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5.3 KiB
94 lines
5.3 KiB
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comments: true |
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description: Explore the diverse range of YOLO family, SAM, MobileSAM, FastSAM, YOLO-NAS, and RT-DETR models supported by Ultralytics. Get started with examples for both CLI and Python usage. |
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keywords: Ultralytics, documentation, YOLO, SAM, MobileSAM, FastSAM, YOLO-NAS, RT-DETR, models, architectures, Python, CLI |
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--- |
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# Models Supported by Ultralytics |
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Welcome to Ultralytics' model documentation! We offer support for a wide range of models, each tailored to specific tasks like [object detection](../tasks/detect.md), [instance segmentation](../tasks/segment.md), [image classification](../tasks/classify.md), [pose estimation](../tasks/pose.md), and [multi-object tracking](../modes/track.md). If you're interested in contributing your model architecture to Ultralytics, check out our [Contributing Guide](../help/contributing.md). |
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## Featured Models |
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Here are some of the key models supported: |
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1. **[YOLOv3](yolov3.md)**: The third iteration of the YOLO model family, originally by Joseph Redmon, known for its efficient real-time object detection capabilities. |
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2. **[YOLOv4](yolov4.md)**: A darknet-native update to YOLOv3, released by Alexey Bochkovskiy in 2020. |
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3. **[YOLOv5](yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions. |
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4. **[YOLOv6](yolov6.md)**: Released by [Meituan](https://about.meituan.com/) in 2022, and in use in many of the company's autonomous delivery robots. |
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5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4. |
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6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification. |
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7. **[Segment Anything Model (SAM)](sam.md)**: Meta's Segment Anything Model (SAM). |
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8. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University. |
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9. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences. |
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10. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models. |
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11. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models. |
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<p align="center"> |
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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/MWq1UxqTClU?si=nHAW-lYDzrz68jR0" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Run Ultralytics YOLO models in just a few lines of code. |
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</p> |
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## Getting Started: Usage Examples |
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This example provides simple YOLO training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages. |
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Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md) and [Pose](../tasks/pose.md) docs. |
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!!! Example |
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=== "Python" |
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PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in Python: |
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```python |
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from ultralytics import YOLO |
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# Load a COCO-pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Display model information (optional) |
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model.info() |
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# Train the model on the COCO8 example dataset for 100 epochs |
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results = model.train(data='coco8.yaml', epochs=100, imgsz=640) |
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# Run inference with the YOLOv8n model on the 'bus.jpg' image |
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results = model('path/to/bus.jpg') |
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``` |
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=== "CLI" |
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CLI commands are available to directly run the models: |
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```bash |
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# Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs |
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yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640 |
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# Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image |
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yolo predict model=yolov8n.pt source=path/to/bus.jpg |
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``` |
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## Contributing New Models |
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Interested in contributing your model to Ultralytics? Great! We're always open to expanding our model portfolio. |
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1. **Fork the Repository**: Start by forking the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics). |
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2. **Clone Your Fork**: Clone your fork to your local machine and create a new branch to work on. |
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3. **Implement Your Model**: Add your model following the coding standards and guidelines provided in our [Contributing Guide](../help/contributing.md). |
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4. **Test Thoroughly**: Make sure to test your model rigorously, both in isolation and as part of the pipeline. |
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5. **Create a Pull Request**: Once you're satisfied with your model, create a pull request to the main repository for review. |
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6. **Code Review & Merging**: After review, if your model meets our criteria, it will be merged into the main repository. |
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For detailed steps, consult our [Contributing Guide](../help/contributing.md).
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