单目3D初始代码
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docs/en/platform/data/index.md
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description: Learn about data management in Ultralytics Platform including dataset upload, annotation tools, and statistics visualization for YOLO model training.
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keywords: Ultralytics Platform, data management, datasets, annotation, YOLO, computer vision, data preparation, labeling
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---
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# Data Preparation
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Data preparation is the foundation of successful [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models. [Ultralytics Platform](https://platform.ultralytics.com) provides comprehensive tools for managing your training data, from upload through annotation to analysis.
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## Overview
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The Data section of Ultralytics Platform helps you:
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- **Upload** images, videos, and archives (ZIP, TAR, GZ)
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- **Annotate** with manual drawing tools and SAM-powered smart labeling
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- **Analyze** your data with statistics and visualizations
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- **Export** in [NDJSON format](../../datasets/detect/index.md#ultralytics-ndjson-format) for local training
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## Workflow
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```mermaid
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graph LR
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A[Upload] --> B[Annotate]
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B --> C[Analyze]
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C --> D[Train]
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style A fill:#4CAF50,color:#fff
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style B fill:#2196F3,color:#fff
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style C fill:#FF9800,color:#fff
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style D fill:#9C27B0,color:#fff
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```
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| Stage | Description |
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| ------------ | ----------------------------------------------------------------------------------------------------- |
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| **Upload** | Import images, videos, or archives with automatic processing |
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| **Annotate** | Label data with bounding boxes, polygons, keypoints, or classifications |
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| **Analyze** | View class distributions, spatial heatmaps, and dimension statistics |
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| **Export** | Download in [NDJSON format](../../datasets/detect/index.md#ultralytics-ndjson-format) for offline use |
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## Supported Tasks
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Ultralytics Platform supports all 5 YOLO task types:
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| Task | Description | Annotation Tool |
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| ------------------------------------------------ | ------------------------------------------- | ----------------- |
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| **[Detect](../../datasets/detect/index.md)** | Object detection with bounding boxes | Rectangle tool |
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| **[Segment](../../datasets/segment/index.md)** | Instance segmentation with pixel masks | Polygon tool |
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| **[Pose](../../datasets/pose/index.md)** | Keypoint estimation (17-point COCO format) | Keypoint tool |
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| **[OBB](../../datasets/obb/index.md)** | Oriented bounding boxes for rotated objects | Oriented box tool |
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| **[Classify](../../datasets/classify/index.md)** | Image-level classification | Class selector |
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!!! info "Task Type Selection"
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The task type is set when creating a dataset and determines which annotation tools are available. You can change it later from the dataset settings, but incompatible annotations won't be displayed after switching.
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## Key Features
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### Smart Storage
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Ultralytics Platform uses Content-Addressable Storage (CAS) for efficient data management:
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- **Deduplication**: Identical images stored only once via XXH3-128 hashing
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- **Integrity**: Hash-based addressing ensures data integrity
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- **Efficiency**: Optimized storage and fast processing
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### Dataset URIs
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Reference datasets using the `ul://` URI format (see [Using Platform Datasets](../api/index.md#using-platform-datasets)):
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```bash
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yolo train data=ul://username/datasets/my-dataset
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```
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This allows training on the platform's datasets from any machine with your [API key](../account/api-keys.md) configured.
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!!! example "Use Platform Data from Python"
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```python
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from ultralytics import YOLO
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model = YOLO("yolo26n.pt")
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model.train(data="ul://username/datasets/my-dataset", epochs=100)
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```
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### Dataset Tabs
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Every dataset page provides five tabs:
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| Tab | Description |
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| ----------- | ---------------------------------------------------------------------------- |
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| **Images** | Browse images in grid, compact, or table view with annotation overlays |
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| **Classes** | View and edit class names, colors, and label counts per class |
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| **Charts** | Automatic statistics: split distribution, class counts, heatmaps |
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| **Models** | [Models](../train/models.md) trained on this dataset with metrics and status |
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| **Errors** | Images that failed processing with error details and fix guidance |
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### Statistics and Visualization
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The `Charts` tab provides automatic analysis including:
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- **Split Distribution**: Donut chart of train/val/test image counts
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- **Top Classes**: Donut chart of most frequent annotation classes
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- **Image Widths**: Histogram of image width distribution
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- **Image Heights**: Histogram of image height distribution
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- **Points per Instance**: Polygon vertex or keypoint count distribution (segment/pose datasets)
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- **Annotation Locations**: 2D heatmap of bounding box center positions
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- **Image Dimensions**: 2D heatmap of width vs height with aspect ratio guide lines
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## Quick Links
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- [**Datasets**](datasets.md): Upload and manage your training data
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- [**Annotation**](annotation.md): Label data with manual and AI-assisted tools
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## FAQ
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### What file formats are supported for upload?
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Ultralytics Platform supports:
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**Images:** JPEG, PNG, WebP, BMP, TIFF, HEIC, AVIF, JP2, DNG, MPO (max 50MB each)
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**Videos:** MP4, WebM, MOV, AVI, MKV, M4V (max 1GB, frames extracted at 1 FPS, max 100 frames)
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**Archives:** ZIP, TAR, TAR.GZ, TGZ, GZ (max 10GB) containing images with optional [YOLO-format labels](../../datasets/detect/index.md#ultralytics-yolo-format)
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### What is the maximum dataset size?
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Storage limits depend on your plan:
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| Plan | Storage Limit |
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| ---------- | ------------- |
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| Free | 100 GB |
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| Pro | 500 GB |
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| Enterprise | Custom |
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Individual file limits: Images 50MB, Videos 1GB, Archives 10GB
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### Can I use my Platform datasets for local training?
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Yes! Use the dataset URI format to train locally:
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=== "CLI"
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```bash
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export ULTRALYTICS_API_KEY="your_key"
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yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset epochs=100
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```
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=== "Python"
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```python
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import os
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os.environ["ULTRALYTICS_API_KEY"] = "your_key"
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from ultralytics import YOLO
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model = YOLO("yolo26n.pt")
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model.train(data="ul://username/datasets/my-dataset", epochs=100)
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```
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Or export your dataset in [NDJSON format](../../datasets/detect/index.md#ultralytics-ndjson-format) for fully offline training.
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