--- comments: true description: Explore the diverse range of YOLO family, SAM, MobileSAM, FastSAM, YOLO-NAS, YOLO-World and RT-DETR models supported by Ultralytics. Get started with examples for both CLI and Python usage. keywords: Ultralytics, documentation, YOLO, SAM, MobileSAM, FastSAM, YOLO-NAS, RT-DETR, YOLO-World, models, architectures, Python, CLI --- # Models Supported by Ultralytics 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). ## Featured Models Here are some of the key models supported: 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. 2. **[YOLOv4](yolov4.md)**: A darknet-native update to YOLOv3, released by Alexey Bochkovskiy in 2020. 3. **[YOLOv5](yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions. 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. 5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4. 6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification. 7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI). 8. **[Segment Anything Model (SAM)](sam.md)**: Meta's Segment Anything Model (SAM). 9. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University. 10. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences. 11. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models. 12. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models. 13. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.



Watch: Run Ultralytics YOLO models in just a few lines of code.

## Getting Started: Usage Examples 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. 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. !!! Example === "Python" 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: ```python from ultralytics import YOLO # Load a COCO-pretrained YOLOv8n model model = YOLO('yolov8n.pt') # Display model information (optional) model.info() # Train the model on the COCO8 example dataset for 100 epochs results = model.train(data='coco8.yaml', epochs=100, imgsz=640) # Run inference with the YOLOv8n model on the 'bus.jpg' image results = model('path/to/bus.jpg') ``` === "CLI" CLI commands are available to directly run the models: ```bash # Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640 # Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image yolo predict model=yolov8n.pt source=path/to/bus.jpg ``` ## Contributing New Models Interested in contributing your model to Ultralytics? Great! We're always open to expanding our model portfolio. 1. **Fork the Repository**: Start by forking the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics). 2. **Clone Your Fork**: Clone your fork to your local machine and create a new branch to work on. 3. **Implement Your Model**: Add your model following the coding standards and guidelines provided in our [Contributing Guide](../help/contributing.md). 4. **Test Thoroughly**: Make sure to test your model rigorously, both in isolation and as part of the pipeline. 5. **Create a Pull Request**: Once you're satisfied with your model, create a pull request to the main repository for review. 6. **Code Review & Merging**: After review, if your model meets our criteria, it will be merged into the main repository. For detailed steps, consult our [Contributing Guide](../help/contributing.md).