---
comments: true
description: Learn how to crop and extract objects using Ultralytics YOLO26 for focused analysis, reduced data volume, and enhanced precision.
keywords: Ultralytics, YOLO26, object cropping, object detection, image processing, video analysis, AI, machine learning
---

# Object Cropping using Ultralytics YOLO26

## What is Object Cropping?

Object cropping with [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics/) involves isolating and extracting specific detected objects from an image or video. The YOLO26 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/J1BaCqytBmA"
    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> Object Cropping using Ultralytics YOLO
</p>

## Advantages of Object Cropping

- **Focused Analysis**: YOLO26 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
- **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
- **Enhanced Precision**: YOLO26's [object detection](https://www.ultralytics.com/glossary/object-detection) [accuracy](https://www.ultralytics.com/glossary/accuracy) ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.

## Visuals

|                                                                                 Airport Luggage                                                                                 |
| :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ![Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLO26](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/suitcases-cropping-airport-conveyor-belt.avif) |
|                                                      Suitcases Cropping at airport conveyor belt using Ultralytics YOLO26                                                       |

!!! example "Object Cropping using Ultralytics YOLO"

    === "CLI"

        ```bash
        # Crop the objects
        yolo solutions crop show=True

        # Pass a source video
        yolo solutions crop source="path/to/video.mp4"

        # Crop specific classes
        yolo solutions crop classes="[0, 2]"
        ```

    === "Python"

        ```python
        import cv2

        from ultralytics import solutions

        cap = cv2.VideoCapture("path/to/video.mp4")
        assert cap.isOpened(), "Error reading video file"

        # Initialize object cropper
        cropper = solutions.ObjectCropper(
            show=True,  # display the output
            model="yolo26n.pt",  # model for object cropping, e.g., yolo26x.pt.
            classes=[0, 2],  # crop specific classes such as person and car with the COCO pretrained model.
            # conf=0.5,  # adjust confidence threshold for the objects.
            # crop_dir="cropped-detections",  # set the directory name for cropped detections
        )

        # Process video
        while cap.isOpened():
            success, im0 = cap.read()

            if not success:
                print("Video frame is empty or processing is complete.")
                break

            results = cropper(im0)

            # print(results)  # access the output

        cap.release()
        cv2.destroyAllWindows()  # destroy all opened windows
        ```

        When you provide the optional `crop_dir` argument, every cropped object is written to that folder with filenames that include the source image name and class. This makes it easy to inspect detections or build downstream datasets without writing extra code.

### `ObjectCropper` Arguments

Here's a table with the `ObjectCropper` arguments:

{% from "macros/solutions-args.md" import param_table %}
{{ param_table(["model", "crop_dir"]) }}

Moreover, the following visualization arguments are available for use:

{% from "macros/visualization-args.md" import param_table %}
{{ param_table(["show", "line_width"]) }}

## FAQ

### What is object cropping in Ultralytics YOLO26 and how does it work?

Object cropping using [Ultralytics YOLO26](https://github.com/ultralytics/ultralytics) involves isolating and extracting specific objects from an image or video based on YOLO26's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced [precision](https://www.ultralytics.com/glossary/precision) by leveraging YOLO26 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the [object cropping example](#object-cropping-using-ultralytics-yolo26).

### Why should I use Ultralytics YOLO26 for object cropping over other solutions?

Ultralytics YOLO26 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for [focused analysis](#advantages-of-object-cropping) and applications needing high data integrity. Moreover, YOLO26 integrates seamlessly with tools like [OpenVINO](../integrations/openvino.md) and [TensorRT](../integrations/tensorrt.md) for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the [guide on model export](../modes/export.md).

### How can I reduce the data volume of my dataset using object cropping?

By using Ultralytics YOLO26 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLO26's capabilities, visit our [quickstart guide](../quickstart.md).

### Can I use Ultralytics YOLO26 for real-time video analysis and object cropping?

Yes, Ultralytics YOLO26 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as [surveillance](security-alarm-system.md), sports analysis, and automated inspection systems. Check out the [tracking](../modes/track.md) and [prediction modes](../modes/predict.md) to understand how to implement real-time processing.

### What are the hardware requirements for efficiently running YOLO26 for object cropping?

Ultralytics YOLO26 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using [CoreML](../integrations/coreml.md) for iOS or [TFLite](../integrations/tflite.md) for Android. More details on supported devices and formats can be found in our [model deployment options](../guides/model-deployment-options.md).
