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Add Ultralytics tasks and YOLO-NAS models (#2735)

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docs/models/index.md
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@ -12,9 +12,10 @@ In this documentation, we provide information on four major models:
1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
3. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model.
4. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
5. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
6. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
7. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
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:
@ -35,4 +36,4 @@ model.info() # display model information
model.train(data="coco128.yaml", epochs=100) # train the model
```
For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.