanford Dogs Dataset website](http://vision.stanford.edu/aditya86/ImageNetDogs/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Dog-pose dataset, and how is it used with Ultralytics YOLO26?
|
||||
|
||||
The Dog-Pose dataset features 6,773 training and 1,703 test images annotated with 24 keypoints for dog pose estimation. It's designed for training and validating models with [Ultralytics YOLO26](../../models/yolo26.md), supporting applications like animal behavior analysis, pet monitoring, and veterinary studies. The dataset's comprehensive annotations make it ideal for developing accurate pose estimation models for canines.
|
||||
|
||||
### How do I train a YOLO26 model using the Dog-pose dataset in Ultralytics?
|
||||
|
||||
To train a YOLO26n-pose model on the Dog-pose dataset for 100 epochs with an image size of 640, follow these examples:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="dog-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo pose train data=dog-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the benefits of using the Dog-pose dataset?
|
||||
|
||||
The Dog-pose dataset offers several benefits:
|
||||
|
||||
**Large and Diverse Dataset**: With over 8,400 images, it provides substantial data covering a wide range of dog poses, breeds, and contexts, enabling robust model training and evaluation.
|
||||
|
||||
**Detailed Keypoint Annotations**: Each image includes 24 keypoints with 3 dimensions per keypoint (x, y, visibility), offering precise annotations for training accurate pose detection models.
|
||||
|
||||
**Real-World Scenarios**: Includes images from varied environments, enhancing the model's ability to generalize to real-world applications like [pet monitoring](https://www.ultralytics.com/blog/custom-training-ultralytics-yolo11-for-dog-pose-estimation) and behavior analysis.
|
||||
|
||||
**Transfer Learning Advantage**: The dataset works well with [transfer learning](https://www.ultralytics.com/blog/understanding-few-shot-zero-shot-and-transfer-learning) techniques, allowing models pretrained on human pose datasets to adapt to dog-specific features.
|
||||
|
||||
For more about its features and usage, see the [Dataset Introduction](#introduction) section.
|
||||
|
||||
### How does mosaicing benefit the YOLO26 training process using the Dog-pose dataset?
|
||||
|
||||
Mosaicing, as illustrated in the sample images from the Dog-pose dataset, merges multiple images into a single composite, enriching the diversity of objects and scenes in each training batch. This technique offers several benefits:
|
||||
|
||||
- Increases the variety of dog poses, sizes, and backgrounds in each batch
|
||||
- Improves the model's ability to detect dogs in different contexts and scales
|
||||
- Enhances generalization by exposing the model to more diverse visual patterns
|
||||
- Reduces overfitting by creating novel combinations of training examples
|
||||
|
||||
This approach leads to more robust models that perform better in real-world scenarios. For example images, refer to the [Sample Images and Annotations](#sample-images-and-annotations) section.
|
||||
|
||||
### Where can I find the Dog-pose dataset YAML file and how do I use it?
|
||||
|
||||
The Dog-pose dataset YAML file can be found at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dog-pose.yaml>. This file defines the dataset configuration, including paths, classes, keypoint details, and other relevant information. The YAML specifies 24 keypoints with 3 dimensions per keypoint, making it suitable for detailed pose estimation tasks.
|
||||
|
||||
To use this file with YOLO26 training scripts, simply reference it in your training command as shown in the [Usage](#usage) section. The dataset will be automatically downloaded when first used, making setup straightforward.
|
||||
|
||||
For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).
|
||||
186
algorithms/dms_yolo/code/docs/en/datasets/pose/hand-keypoints.md
Normal file
186
algorithms/dms_yolo/code/docs/en/datasets/pose/hand-keypoints.md
Normal file
@@ -0,0 +1,186 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the hand keypoints estimation dataset for advanced pose estimation. Learn about datasets, pretrained models, metrics, and applications for training with YOLO.
|
||||
keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO, deep learning, computer vision
|
||||
---
|
||||
|
||||
# Hand Keypoints Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high [accuracy](https://www.ultralytics.com/glossary/accuracy) and consistency, and the dataset is compatible with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics) formats.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/fd6u1TW_AGY"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Hand Keypoints Estimation with Ultralytics YOLO26 | Human Hand Pose Estimation Tutorial
|
||||
</p>
|
||||
|
||||
## Hand Landmarks
|
||||
|
||||
|
||||
|
||||
## Keypoints
|
||||
|
||||
The dataset includes keypoints for hand detection. The keypoints are annotated as follows:
|
||||
|
||||
1. Wrist
|
||||
2. Thumb (4 points)
|
||||
3. Index finger (4 points)
|
||||
4. Middle finger (4 points)
|
||||
5. Ring finger (4 points)
|
||||
6. Little finger (4 points)
|
||||
|
||||
Each hand has a total of 21 keypoints.
|
||||
|
||||
## Key Features
|
||||
|
||||
- **Large Dataset**: 26,768 images with hand keypoint annotations.
|
||||
- **YOLO26 Compatibility**: Labels ship in YOLO keypoint format and are ready for use with YOLO26 models.
|
||||
- **21 Keypoints**: Detailed hand pose representation spanning the wrist and four points per finger.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The hand keypoint dataset is split into two subsets:
|
||||
|
||||
1. **Train**: This subset contains 18,776 images from the hand keypoints dataset, annotated for training pose estimation models.
|
||||
2. **Val**: This subset contains 7,992 images that can be used for validation purposes during model training.
|
||||
|
||||
## Applications
|
||||
|
||||
Hand keypoints can be used for [gesture recognition](https://www.ultralytics.com/blog/enhancing-hand-keypoints-estimation-with-ultralytics-yolo11), [AR/VR controls](https://docs.ultralytics.com/tasks/pose/), robotic manipulation, and hand movement analysis in healthcare. They can also be applied in animation for motion capture and biometric authentication systems for security. The detailed tracking of finger positions enables precise interaction with virtual objects and touchless control interfaces.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Hand Keypoints dataset, the `hand-keypoints.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/hand-keypoints.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/hand-keypoints.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=hand-keypoints.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
The Hand keypoints dataset contains a diverse set of images with human hands annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the Hand Keypoints dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the hand-keypoints dataset in your research or development work, please acknowledge the following sources:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "Credits"
|
||||
|
||||
We would like to thank the following sources for providing the images used in this dataset:
|
||||
|
||||
- [11k Hands](https://sites.google.com/view/11khands)
|
||||
- [2000 Hand Gestures](https://www.kaggle.com/datasets/ritikagiridhar/2000-hand-gestures)
|
||||
- [Gesture Recognition](https://www.kaggle.com/datasets/imsparsh/gesture-recognition)
|
||||
|
||||
The images were collected and used under the respective licenses provided by each platform and are distributed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
|
||||
|
||||
We would also like to acknowledge the creator of this dataset, [Rion Dsilva](https://www.linkedin.com/in/rion-dsilva-043464229/), for his great contribution to Vision AI research.
|
||||
|
||||
## FAQ
|
||||
|
||||
### How do I train a YOLO26 model on the Hand Keypoints dataset?
|
||||
|
||||
To train a YOLO26 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLO26n-pose model for 100 epochs with an image size of 640:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=hand-keypoints.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### What are the key features of the Hand Keypoints dataset?
|
||||
|
||||
The Hand Keypoints dataset is designed for advanced [pose estimation](https://docs.ultralytics.com/datasets/pose/) tasks and includes several key features:
|
||||
|
||||
- **Large Dataset**: Contains 26,768 images with hand keypoint annotations.
|
||||
- **YOLO26 Compatibility**: Ready for use with YOLO26 models.
|
||||
- **21 Keypoints**: Detailed hand pose representation, including wrist and finger joints.
|
||||
|
||||
For more details, you can explore the [Hand Keypoints Dataset](#introduction) section.
|
||||
|
||||
### What applications can benefit from using the Hand Keypoints dataset?
|
||||
|
||||
The Hand Keypoints dataset can be applied in various fields, including:
|
||||
|
||||
- **Gesture Recognition**: Enhancing human-computer interaction.
|
||||
- **AR/VR Controls**: Improving user experience in augmented and virtual reality.
|
||||
- **Robotic Manipulation**: Enabling precise control of robotic hands.
|
||||
- **Healthcare**: Analyzing hand movements for medical diagnostics.
|
||||
- **Animation**: Capturing motion for realistic animations.
|
||||
- **Biometric Authentication**: Enhancing security systems.
|
||||
|
||||
For more information, refer to the [Applications](#applications) section.
|
||||
|
||||
### How is the Hand Keypoints dataset structured?
|
||||
|
||||
The Hand Keypoints dataset is divided into two subsets:
|
||||
|
||||
1. **Train**: Contains 18,776 images for training pose estimation models.
|
||||
2. **Val**: Contains 7,992 images for validation purposes during model training.
|
||||
|
||||
This structure ensures a comprehensive training and validation process. For more details, see the [Dataset Structure](#dataset-structure) section.
|
||||
|
||||
### How do I use the dataset YAML file for training?
|
||||
|
||||
The dataset configuration is defined in a YAML file, which includes paths, classes, and other relevant information. The `hand-keypoints.yaml` file can be found at [hand-keypoints.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml).
|
||||
|
||||
To use this YAML file for training, specify it in your training script or CLI command as shown in the training example above. For more details, refer to the [Dataset YAML](#dataset-yaml) section.
|
||||
216
algorithms/dms_yolo/code/docs/en/datasets/pose/index.md
Normal file
216
algorithms/dms_yolo/code/docs/en/datasets/pose/index.md
Normal file
@@ -0,0 +1,216 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn about Ultralytics YOLO format for pose estimation datasets, supported formats, COCO-Pose, COCO8-Pose, Tiger-Pose, and how to add your own dataset.
|
||||
keywords: pose estimation, Ultralytics, YOLO format, COCO-Pose, COCO8-Pose, Tiger-Pose, dataset conversion, keypoints
|
||||
---
|
||||
|
||||
# Pose Estimation Datasets Overview
|
||||
|
||||
## Supported Dataset Formats
|
||||
|
||||
### Ultralytics YOLO format
|
||||
|
||||
The dataset label format used for training YOLO pose models is as follows:
|
||||
|
||||
1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
|
||||
2. One row per object: Each row in the text file corresponds to one object instance in the image.
|
||||
3. Object information per row: Each row contains the following information about the object instance:
|
||||
- Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
|
||||
- Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
|
||||
- Object width and height: The width and height of the object, normalized to be between 0 and 1.
|
||||
- Object keypoint coordinates: The keypoints of the object, normalized to be between 0 and 1.
|
||||
|
||||
Here is an example of the label format for a pose estimation task:
|
||||
|
||||
Format with 2D keypoints
|
||||
|
||||
```
|
||||
<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>
|
||||
```
|
||||
|
||||
Format with 3D keypoints (includes visibility per point)
|
||||
|
||||
```
|
||||
<class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <pn-visibility>
|
||||
```
|
||||
|
||||
In this format, `<class-index>` is the index of the class for the object, `<x> <y> <width> <height>` are the normalized coordinates of the [bounding box](https://www.ultralytics.com/glossary/bounding-box), and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the normalized keypoint coordinates. The visibility channel is optional but useful for datasets that annotate occlusion.
|
||||
|
||||
### Dataset YAML format
|
||||
|
||||
The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training pose estimation models. Here is an example of the YAML format used for defining a pose dataset:
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8-pose.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-pose.yaml"
|
||||
```
|
||||
|
||||
The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
|
||||
|
||||
`names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
|
||||
|
||||
(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face. For example if we assume five keypoints of facial landmark: [left eye, right eye, nose, left mouth, right mouth], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3] (just exchange the left-right index, i.e. 0-1 and 3-4, and do not modify others like nose in this example).
|
||||
|
||||
## Usage
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=coco8-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Supported Datasets
|
||||
|
||||
This section outlines the datasets that are compatible with Ultralytics YOLO format and can be used for training [pose estimation](https://docs.ultralytics.com/tasks/pose/) models:
|
||||
|
||||
### COCO-Pose
|
||||
|
||||
- **Description**: COCO-Pose is a large-scale [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and pose estimation dataset. It is a subset of the popular COCO dataset and focuses on human pose estimation. COCO-Pose includes multiple keypoints for each human instance.
|
||||
- **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses.
|
||||
- **Number of Classes**: 1 (Human).
|
||||
- **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
|
||||
- **Usage**: Suitable for training human pose estimation models.
|
||||
- **Additional Notes**: The dataset is rich and diverse, containing over 200k labeled images.
|
||||
- [Read more about COCO-Pose](coco.md)
|
||||
|
||||
### COCO8-Pose
|
||||
|
||||
- **Description**: [Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation.
|
||||
- **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses.
|
||||
- **Number of Classes**: 1 (Human).
|
||||
- **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
|
||||
- **Usage**: Suitable for testing and debugging object detection models, or for experimenting with new detection approaches.
|
||||
- **Additional Notes**: COCO8-Pose is ideal for sanity checks and [CI checks](https://docs.ultralytics.com/help/CI/).
|
||||
- [Read more about COCO8-Pose](coco8-pose.md)
|
||||
|
||||
### Dog-Pose
|
||||
|
||||
- **Description**: The Dog Pose dataset contains 6,773 training and 1,703 test images, providing a diverse and extensive resource for canine keypoint estimation.
|
||||
- **Label Format**: Follows the Ultralytics YOLO format, with annotations for multiple keypoints specific to dog anatomy.
|
||||
- **Number of Classes**: 1 (Dog).
|
||||
- **Keypoints**: Includes 24 keypoints tailored to dog poses, such as limbs, joints, and head positions.
|
||||
- **Usage**: Ideal for training models to estimate dog poses in various scenarios, from research to [real-world applications](https://www.ultralytics.com/blog/custom-training-ultralytics-yolo11-for-dog-pose-estimation).
|
||||
- [Read more about Dog-Pose](dog-pose.md)
|
||||
|
||||
### Hand Keypoints
|
||||
|
||||
- **Description**: The hand keypoints pose dataset comprises nearly 26K images, with 18,776 images allocated for training and 7,992 for validation.
|
||||
- **Label Format**: Same as the Ultralytics YOLO format described above, but with 21 keypoints for a human hand and a visibility dimension.
|
||||
- **Number of Classes**: 1 (Hand).
|
||||
- **Keypoints**: 21 keypoints.
|
||||
- **Usage**: Great for human hand pose estimation and [gesture recognition](https://www.ultralytics.com/blog/enhancing-hand-keypoints-estimation-with-ultralytics-yolo11).
|
||||
- [Read more about Hand Keypoints](hand-keypoints.md)
|
||||
|
||||
### Tiger-Pose
|
||||
|
||||
- **Description**: The [Ultralytics](https://www.ultralytics.com/) Tiger Pose dataset comprises 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation.
|
||||
- **Label Format**: Same as Ultralytics YOLO format as described above, with 12 keypoints for animal pose and no visible dimension.
|
||||
- **Number of Classes**: 1 (Tiger).
|
||||
- **Keypoints**: 12 keypoints.
|
||||
- **Usage**: Great for animal pose or any other pose that is not human-based.
|
||||
- [Read more about Tiger-Pose](tiger-pose.md)
|
||||
|
||||
### Adding your own dataset
|
||||
|
||||
If you have your own dataset and would like to use it for training pose estimation models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.
|
||||
|
||||
### Conversion Tool
|
||||
|
||||
Ultralytics provides a convenient conversion tool to convert labels from the popular [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/) format to YOLO format:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_coco
|
||||
|
||||
convert_coco(labels_dir="path/to/coco/annotations/", use_keypoints=True)
|
||||
```
|
||||
|
||||
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Ultralytics YOLO format for pose estimation?
|
||||
|
||||
The Ultralytics YOLO format for pose estimation datasets involves labeling each image with a corresponding text file. Each row of the text file stores information about an object instance:
|
||||
|
||||
- Object class index
|
||||
- Object center coordinates (normalized x and y)
|
||||
- Object width and height (normalized)
|
||||
- Object keypoint coordinates (normalized pxn and pyn)
|
||||
|
||||
For 2D poses, keypoints include pixel coordinates. For 3D, each keypoint also has a visibility flag. For more details, see [Ultralytics YOLO format](#ultralytics-yolo-format).
|
||||
|
||||
### How do I use the COCO-Pose dataset with Ultralytics YOLO?
|
||||
|
||||
To use the [COCO-Pose dataset](https://docs.ultralytics.com/datasets/pose/coco/) with Ultralytics YOLO:
|
||||
|
||||
1. Download the dataset and prepare your label files in the YOLO format.
|
||||
2. Create a YAML configuration file specifying paths to training and validation images, keypoint shape, and class names.
|
||||
3. Use the configuration file for training:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo26n-pose.pt") # load pretrained model
|
||||
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
For more information, visit [COCO-Pose](coco.md) and [train](../../modes/train.md) sections.
|
||||
|
||||
### How can I add my own dataset for pose estimation in Ultralytics YOLO?
|
||||
|
||||
To add your dataset:
|
||||
|
||||
1. Convert your annotations to the Ultralytics YOLO format.
|
||||
2. Create a YAML configuration file specifying the dataset paths, number of classes, and class names.
|
||||
3. Use the configuration file to train your model:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo26n-pose.pt")
|
||||
results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
For complete steps, check the [Adding your own dataset](#adding-your-own-dataset) section.
|
||||
|
||||
### What is the purpose of the dataset YAML file in Ultralytics YOLO?
|
||||
|
||||
The dataset YAML file in Ultralytics YOLO defines the dataset and model configuration for training. It specifies paths to training, validation, and test images, keypoint shapes, class names, and other configuration options. This structured format helps streamline [dataset management](https://docs.ultralytics.com/datasets/explorer/) and model training. Here is an example YAML format:
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-pose.yaml"
|
||||
```
|
||||
|
||||
Read more about creating YAML configuration files in [Dataset YAML format](#dataset-yaml-format).
|
||||
|
||||
### How can I convert COCO dataset labels to Ultralytics YOLO format for pose estimation?
|
||||
|
||||
Ultralytics provides a conversion tool to convert COCO dataset labels to the YOLO format, including keypoint information:
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_coco
|
||||
|
||||
convert_coco(labels_dir="path/to/coco/annotations/", use_keypoints=True)
|
||||
```
|
||||
|
||||
This tool helps seamlessly integrate COCO datasets into YOLO projects. For details, refer to the [Conversion Tool](#conversion-tool) section and the [data preprocessing guide](https://docs.ultralytics.com/guides/preprocessing_annotated_data/).
|
||||
170
algorithms/dms_yolo/code/docs/en/datasets/pose/tiger-pose.md
Normal file
170
algorithms/dms_yolo/code/docs/en/datasets/pose/tiger-pose.md
Normal file
@@ -0,0 +1,170 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore Ultralytics Tiger-Pose dataset with 263 diverse images. Ideal for testing, training, and refining pose estimation algorithms.
|
||||
keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLO26, training data, machine learning, neural networks
|
||||
---
|
||||
|
||||
# Tiger-Pose Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
[Ultralytics](https://www.ultralytics.com/) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithms.
|
||||
|
||||
Despite its manageable training split of 210 images, the Tiger-Pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for [pose estimation](https://docs.ultralytics.com/tasks/pose/).
|
||||
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
- **Total images**: 263 (210 train / 53 val).
|
||||
- **Keypoints**: 12 per tiger (no visibility flag).
|
||||
- **Directory layout**: YOLO-format keypoints stored under `labels/{train,val}` alongside `images/{train,val}` directories.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Gc6K5eKrTNQ"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train YOLO26 Pose Model on Tiger-Pose Dataset Using Ultralytics Platform
|
||||
</p>
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file serves as the means to specify the configuration details of a dataset. It encompasses crucial data such as file paths, class definitions, and other pertinent information. Specifically, for the `tiger-pose.yaml` file, you can check [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/tiger-pose.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/tiger-pose.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-pose model on the Tiger-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=tiger-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are some examples of images from the Tiger-Pose dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-4.avif" alt="Tiger pose estimation dataset mosaic training batch" width="100%">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the Tiger-Pose dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Inference Example
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("path/to/best.pt") # load a tiger-pose trained model
|
||||
|
||||
# Run inference
|
||||
results = model.predict(source="https://youtu.be/MIBAT6BGE6U", show=True)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Run inference using a tiger-pose trained model
|
||||
yolo pose predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"
|
||||
```
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Ultralytics Tiger-Pose dataset used for?
|
||||
|
||||
The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
### How do I train a YOLO26 model on the Tiger-Pose dataset?
|
||||
|
||||
To train a YOLO26n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo pose train data=tiger-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What configurations does the `tiger-pose.yaml` file include?
|
||||
|
||||
The `tiger-pose.yaml` file is used to specify the configuration details of the Tiger-Pose dataset. It includes crucial data such as file paths and class definitions. To see the exact configuration, you can check out the [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
|
||||
|
||||
### How can I run inference using a YOLO26 model trained on the Tiger-Pose dataset?
|
||||
|
||||
To perform inference using a YOLO26 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
|
||||
|
||||
!!! example "Inference Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("path/to/best.pt") # load a tiger-pose trained model
|
||||
|
||||
# Run inference
|
||||
results = model.predict(source="https://youtu.be/MIBAT6BGE6U", show=True)
|
||||
```
|
||||
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Run inference using a tiger-pose trained model
|
||||
yolo pose predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"
|
||||
```
|
||||
|
||||
### What are the benefits of using the Tiger-Pose dataset for pose estimation?
|
||||
|
||||
The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics), enhancing model performance and [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
@@ -0,0 +1,178 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Carparts Segmentation Dataset for automotive AI applications. Enhance your segmentation models with rich, annotated data using Ultralytics YOLO.
|
||||
keywords: Carparts Segmentation Dataset, computer vision, automotive AI, vehicle maintenance, Ultralytics, YOLO, segmentation models, deep learning, object segmentation
|
||||
---
|
||||
|
||||
# Carparts Segmentation Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Carparts Segmentation Dataset In Colab"></a>
|
||||
|
||||
The Carparts Segmentation Dataset, available on Roboflow Universe, is a curated collection of images and videos designed for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications, specifically focusing on [segmentation tasks](https://docs.ultralytics.com/tasks/segment/). Hosted on Roboflow Universe, this dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable [annotated](https://www.ultralytics.com/glossary/data-labeling) examples for training and testing segmentation models.
|
||||
|
||||
Whether you're working on [automotive research](https://www.ultralytics.com/solutions/ai-in-automotive), developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of your projects using models like [Ultralytics YOLO](../../models/yolo26.md).
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/7lZa3Yi2kbo"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Carparts <a href="https://www.ultralytics.com/glossary/instance-segmentation">Instance Segmentation</a> with Ultralytics YOLO26.
|
||||
</p>
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The data distribution within the Carparts Segmentation Dataset is organized as follows:
|
||||
|
||||
- **Training set**: Includes 3156 images, each accompanied by its corresponding annotations. This set is used for [training](https://www.ultralytics.com/glossary/training-data) the [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) [model](https://www.ultralytics.com/glossary/foundation-model).
|
||||
- **Testing set**: Comprises 276 images, with each one paired with its respective annotations. This set is used to evaluate the model's performance after training using [test data](https://www.ultralytics.com/glossary/test-data).
|
||||
- **Validation set**: Consists of 401 images, each having corresponding annotations. This set is used during training to tune [hyperparameters](https://docs.ultralytics.com/guides/hyperparameter-tuning/) and prevent [overfitting](https://www.ultralytics.com/glossary/overfitting) using [validation data](https://www.ultralytics.com/glossary/validation-data).
|
||||
|
||||
## Applications
|
||||
|
||||
Carparts Segmentation finds applications in various domains including:
|
||||
|
||||
- **Automotive Quality Control**: Identifying defects or inconsistencies in car parts during manufacturing ([AI in Manufacturing](https://www.ultralytics.com/solutions/ai-in-manufacturing)).
|
||||
- **Auto Repair**: Assisting mechanics in identifying parts for repair or replacement.
|
||||
- **E-commerce Cataloging**: Automatically tagging and categorizing car parts in online stores for [e-commerce](https://en.wikipedia.org/wiki/E-commerce) platforms.
|
||||
- **Traffic Monitoring**: Analyzing vehicle components in traffic surveillance footage.
|
||||
- **Autonomous Vehicles**: Enhancing the perception systems of [self-driving cars](https://www.ultralytics.com/blog/ai-in-self-driving-cars) to better understand surrounding vehicles.
|
||||
- **Insurance Processing**: Automating damage assessment by identifying affected car parts during insurance claims.
|
||||
- **Recycling**: Sorting vehicle components for efficient recycling processes.
|
||||
- **Smart City Initiatives**: Contributing data for urban planning and traffic management systems within [Smart Cities](https://en.wikipedia.org/wiki/Smart_city).
|
||||
|
||||
By accurately identifying and categorizing different vehicle components, carparts segmentation streamlines processes and contributes to increased efficiency and automation across these industries.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A [YAML](https://www.ultralytics.com/glossary/yaml) (Yet Another Markup Language) file defines the dataset configuration, including paths, class names, and other essential details. For the Carparts Segmentation dataset, the `carparts-seg.yaml` file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml). You can learn more about the YAML format at [yaml.org](https://yaml.org/).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/carparts-seg.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/carparts-seg.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train an [Ultralytics YOLO26](../../models/yolo26.md) model on the Carparts Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following code snippets. Refer to the model [Training guide](../../modes/train.md) for a comprehensive list of available arguments and explore [model training tips](https://docs.ultralytics.com/guides/model-training-tips/) for best practices.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained segmentation model like YOLO26n-seg
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model on the Carparts Segmentation dataset
|
||||
results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
|
||||
|
||||
# After training, you can validate the model's performance on the validation set
|
||||
results = model.val()
|
||||
|
||||
# Or perform prediction on new images or videos
|
||||
results = model.predict("path/to/your/image.jpg")
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model using the Command Line Interface
|
||||
# Specify the dataset config file, model, number of epochs, and image size
|
||||
yolo segment train data=carparts-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
|
||||
# Validate the trained model using the validation set
|
||||
yolo segment val data=carparts-seg.yaml model=path/to/best.pt
|
||||
|
||||
# Predict using the trained model on a specific image source
|
||||
yolo segment predict model=path/to/best.pt source=path/to/your/image.jpg
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The Carparts Segmentation dataset includes a diverse array of images and videos captured from various perspectives. Below are examples showcasing the data and its corresponding annotations:
|
||||
|
||||
|
||||
|
||||
- The image demonstrates [object segmentation](https://docs.ultralytics.com/tasks/segment/) within a car image sample. Annotated [bounding boxes](https://www.ultralytics.com/glossary/bounding-box) with masks highlight the identified car parts (e.g., headlights, grille).
|
||||
- The dataset features a variety of images captured under different conditions (locations, lighting, object densities), providing a comprehensive resource for training robust car part segmentation models.
|
||||
- This example underscores the dataset's complexity and the importance of [high-quality data](https://www.ultralytics.com/blog/the-importance-of-high-quality-computer-vision-datasets) for computer vision tasks, especially in specialized domains like automotive component analysis. Techniques like [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) can further enhance model generalization.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you utilize the Carparts Segmentation dataset in your research or development efforts, please cite the original source:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{ car-seg-un1pm_dataset,
|
||||
title = { car-seg Dataset },
|
||||
type = { Open Source Dataset },
|
||||
author = { Gianmarco Russo },
|
||||
url = { https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm },
|
||||
journal = { Roboflow Universe },
|
||||
publisher = { Roboflow },
|
||||
year = { 2023 },
|
||||
month = { nov },
|
||||
note = { visited on 2024-01-24 },
|
||||
}
|
||||
```
|
||||
|
||||
We acknowledge the contribution of Gianmarco Russo and the Roboflow team in creating and maintaining this valuable dataset for the computer vision community. For more datasets, visit the [Ultralytics Datasets collection](https://docs.ultralytics.com/datasets/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Carparts Segmentation Dataset?
|
||||
|
||||
The Carparts Segmentation Dataset is a specialized collection of images and videos for training computer vision models to perform [segmentation](https://docs.ultralytics.com/tasks/segment/) on car parts. It includes diverse visuals with detailed annotations, suitable for automotive AI applications.
|
||||
|
||||
### How can I use the Carparts Segmentation Dataset with Ultralytics YOLO26?
|
||||
|
||||
You can train an [Ultralytics YOLO26](../../models/yolo26.md) segmentation model using this dataset. Load a pretrained model (e.g., `yolo26n-seg.pt`) and initiate training using the provided Python or CLI examples, referencing the `carparts-seg.yaml` configuration file. Check the [Training Guide](../../modes/train.md) for detailed instructions.
|
||||
|
||||
!!! example "Train Example Snippet"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo segment train data=carparts-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are some applications of Carparts Segmentation?
|
||||
|
||||
Carparts Segmentation is useful in:
|
||||
|
||||
- **Automotive Quality Control**: Ensuring parts meet standards ([AI in Manufacturing](https://www.ultralytics.com/solutions/ai-in-manufacturing)).
|
||||
- **Auto Repair**: Identifying parts needing service.
|
||||
- **E-commerce**: Cataloging parts online.
|
||||
- **Autonomous Vehicles**: Improving vehicle perception ([AI in Automotive](https://www.ultralytics.com/solutions/ai-in-automotive)).
|
||||
- **Insurance**: Assessing vehicle damage automatically.
|
||||
- **Recycling**: Sorting parts efficiently.
|
||||
|
||||
### Where can I find the dataset configuration file for Carparts Segmentation?
|
||||
|
||||
The dataset configuration file, `carparts-seg.yaml`, which contains details about the dataset paths and classes, is located in the Ultralytics GitHub repository: [carparts-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml).
|
||||
|
||||
### Why should I use the Carparts Segmentation Dataset?
|
||||
|
||||
This dataset offers rich, annotated data crucial for developing accurate [segmentation models](https://docs.ultralytics.com/tasks/segment/) for automotive applications. Its diversity helps improve model robustness and performance in real-world scenarios like automated vehicle inspection, enhancing safety systems, and supporting autonomous driving technology. Using high-quality, domain-specific datasets like this accelerates AI development.
|
||||
156
algorithms/dms_yolo/code/docs/en/datasets/segment/coco.md
Normal file
156
algorithms/dms_yolo/code/docs/en/datasets/segment/coco.md
Normal file
@@ -0,0 +1,156 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the COCO-Seg dataset, an extension of COCO, with detailed segmentation annotations. Learn how to train YOLO models with COCO-Seg.
|
||||
keywords: COCO-Seg, dataset, YOLO models, instance segmentation, object detection, COCO dataset, YOLO26, computer vision, Ultralytics, machine learning
|
||||
---
|
||||
|
||||
# COCO-Seg Dataset
|
||||
|
||||
The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO (Common Objects in Context) dataset, is specially designed to aid research in object [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation). It uses the same images as COCO but introduces more detailed segmentation annotations. This dataset is a crucial resource for researchers and developers working on instance segmentation tasks, especially for training [Ultralytics YOLO](https://docs.ultralytics.com/models/) models.
|
||||
|
||||
## COCO-Seg Pretrained Models
|
||||
|
||||
{% include "macros/yolo-seg-perf.md" %}
|
||||
|
||||
## Key Features
|
||||
|
||||
- COCO-Seg retains the original 330K images from COCO.
|
||||
- The dataset consists of the same 80 object categories found in the original COCO dataset.
|
||||
- Annotations now include more detailed instance segmentation masks for each object in the images.
|
||||
- COCO-Seg provides standardized evaluation metrics like [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) for object detection, and mean Average [Recall](https://www.ultralytics.com/glossary/recall) (mAR) for instance segmentation tasks, enabling effective comparison of model performance.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The COCO-Seg dataset is partitioned into three subsets:
|
||||
|
||||
1. **Train2017**: 118K images for training instance segmentation models.
|
||||
2. **Val2017**: 5K images used for validation during model development.
|
||||
3. **Test2017**: 20K images used for benchmarking. Ground-truth annotations for this subset are not publicly available, so predictions must be submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7383) for scoring.
|
||||
|
||||
## Applications
|
||||
|
||||
COCO-Seg is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in instance segmentation, such as the YOLO models. The large number of annotated images, the diversity of object categories, and the standardized evaluation metrics make it an indispensable resource for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) researchers and practitioners.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Seg dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-seg model on the COCO-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
COCO-Seg, like its predecessor COCO, contains a diverse set of images with various object categories and complex scenes. However, COCO-Seg introduces more detailed instance segmentation masks for each object in the images. Here are some examples of images from the dataset, along with their corresponding instance segmentation masks:
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. [Mosaicing](https://docs.ultralytics.com/guides/hyperparameter-tuning/) is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This aids the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the COCO-Seg dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We extend our thanks to the COCO Consortium for creating and maintaining this invaluable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO-Seg dataset and how does it differ from the original COCO dataset?
|
||||
|
||||
The [COCO-Seg](https://cocodataset.org/#home) dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on [object instance segmentation](https://docs.ultralytics.com/tasks/segment/).
|
||||
|
||||
### How can I train a YOLO26 model using the COCO-Seg dataset?
|
||||
|
||||
To train a YOLO26n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### What are the key features of the COCO-Seg dataset?
|
||||
|
||||
The COCO-Seg dataset includes several key features:
|
||||
|
||||
- Retains the original 330K images from the COCO dataset.
|
||||
- Annotates the same 80 object categories found in the original COCO.
|
||||
- Provides more detailed instance segmentation masks for each object.
|
||||
- Uses standardized evaluation metrics such as mean Average [Precision](https://www.ultralytics.com/glossary/precision) (mAP) for [object detection](https://www.ultralytics.com/glossary/object-detection) and mean Average Recall (mAR) for instance segmentation tasks.
|
||||
|
||||
### What pretrained models are available for COCO-Seg, and what are their performance metrics?
|
||||
|
||||
The COCO-Seg dataset supports multiple pretrained YOLO26 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
|
||||
|
||||
{% include "macros/yolo-seg-perf.md" %}
|
||||
|
||||
These models range from the lightweight YOLO26n-seg to the more powerful YOLO26x-seg, offering different trade-offs between speed and accuracy to suit various application requirements. For more information on model selection, visit the [Ultralytics models page](https://docs.ultralytics.com/models/).
|
||||
|
||||
### How is the COCO-Seg dataset structured and what subsets does it contain?
|
||||
|
||||
The COCO-Seg dataset is partitioned into three subsets for specific training and evaluation needs:
|
||||
|
||||
1. **Train2017**: Contains 118K images used primarily for training instance segmentation models.
|
||||
2. **Val2017**: Comprises 5K images utilized for validation during the training process.
|
||||
3. **Test2017**: Encompasses 20K images reserved for testing and benchmarking trained models. Note that ground truth annotations for this subset are not publicly available, and performance results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7383) for assessment.
|
||||
|
||||
For smaller experimentation needs, you might also consider using the [COCO8-seg dataset](https://docs.ultralytics.com/datasets/segment/coco8-seg/), which is a compact version containing just 8 images from the COCO train 2017 set.
|
||||
130
algorithms/dms_yolo/code/docs/en/datasets/segment/coco128-seg.md
Normal file
130
algorithms/dms_yolo/code/docs/en/datasets/segment/coco128-seg.md
Normal file
@@ -0,0 +1,130 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover the COCO128-Seg dataset by Ultralytics, a compact yet diverse segmentation dataset ideal for testing and training YOLO26 models.
|
||||
keywords: COCO128-Seg, Ultralytics, segmentation dataset, YOLO26, COCO 2017, model training, computer vision, dataset configuration
|
||||
---
|
||||
|
||||
# COCO128-Seg Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
[Ultralytics](https://www.ultralytics.com/) COCO128-Seg is a small but versatile [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) dataset composed of the first 128 images of the COCO train 2017 set. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 128 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
- **Images**: 128 total. The default YAML reuses the same directory for train and val so you can quickly iterate, but you can duplicate or customize the split if desired.
|
||||
- **Classes**: Same 80 object categories as COCO.
|
||||
- **Labels**: YOLO-format polygons saved beside each image inside `labels/{train,val}`.
|
||||
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO128-Seg dataset, the `coco128-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128-seg.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco128-seg.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco128-seg.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-seg model on the COCO128-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco128-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are some examples of images from the COCO128-Seg dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-2.avif" alt="COCO128-seg instance segmentation dataset mosaic" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the COCO128-Seg dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO128-Seg dataset, and how is it used in Ultralytics YOLO26?
|
||||
|
||||
The **COCO128-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 128 images from the COCO train 2017 set. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO26](https://github.com/ultralytics/ultralytics) and [Platform](https://platform.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### How can I train a YOLO26n-seg model using the COCO128-Seg dataset?
|
||||
|
||||
To train a **YOLO26n-seg** model on the COCO128-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # Load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco128-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a thorough explanation of available arguments and configuration options, you can check the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### Why is the COCO128-Seg dataset important for model development and debugging?
|
||||
|
||||
The **COCO128-Seg dataset** offers a balanced combination of manageability and diversity with 128 images, making it perfect for quickly testing and debugging segmentation models or experimenting with new detection techniques. Its moderate size allows for fast training iterations while providing enough diversity to validate training pipelines before scaling to larger datasets. Learn more about supported dataset formats in the [Ultralytics segmentation dataset guide](https://docs.ultralytics.com/datasets/segment/).
|
||||
|
||||
### Where can I find the YAML configuration file for the COCO128-Seg dataset?
|
||||
|
||||
The YAML configuration file for the **COCO128-Seg dataset** is available in the Ultralytics repository. You can access the file directly at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128-seg.yaml>. The YAML file includes essential information about dataset paths, classes, and configuration settings required for model training and validation.
|
||||
|
||||
### What are some benefits of using mosaicing during training with the COCO128-Seg dataset?
|
||||
|
||||
Using **mosaicing** during training helps increase the diversity and variety of objects and scenes in each training batch. This technique combines multiple images into a single composite image, enhancing the model's ability to generalize to different object sizes, aspect ratios, and contexts within the scene. Mosaicing is beneficial for improving a model's robustness and [accuracy](https://www.ultralytics.com/glossary/accuracy), especially when working with moderately-sized datasets like COCO128-Seg. For an example of mosaiced images, see the [Sample Images and Annotations](#sample-images-and-annotations) section.
|
||||
130
algorithms/dms_yolo/code/docs/en/datasets/segment/coco8-seg.md
Normal file
130
algorithms/dms_yolo/code/docs/en/datasets/segment/coco8-seg.md
Normal file
@@ -0,0 +1,130 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover the versatile and manageable COCO8-Seg dataset by Ultralytics, ideal for testing and debugging segmentation models or new detection approaches.
|
||||
keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLO26, COCO 2017, model training, computer vision, dataset configuration
|
||||
---
|
||||
|
||||
# COCO8-Seg Dataset
|
||||
|
||||
## Introduction
|
||||
|
||||
[Ultralytics](https://www.ultralytics.com/) COCO8-Seg is a small but versatile [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
- **Images**: 8 total (4 train / 4 val).
|
||||
- **Classes**: 80 COCO categories.
|
||||
- **Labels**: YOLO-format polygons stored under `labels/{train,val}` matching each image file.
|
||||
|
||||
This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Seg dataset, the `coco8-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8-seg.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train a YOLO26n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Images and Annotations
|
||||
|
||||
Here are some examples of images from the COCO8-Seg dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-2.avif" alt="COCO8-seg instance segmentation dataset mosaic" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
The example showcases the variety and complexity of the images in the COCO8-Seg dataset and the benefits of using mosaicing during the training process.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the COCO dataset in your research or development work, please cite the following paper:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{lin2015microsoft,
|
||||
title={Microsoft COCO: Common Objects in Context},
|
||||
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
|
||||
year={2015},
|
||||
eprint={1405.0312},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CV}
|
||||
}
|
||||
```
|
||||
|
||||
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLO26?
|
||||
|
||||
The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLO26](https://github.com/ultralytics/ultralytics) and [Platform](https://platform.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
|
||||
|
||||
### How can I train a YOLO26n-seg model using the COCO8-Seg dataset?
|
||||
|
||||
To train a **YOLO26n-seg** model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # Load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
For a thorough explanation of available arguments and configuration options, you can check the [Training](../../modes/train.md) documentation.
|
||||
|
||||
### Why is the COCO8-Seg dataset important for model development and debugging?
|
||||
|
||||
The **COCO8-Seg dataset** offers a compact yet diverse set of 8 images, making it perfect for quickly testing and debugging segmentation models or experimenting with new detection techniques. Its small size allows for fast sanity checks and early pipeline validation, helping identify issues before scaling to larger datasets. Learn more about supported dataset formats in the [Ultralytics segmentation dataset guide](https://docs.ultralytics.com/datasets/segment/).
|
||||
|
||||
### Where can I find the YAML configuration file for the COCO8-Seg dataset?
|
||||
|
||||
The YAML configuration file for the **COCO8-Seg dataset** is available in the Ultralytics repository. You can access the file directly at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml>. The YAML file includes essential information about dataset paths, classes, and configuration settings required for model training and validation.
|
||||
|
||||
### What are some benefits of using mosaicing during training with the COCO8-Seg dataset?
|
||||
|
||||
Using **mosaicing** during training helps increase the diversity and variety of objects and scenes in each training batch. This technique combines multiple images into a single composite image, enhancing the model's ability to generalize to different object sizes, aspect ratios, and contexts within the scene. Mosaicing is beneficial for improving a model's robustness and [accuracy](https://www.ultralytics.com/glossary/accuracy), especially when working with small datasets like COCO8-Seg. For an example of mosaiced images, see the [Sample Images and Annotations](#sample-images-and-annotations) section.
|
||||
156
algorithms/dms_yolo/code/docs/en/datasets/segment/crack-seg.md
Normal file
156
algorithms/dms_yolo/code/docs/en/datasets/segment/crack-seg.md
Normal file
@@ -0,0 +1,156 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the extensive Crack Segmentation Dataset, ideal for transportation safety, infrastructure maintenance, and self-driving car model development using Ultralytics YOLO.
|
||||
keywords: Crack Segmentation Dataset, Ultralytics, transportation safety, public safety, self-driving cars, computer vision, road safety, infrastructure maintenance, dataset, YOLO, segmentation, deep learning
|
||||
---
|
||||
|
||||
# Crack Segmentation Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-crack-segmentation-dataset.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Crack Segmentation Dataset In Colab"></a>
|
||||
|
||||
The Crack Segmentation Dataset, available on Roboflow Universe, is an extensive resource designed for individuals involved in transportation and public safety studies. It is also beneficial for developing [self-driving car](https://www.ultralytics.com/blog/ai-in-self-driving-cars) models or exploring various [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. This dataset is part of the broader collection available on the Ultralytics [Datasets Hub](../../datasets/index.md).
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/C4mc40YKm-g"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Crack segmentation using Ultralytics YOLOv9.
|
||||
</p>
|
||||
|
||||
Comprising 4029 static images captured from diverse road and wall scenarios, this dataset is a valuable asset for crack segmentation tasks. Whether you are researching transportation infrastructure or aiming to enhance the [accuracy](https://www.ultralytics.com/glossary/accuracy) of autonomous driving systems, this dataset provides a rich collection of images for training [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models.
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The Crack Segmentation Dataset is organized into three subsets:
|
||||
|
||||
- **Training set**: 3717 images with corresponding annotations.
|
||||
- **Testing set**: 112 images with corresponding annotations.
|
||||
- **Validation set**: 200 images with corresponding annotations.
|
||||
|
||||
## Applications
|
||||
|
||||
Crack segmentation finds practical applications in [infrastructure maintenance](https://www.ultralytics.com/blog/using-ai-for-crack-detection-and-segmentation), aiding in the identification and assessment of structural damage in buildings, bridges, and roads. It also plays a crucial role in enhancing [road safety](https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries) by enabling automated systems to detect pavement cracks for timely repairs.
|
||||
|
||||
In industrial settings, crack detection using deep learning models like [Ultralytics YOLO26](../../models/yolo26.md) helps ensure building integrity in construction, prevents costly downtimes in [manufacturing](https://www.ultralytics.com/solutions/ai-in-manufacturing), and makes road inspections safer and more effective. Automatically identifying and classifying cracks allows maintenance teams to prioritize repairs efficiently, contributing to better [model evaluation insights](../../guides/model-evaluation-insights.md).
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A [YAML](https://www.ultralytics.com/glossary/yaml) (Yet Another Markup Language) file defines the dataset configuration. It includes details about the dataset's paths, classes, and other relevant information. For the Crack Segmentation dataset, the `crack-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/crack-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/crack-seg.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/crack-seg.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/crack-seg.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train the Ultralytics YOLO26n-seg model on the Crack Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following [Python](https://www.python.org/) or CLI snippets. Refer to the model [Training](../../modes/train.md) documentation page for a comprehensive list of available arguments and configurations like [hyperparameter tuning](../../guides/hyperparameter-tuning.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
# Using a pretrained model like yolo26n-seg.pt is recommended for faster convergence
|
||||
model = YOLO("yolo26n-seg.pt")
|
||||
|
||||
# Train the model on the Crack Segmentation dataset
|
||||
# Ensure 'crack-seg.yaml' is accessible or provide the full path
|
||||
results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)
|
||||
|
||||
# After training, the model can be used for prediction or exported
|
||||
# results = model.predict(source='path/to/your/images')
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model using the Command Line Interface
|
||||
# Ensure the dataset YAML file 'crack-seg.yaml' is correctly configured and accessible
|
||||
yolo segment train data=crack-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The Crack Segmentation dataset contains a diverse collection of images captured from various perspectives, showcasing different types of cracks on roads and walls. Here are some examples:
|
||||
|
||||
|
||||
|
||||
- This image demonstrates [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), featuring annotated [bounding boxes](https://www.ultralytics.com/glossary/bounding-box) with masks outlining identified cracks. The dataset includes images from different locations and environments, making it a comprehensive resource for developing robust models for this task. Techniques like [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) can further enhance dataset diversity. Learn more about instance segmentation and tracking in our [guide](../../guides/instance-segmentation-and-tracking.md).
|
||||
|
||||
- The example highlights the diversity within the Crack Segmentation dataset, emphasizing the importance of high-quality data for training effective computer vision models.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you use the Crack Segmentation dataset in your research or development work, please cite the source appropriately. The dataset was made available via Roboflow:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{ crack-bphdr_dataset,
|
||||
title = { crack Dataset },
|
||||
type = { Open Source Dataset },
|
||||
author = { University },
|
||||
url = { https://universe.roboflow.com/university-bswxt/crack-bphdr },
|
||||
journal = { Roboflow Universe },
|
||||
publisher = { Roboflow },
|
||||
year = { 2022 },
|
||||
month = { dec },
|
||||
note = { visited on 2024-01-23 },
|
||||
}
|
||||
```
|
||||
|
||||
We acknowledge the team at Roboflow for making the Crack Segmentation dataset available, providing a valuable resource for the computer vision community, particularly for projects related to road safety and infrastructure assessment.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Crack Segmentation Dataset?
|
||||
|
||||
The Crack Segmentation Dataset is a collection of 4029 static images designed for transportation and public safety studies. It's suitable for tasks like [self-driving car](https://www.ultralytics.com/blog/ai-in-self-driving-cars) model development and [infrastructure maintenance](https://www.ultralytics.com/blog/using-ai-for-crack-detection-and-segmentation). It includes training, testing, and validation sets for crack detection and [segmentation](../../tasks/segment.md) tasks.
|
||||
|
||||
### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLO26?
|
||||
|
||||
To train an [Ultralytics YOLO26](../../models/yolo26.md) model on this dataset, use the provided Python or CLI examples. Detailed instructions and parameters are available on the model [Training](../../modes/train.md) page. You can manage your training process using tools like [Ultralytics Platform](https://platform.ultralytics.com).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a pretrained model (recommended)
|
||||
model = YOLO("yolo26n-seg.pt")
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained model via CLI
|
||||
yolo segment train data=crack-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
### Why use the Crack Segmentation Dataset for self-driving car projects?
|
||||
|
||||
This dataset is valuable for self-driving car projects due to its diverse images of roads and walls, covering various real-world scenarios. This diversity improves the robustness of models trained for crack detection, which is crucial for road safety and infrastructure assessment. The detailed annotations aid in [developing models](../../guides/model-training-tips.md) that can accurately identify potential road hazards.
|
||||
|
||||
### What features does Ultralytics YOLO offer for crack segmentation?
|
||||
|
||||
Ultralytics YOLO provides real-time [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification capabilities, making it highly suitable for crack segmentation tasks. It efficiently handles large datasets and complex scenarios. The framework includes comprehensive modes for [Training](../../modes/train.md), [Prediction](../../modes/predict.md), and [Exporting](../../modes/export.md) models. YOLO's [anchor-free detection](https://www.ultralytics.com/blog/benefits-ultralytics-yolo11-being-anchor-free-detector) approach can improve performance on irregular shapes like cracks, and performance can be measured using standard [metrics](../../guides/yolo-performance-metrics.md).
|
||||
|
||||
### How do I cite the Crack Segmentation Dataset?
|
||||
|
||||
If using this dataset in your work, please cite it using the provided BibTeX entry above to give appropriate credit to the creators.
|
||||
212
algorithms/dms_yolo/code/docs/en/datasets/segment/index.md
Normal file
212
algorithms/dms_yolo/code/docs/en/datasets/segment/index.md
Normal file
@@ -0,0 +1,212 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the supported dataset formats for Ultralytics YOLO and learn how to prepare and use datasets for training object segmentation models.
|
||||
keywords: Ultralytics, YOLO, instance segmentation, dataset formats, auto-annotation, COCO, segmentation models, training data
|
||||
---
|
||||
|
||||
# Instance Segmentation Datasets Overview
|
||||
|
||||
Instance segmentation is a computer vision task that involves identifying and delineating individual objects within an image. This guide provides an overview of dataset formats supported by Ultralytics YOLO for instance segmentation tasks, along with instructions on how to prepare, convert, and use these datasets for training your models.
|
||||
|
||||
## Supported Dataset Formats
|
||||
|
||||
### Ultralytics YOLO format
|
||||
|
||||
The dataset label format used for training YOLO segmentation models is as follows:
|
||||
|
||||
1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
|
||||
2. One row per object: Each row in the text file corresponds to one object instance in the image.
|
||||
3. Object information per row: Each row contains the following information about the object instance:
|
||||
- Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
|
||||
- Object bounding coordinates: The bounding coordinates around the mask area, normalized to be between 0 and 1.
|
||||
|
||||
The format for a single row in the segmentation dataset file is as follows:
|
||||
|
||||
```
|
||||
<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>
|
||||
```
|
||||
|
||||
In this format, `<class-index>` is the index of the class for the object, and `<x1> <y1> <x2> <y2> ... <xn> <yn>` are the normalized polygon coordinates of the object's segmentation mask (values are in `[0, 1]` relative to image width and height). The coordinates are separated by spaces.
|
||||
|
||||
Here is an example of the YOLO dataset format for a single image with two objects made up of a 3-point segment and a 5-point segment.
|
||||
|
||||
```
|
||||
0 0.681 0.485 0.670 0.487 0.676 0.487
|
||||
1 0.504 0.000 0.501 0.004 0.498 0.004 0.493 0.010 0.492 0.0104
|
||||
```
|
||||
|
||||
!!! tip
|
||||
|
||||
- The length of each row does **not** have to be equal.
|
||||
- Each segmentation label must have a **minimum of 3 `(x, y)` points**: `<class-index> <x1> <y1> <x2> <y2> <x3> <y3>`
|
||||
|
||||
### Dataset YAML format
|
||||
|
||||
The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Segmentation Models. Here is an example of the YAML format used for defining a segmentation dataset:
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/coco8-seg.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
|
||||
```
|
||||
|
||||
The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
|
||||
|
||||
`names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
|
||||
|
||||
## Usage
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
|
||||
|
||||
# Train the model
|
||||
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Start training from a pretrained *.pt model
|
||||
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
```
|
||||
|
||||
## Supported Datasets
|
||||
|
||||
Ultralytics YOLO supports various datasets for instance segmentation tasks. Here's a list of the most commonly used ones:
|
||||
|
||||
- [Carparts-seg](carparts-seg.md): A specialized dataset focused on the segmentation of car parts, ideal for automotive applications. It includes a variety of vehicles with detailed annotations of individual car components.
|
||||
- [COCO](coco.md): A comprehensive dataset for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and captioning, featuring over 200K labeled images across a wide range of categories.
|
||||
- [COCO8-seg](coco8-seg.md): A compact, 8-image subset of COCO designed for quick testing of segmentation model training, ideal for CI checks and workflow validation in the `ultralytics` repository.
|
||||
- [COCO128-seg](coco128-seg.md): A smaller dataset for [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) tasks, containing a subset of 128 COCO images with segmentation annotations.
|
||||
- [Crack-seg](crack-seg.md): A dataset tailored for the segmentation of cracks in various surfaces. Essential for infrastructure maintenance and quality control, it provides detailed imagery for training models to identify structural weaknesses.
|
||||
- [Package-seg](package-seg.md): A dataset dedicated to the segmentation of different types of packaging materials and shapes. It's particularly useful for logistics and warehouse automation, aiding in the development of systems for package handling and sorting.
|
||||
|
||||
### Adding your own dataset
|
||||
|
||||
If you have your own dataset and would like to use it for training segmentation models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file. Keep `images/` and `labels/` as separate folders at the same level, with matching subfolder structure; placing label `.txt` files in the image folder can cause the model to miss labels.
|
||||
|
||||
## Port or Convert Label Formats
|
||||
|
||||
### COCO Dataset Format to YOLO Format
|
||||
|
||||
You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_coco
|
||||
|
||||
convert_coco(labels_dir="path/to/coco/annotations/", use_segments=True)
|
||||
```
|
||||
|
||||
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
|
||||
|
||||
Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful segmentation models.
|
||||
|
||||
## Auto-Annotation
|
||||
|
||||
Auto-annotation is an essential feature that allows you to generate a segmentation dataset using a pretrained detection model. It enables you to quickly and accurately annotate a large number of images without the need for manual labeling, saving time and effort.
|
||||
|
||||
### Generate Segmentation Dataset Using a Detection Model
|
||||
|
||||
To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics.data.annotator import auto_annotate
|
||||
|
||||
auto_annotate(data="path/to/images", det_model="yolo26x.pt", sam_model="sam_b.pt")
|
||||
```
|
||||
|
||||
{% include "macros/sam-auto-annotate.md" %}
|
||||
|
||||
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pretrained detection models i.e. [YOLO26](../../models/yolo26.md), [YOLO11](../../models/yolo11.md) or other [models](../../models/index.md) and segmentation models i.e, [SAM](../../models/sam.md), [SAM2](../../models/sam-2.md) or [MobileSAM](../../models/mobile-sam.md), the device to run the models on, and the output directory for saving the annotated results.
|
||||
|
||||
By leveraging the power of pretrained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
|
||||
|
||||
### Visualize Dataset Annotations
|
||||
|
||||
Before training your model, it's often helpful to visualize your dataset annotations to ensure they're correct. Ultralytics provides a utility function for this purpose:
|
||||
|
||||
```python
|
||||
from ultralytics.data.utils import visualize_image_annotations
|
||||
|
||||
label_map = { # Define the label map with all annotated class labels.
|
||||
0: "person",
|
||||
1: "car",
|
||||
}
|
||||
|
||||
# Visualize
|
||||
visualize_image_annotations(
|
||||
"path/to/image.jpg", # Input image path.
|
||||
"path/to/annotations.txt", # Annotation file path for the image.
|
||||
label_map,
|
||||
)
|
||||
```
|
||||
|
||||
This function draws bounding boxes, labels objects with class names, and adjusts text color for better readability, helping you identify and correct any annotation errors before training.
|
||||
|
||||
### Converting Segmentation Masks to YOLO Format
|
||||
|
||||
If you have segmentation masks in binary format, you can convert them to the YOLO segmentation format using:
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_segment_masks_to_yolo_seg
|
||||
|
||||
# For datasets like COCO with 80 classes
|
||||
convert_segment_masks_to_yolo_seg(masks_dir="path/to/masks_dir", output_dir="path/to/output_dir", classes=80)
|
||||
```
|
||||
|
||||
This utility converts binary mask images into the YOLO segmentation format and saves them in the specified output directory.
|
||||
|
||||
## FAQ
|
||||
|
||||
### What dataset formats does Ultralytics YOLO support for instance segmentation?
|
||||
|
||||
Ultralytics YOLO supports several dataset formats for instance segmentation, with the primary format being its own Ultralytics YOLO format. Each image in your dataset needs a corresponding text file with object information segmented into multiple rows (one row per object), listing the class index and normalized bounding coordinates. For more detailed instructions on the YOLO dataset format, visit the [Instance Segmentation Datasets Overview](#instance-segmentation-datasets-overview).
|
||||
|
||||
### How can I convert COCO dataset annotations to the YOLO format?
|
||||
|
||||
Converting COCO format annotations to YOLO format is straightforward using Ultralytics tools. You can use the `convert_coco` function from the `ultralytics.data.converter` module:
|
||||
|
||||
```python
|
||||
from ultralytics.data.converter import convert_coco
|
||||
|
||||
convert_coco(labels_dir="path/to/coco/annotations/", use_segments=True)
|
||||
```
|
||||
|
||||
This script converts your COCO dataset annotations to the required YOLO format, making it suitable for training your YOLO models. For more details, refer to [Port or Convert Label Formats](#coco-dataset-format-to-yolo-format).
|
||||
|
||||
### How do I prepare a YAML file for training Ultralytics YOLO models?
|
||||
|
||||
To prepare a YAML file for training YOLO models with Ultralytics, you need to define the dataset paths and class names. Here's an example YAML configuration:
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
|
||||
```
|
||||
|
||||
Ensure you update the paths and class names according to your dataset. For more information, check the [Dataset YAML Format](#dataset-yaml-format) section.
|
||||
|
||||
### What is the auto-annotation feature in Ultralytics YOLO?
|
||||
|
||||
Auto-annotation in Ultralytics YOLO allows you to generate segmentation annotations for your dataset using a pretrained detection model. This significantly reduces the need for manual labeling. You can use the `auto_annotate` function as follows:
|
||||
|
||||
```python
|
||||
from ultralytics.data.annotator import auto_annotate
|
||||
|
||||
auto_annotate(data="path/to/images", det_model="yolo26x.pt", sam_model="sam_b.pt") # or sam_model="mobile_sam.pt"
|
||||
```
|
||||
|
||||
This function automates the annotation process, making it faster and more efficient. For more details, explore the [Auto-Annotate Reference](https://docs.ultralytics.com/reference/data/annotator/#ultralytics.data.annotator.auto_annotate).
|
||||
165
algorithms/dms_yolo/code/docs/en/datasets/segment/package-seg.md
Normal file
165
algorithms/dms_yolo/code/docs/en/datasets/segment/package-seg.md
Normal file
@@ -0,0 +1,165 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Package Segmentation Dataset. Optimize logistics and enhance vision models with curated images for package identification and sorting.
|
||||
keywords: Package Segmentation Dataset, computer vision, package identification, logistics, warehouse automation, segmentation models, training data, Ultralytics YOLO
|
||||
---
|
||||
|
||||
# Package Segmentation Dataset
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-package-segmentation-dataset.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Package Segmentation Dataset In Colab"></a>
|
||||
|
||||
The Package Segmentation Dataset, available on Roboflow Universe, is a curated collection of images specifically tailored for tasks related to package segmentation within the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv). This dataset is designed to assist researchers, developers, and enthusiasts working on projects involving package identification, sorting, and handling, primarily focusing on [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/im7xBCnPURg"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Package Segmentation Model using Ultralytics YOLO26 | Industrial Packages 🎉
|
||||
</p>
|
||||
|
||||
Containing a diverse set of images showcasing various packages in different contexts and environments, the dataset serves as a valuable resource for training and evaluating segmentation models. Whether you are engaged in logistics, warehouse automation, or any application requiring precise package analysis, the Package Segmentation Dataset provides a targeted and comprehensive set of images to enhance the performance of your computer vision algorithms. Explore more datasets for segmentation tasks on our [datasets overview page](https://docs.ultralytics.com/datasets/segment/).
|
||||
|
||||
## Dataset Structure
|
||||
|
||||
The distribution of data in the Package Segmentation Dataset is structured as follows:
|
||||
|
||||
- **Training set**: Encompasses 1920 images accompanied by their corresponding annotations.
|
||||
- **Testing set**: Consists of 89 images, each paired with its respective annotations.
|
||||
- **Validation set**: Comprises 188 images, each with corresponding annotations.
|
||||
|
||||
## Applications
|
||||
|
||||
Package segmentation, facilitated by the Package Segmentation Dataset, is crucial for optimizing logistics, enhancing last-mile delivery, improving manufacturing quality control, and contributing to smart city solutions. From e-commerce to security applications, this dataset is a key resource, fostering innovation in computer vision for diverse and efficient package analysis applications.
|
||||
|
||||
### Smart Warehouses and Logistics
|
||||
|
||||
In modern warehouses, [vision AI solutions](https://www.ultralytics.com/solutions) can streamline operations by automating package identification and sorting. Computer vision models trained on this dataset can quickly detect and segment packages in real-time, even in challenging environments with dim lighting or cluttered spaces. This leads to faster processing times, reduced errors, and improved overall efficiency in [logistics operations](https://www.ultralytics.com/blog/ultralytics-yolo11-the-key-to-computer-vision-in-logistics).
|
||||
|
||||
### Quality Control and Damage Detection
|
||||
|
||||
Package segmentation models can be used to identify damaged packages by analyzing their shape and appearance. By detecting irregularities or deformations in package outlines, these models help ensure that only intact packages proceed through the supply chain, reducing customer complaints and return rates. This is a key aspect of [quality control in manufacturing](https://www.ultralytics.com/blog/improving-manufacturing-with-computer-vision) and is vital for maintaining product integrity.
|
||||
|
||||
## Dataset YAML
|
||||
|
||||
A YAML (Yet Another Markup Language) file defines the dataset configuration, including paths, classes, and other essential details. For the Package Segmentation dataset, the `package-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml).
|
||||
|
||||
!!! example "ultralytics/cfg/datasets/package-seg.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/package-seg.yaml"
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
To train an [Ultralytics YOLO26n](https://docs.ultralytics.com/models/yolo26/) model on the Package Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training page](../../modes/train.md).
|
||||
|
||||
!!! example "Train Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO("yolo26n-seg.pt") # load a pretrained segmentation model (recommended for training)
|
||||
|
||||
# Train the model on the Package Segmentation dataset
|
||||
results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
|
||||
|
||||
# Validate the model
|
||||
results = model.val()
|
||||
|
||||
# Perform inference on an image
|
||||
results = model("path/to/image.jpg")
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# Load a pretrained segmentation model and start training
|
||||
yolo segment train data=package-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
|
||||
|
||||
# Resume training from the last checkpoint
|
||||
yolo segment train data=package-seg.yaml model=path/to/last.pt resume=True
|
||||
|
||||
# Validate the trained model
|
||||
yolo segment val data=package-seg.yaml model=path/to/best.pt
|
||||
|
||||
# Perform inference using the trained model
|
||||
yolo segment predict model=path/to/best.pt source=path/to/image.jpg
|
||||
```
|
||||
|
||||
## Sample Data and Annotations
|
||||
|
||||
The Package Segmentation dataset comprises a varied collection of images captured from multiple perspectives. Below are instances of data from the dataset, accompanied by their respective segmentation masks:
|
||||
|
||||
|
||||
|
||||
- This image displays an instance of package segmentation, featuring annotated masks outlining recognized package objects. The dataset incorporates a diverse collection of images taken in different locations, environments, and densities. It serves as a comprehensive resource for developing models specific to this [segmentation task](https://docs.ultralytics.com/tasks/segment/).
|
||||
- The example emphasizes the diversity and complexity present in the dataset, underscoring the significance of high-quality data for computer vision tasks involving package segmentation.
|
||||
|
||||
## Benefits of Using YOLO26 for Package Segmentation
|
||||
|
||||
[Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26/) offers several advantages for package segmentation tasks:
|
||||
|
||||
1. **Speed and Accuracy Balance**: YOLO26 achieves high precision and efficiency, making it ideal for [real-time inference](https://www.ultralytics.com/glossary/real-time-inference) in fast-paced logistics environments. It provides a strong balance compared to models like [YOLOv8](https://docs.ultralytics.com/models/yolov8/).
|
||||
|
||||
2. **Adaptability**: Models trained with YOLO26 can adapt to various warehouse conditions, from dim lighting to cluttered spaces, ensuring robust performance.
|
||||
|
||||
3. **Scalability**: During peak periods like holiday seasons, YOLO26 models can efficiently scale to handle increased package volumes without compromising performance or [accuracy](https://www.ultralytics.com/glossary/accuracy).
|
||||
|
||||
4. **Integration Capabilities**: YOLO26 can be easily integrated with existing warehouse management systems and deployed across various platforms using formats like [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/), facilitating end-to-end automated solutions.
|
||||
|
||||
## Citations and Acknowledgments
|
||||
|
||||
If you integrate the Package Segmentation dataset into your research or development initiatives, please cite the source appropriately:
|
||||
|
||||
!!! quote ""
|
||||
|
||||
=== "BibTeX"
|
||||
|
||||
```bibtex
|
||||
@misc{ factory_package_dataset,
|
||||
title = { factory_package Dataset },
|
||||
type = { Open Source Dataset },
|
||||
author = { factorypackage },
|
||||
url = { https://universe.roboflow.com/factorypackage/factory_package },
|
||||
journal = { Roboflow Universe },
|
||||
publisher = { Roboflow },
|
||||
year = { 2024 },
|
||||
month = { jan },
|
||||
note = { visited on 2024-01-24 },
|
||||
}
|
||||
```
|
||||
|
||||
We express our gratitude to the creators of the Package Segmentation dataset for their contribution to the computer vision community. For further exploration of datasets and model training, consider visiting our [Ultralytics Datasets](https://docs.ultralytics.com/datasets/) page and our guide on [model training tips](https://docs.ultralytics.com/guides/model-training-tips/).
|
||||
|
||||
## FAQ
|
||||
|
||||
### What is the Package Segmentation Dataset and how can it help in computer vision projects?
|
||||
|
||||
- The Package Segmentation Dataset is a curated collection of images tailored for tasks involving package [image segmentation](https://www.ultralytics.com/glossary/image-segmentation). It includes diverse images of packages in various contexts, making it invaluable for training and evaluating segmentation models. This dataset is particularly useful for applications in logistics, warehouse automation, and any project requiring precise package analysis.
|
||||
|
||||
### How do I train an Ultralytics YOLO26 model on the Package Segmentation Dataset?
|
||||
|
||||
- You can train an [Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26/) model using both Python and CLI methods. Use the code snippets provided in the [Usage](#usage) section. Refer to the model [Training page](../../modes/train.md) for more details on arguments and configurations.
|
||||
|
||||
### What are the components of the Package Segmentation Dataset, and how is it structured?
|
||||
|
||||
- The dataset is structured into three main components:
|
||||
- **Training set**: Contains 1920 images with annotations.
|
||||
- **Testing set**: Comprises 89 images with corresponding annotations.
|
||||
- **Validation set**: Includes 188 images with annotations.
|
||||
- This structure ensures a balanced dataset for thorough model training, validation, and testing, following best practices outlined in [model evaluation guides](https://docs.ultralytics.com/guides/model-evaluation-insights/).
|
||||
|
||||
### Why should I use Ultralytics YOLO26 with the Package Segmentation Dataset?
|
||||
|
||||
- Ultralytics YOLO26 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLO26's capabilities for precise package segmentation, which is especially beneficial for industries like [logistics](https://www.ultralytics.com/blog/ultralytics-yolo11-the-key-to-computer-vision-in-logistics) and warehouse automation.
|
||||
|
||||
### How can I access and use the package-seg.yaml file for the Package Segmentation Dataset?
|
||||
|
||||
- The `package-seg.yaml` file is hosted on Ultralytics' GitHub repository and contains essential information about the dataset's paths, classes, and configuration. You can view or download it at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml>. This file is crucial for configuring your models to utilize the dataset efficiently. For more insights and practical examples, explore our [Python Usage](https://docs.ultralytics.com/usage/python/) section.
|
||||
142
algorithms/dms_yolo/code/docs/en/datasets/track/index.md
Normal file
142
algorithms/dms_yolo/code/docs/en/datasets/track/index.md
Normal file
@@ -0,0 +1,142 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn how to use Multi-Object Tracking with YOLO. Explore dataset formats, tracking algorithms, and implementation examples using Python or CLI for real-time object tracking.
|
||||
keywords: YOLO, Multi-Object Tracking, Tracking Datasets, Python Tracking Example, CLI Tracking Example, Object Detection, Ultralytics, AI, Machine Learning, BoT-SORT, ByteTrack
|
||||
---
|
||||
|
||||
# Multi-object Tracking Datasets Overview
|
||||
|
||||
Multi-object tracking is a critical component in video analytics that identifies objects and maintains unique IDs for each detected object across video frames. Ultralytics YOLO provides powerful tracking capabilities that can be applied to various domains including surveillance, sports analytics, and traffic monitoring.
|
||||
|
||||
## Dataset Format (Coming Soon)
|
||||
|
||||
Ultralytics tracking currently reuses detection, segmentation, or pose models without requiring tracker-specific training. Native tracker-training support is under active development.
|
||||
|
||||
## Available Trackers
|
||||
|
||||
Ultralytics YOLO supports the following tracking algorithms:
|
||||
|
||||
- [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - Use `botsort.yaml` to enable this tracker (default)
|
||||
- [ByteTrack](https://github.com/FoundationVision/ByteTrack) - Use `bytetrack.yaml` to enable this tracker
|
||||
|
||||
## Usage
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo26n.pt")
|
||||
results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.1, iou=0.7, show=True)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo track model=yolo26n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.1 iou=0.7 show=True
|
||||
```
|
||||
|
||||
## Persisting Tracks Between Frames
|
||||
|
||||
For continuous tracking across video frames, you can use the `persist=True` parameter:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load the YOLO model
|
||||
model = YOLO("yolo26n.pt")
|
||||
|
||||
# Open the video file
|
||||
cap = cv2.VideoCapture("path/to/video.mp4")
|
||||
|
||||
while cap.isOpened():
|
||||
success, frame = cap.read()
|
||||
if success:
|
||||
# Run tracking with persistence between frames
|
||||
results = model.track(frame, persist=True)
|
||||
|
||||
# Visualize the results
|
||||
annotated_frame = results[0].plot()
|
||||
cv2.imshow("Tracking", annotated_frame)
|
||||
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
break
|
||||
else:
|
||||
break
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### How do I use Multi-Object Tracking with Ultralytics YOLO?
|
||||
|
||||
To use Multi-Object Tracking with Ultralytics YOLO, you can start by using the Python or CLI examples provided. Here is how you can get started:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo26n.pt") # Load the YOLO26 model
|
||||
results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.1, iou=0.7, show=True)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
yolo track model=yolo26n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.1 iou=0.7 show=True
|
||||
```
|
||||
|
||||
These commands load the YOLO26 model and use it for tracking objects in the given video source with specific confidence (`conf`) and [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (`iou`) thresholds. For more details, refer to the [track mode documentation](../../modes/track.md).
|
||||
|
||||
### What are the upcoming features for training trackers in Ultralytics?
|
||||
|
||||
Ultralytics is continuously enhancing its AI models. An upcoming feature will enable the training of standalone trackers. Until then, Multi-Object Detector leverages pretrained detection, segmentation, or Pose models for tracking without requiring standalone training. Stay updated by following our [blog](https://www.ultralytics.com/blog) or checking the [upcoming features](../../reference/trackers/track.md).
|
||||
|
||||
### Why should I use Ultralytics YOLO for multi-object tracking?
|
||||
|
||||
Ultralytics YOLO is a state-of-the-art [object detection](https://www.ultralytics.com/glossary/object-detection) model known for its real-time performance and high [accuracy](https://www.ultralytics.com/glossary/accuracy). Using YOLO for multi-object tracking provides several advantages:
|
||||
|
||||
- **Real-time tracking:** Achieve efficient and high-speed tracking ideal for dynamic environments.
|
||||
- **Flexibility with pretrained models:** No need to train from scratch; simply use pretrained detection, segmentation, or Pose models.
|
||||
- **Ease of use:** Simple API integration with both Python and CLI makes setting up tracking pipelines straightforward.
|
||||
- **Extensive documentation and community support:** Ultralytics provides comprehensive documentation and an active community forum to troubleshoot issues and enhance your tracking models.
|
||||
|
||||
For more details on setting up and using YOLO for tracking, visit our [track usage guide](../../modes/track.md).
|
||||
|
||||
### Can I use custom datasets for multi-object tracking with Ultralytics YOLO?
|
||||
|
||||
Yes, you can use custom datasets for multi-object tracking with Ultralytics YOLO. While support for standalone tracker training is an upcoming feature, you can already use pretrained models on your custom datasets. Prepare your datasets in the appropriate format compatible with YOLO and follow the documentation to integrate them.
|
||||
|
||||
### How do I interpret the results from the Ultralytics YOLO tracking model?
|
||||
|
||||
After running a tracking job with Ultralytics YOLO, the results include various data points such as tracked object IDs, their bounding boxes, and the confidence scores. Here's a brief overview of how to interpret these results:
|
||||
|
||||
- **Tracked IDs:** Each object is assigned a unique ID, which helps in tracking it across frames.
|
||||
- **Bounding boxes:** These indicate the location of tracked objects within the frame.
|
||||
- **Confidence scores:** These reflect the model's confidence in detecting the tracked object.
|
||||
|
||||
For detailed guidance on interpreting and visualizing these results, refer to the [results handling guide](../../reference/engine/results.md).
|
||||
|
||||
### How can I customize the tracker configuration?
|
||||
|
||||
You can customize the tracker by creating a modified version of the tracker configuration file. Copy an existing tracker config file from [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers), modify the parameters as needed, and specify this file when running the tracker:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO("yolo26n.pt")
|
||||
results = model.track(source="video.mp4", tracker="custom_tracker.yaml")
|
||||
```
|
||||
Reference in New Issue
Block a user