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37 lines
1.7 KiB
37 lines
1.7 KiB
--- |
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comments: true |
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description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics. |
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--- |
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# Models |
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Ultralytics supports many models and architectures with more to come in the future. Want to add your model architecture? [Here's](../help/contributing.md) how you can contribute. |
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In this documentation, we provide information on four major models: |
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1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities. |
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2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions. |
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3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification. |
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4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM). |
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5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model. |
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You can use these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python: |
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## CLI Example |
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```bash |
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yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100 |
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``` |
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## Python Example |
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```python |
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from ultralytics import YOLO |
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model = YOLO("model.yaml") # build a YOLOv8n model from scratch |
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# YOLO("model.pt") use pre-trained model if available |
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model.info() # display model information |
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model.train(data="coco128.yaml", epochs=100) # train the model |
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``` |
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For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above. |