# Source: https://raw.githubusercontent.com/ultralytics/ultralytics/main/docs/en/models/index.md

---
comments: true
description: Discover a variety of models supported by Ultralytics, including YOLOv3 to YOLO11, NAS, SAM, and RT-DETR for detection, segmentation, and more.
keywords: Ultralytics, supported models, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLO11, SAM, SAM2, SAM3, MobileSAM, FastSAM, YOLO-NAS, RT-DETR, YOLO-World, object detection, image segmentation, classification, pose estimation, multi-object tracking
---

# 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).

![Ultralytics YOLO11 Comparison Plots](https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png)

## 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://www.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. Only inference is supported.
6. **[YOLOv8](yolov8.md)**: A versatile model featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/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. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
9. **[YOLO11](yolo11.md) 🚀**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks including detection, segmentation, pose estimation, tracking, and classification.
10. **[YOLO26](yolo26.md) ⚠️ Coming Soon**: Ultralytics' next-generation YOLO model optimized for edge deployment with end-to-end NMS-free inference.
11. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
12. **[Segment Anything Model 2 (SAM2)](sam-2.md)**: The next generation of Meta's Segment Anything Model for videos and images.
13. **[Segment Anything Model 3 (SAM3)](sam-3.md) 🚀 NEW**: Meta's third generation Segment Anything Model with Promptable Concept Segmentation for text and image exemplar-based segmentation.
14. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
15. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
16. **[YOLO-NAS](yolo-nas.md)**: YOLO [Neural Architecture Search](https://www.ultralytics.com/glossary/neural-architecture-search-nas) (NAS) Models.
17. **[Real-Time Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle real-time Detection [Transformer](https://www.ultralytics.com/glossary/transformer) (RT-DETR) models.
18. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
19. **[YOLOE](yoloe.md)**: An improved open-vocabulary object detector that maintains YOLO's real-time performance while detecting arbitrary classes beyond its training data.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/MWq1UxqTClU?si=nHAW-lYDzrz68jR0"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Run Ultralytics YOLO models in just a few lines of code.
</p>

## 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 spotlights YOLO11 [Detect](../tasks/detect.md) models for [object detection](https://www.ultralytics.com/glossary/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](https://www.ultralytics.com/glossary/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 YOLO11n model
        model = YOLO("yolo11n.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 YOLO11n 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 YOLO11n model and train it on the COCO8 example dataset for 100 epochs
        yolo train model=yolo11n.pt data=coco8.yaml epochs=100 imgsz=640

        # Load a COCO-pretrained YOLO11n model and run inference on the 'bus.jpg' image
        yolo predict model=yolo11n.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).

## FAQ

### What are the key advantages of using Ultralytics YOLO11 for object detection?

Ultralytics YOLO11 offers enhanced capabilities such as real-time object detection, instance segmentation, pose estimation, and classification. Its optimized architecture ensures high-speed performance without sacrificing [accuracy](https://www.ultralytics.com/glossary/accuracy), making it ideal for a variety of applications across diverse AI domains. YOLO11 builds on previous versions with improved performance and additional features, as detailed on the [YOLO11 documentation page](../models/yolo11.md).

### How can I train a YOLO model on custom data?

Training a YOLO model on custom data can be easily accomplished using Ultralytics' libraries. Here's a quick example:

!!! example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a YOLO model
        model = YOLO("yolo11n.pt")  # or any other YOLO model

        # Train the model on custom dataset
        results = model.train(data="custom_data.yaml", epochs=100, imgsz=640)
        ```

    === "CLI"

        ```bash
        yolo train model=yolo11n.pt data='custom_data.yaml' epochs=100 imgsz=640
        ```

For more detailed instructions, visit the [Train](../modes/train.md) documentation page.

### Which YOLO versions are supported by Ultralytics?

Ultralytics supports a comprehensive range of YOLO (You Only Look Once) versions from YOLOv3 to YOLO11, along with models like YOLO-NAS, SAM, and RT-DETR. Each version is optimized for various tasks such as detection, segmentation, and classification. For detailed information on each model, refer to the [Models Supported by Ultralytics](../models/index.md) documentation.

### Why should I use Ultralytics HUB for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) projects?

[Ultralytics HUB](../hub/index.md) provides a no-code, end-to-end platform for training, deploying, and managing YOLO models. It simplifies complex workflows, enabling users to focus on model performance and application. The HUB also offers [cloud training capabilities](../hub/cloud-training.md), comprehensive dataset management, and user-friendly interfaces for both beginners and experienced developers.

### What types of tasks can YOLO11 perform, and how does it compare to other YOLO versions?

YOLO11 is a versatile model capable of performing tasks including object detection, instance segmentation, classification, and pose estimation. Compared to earlier versions, YOLO11 offers significant improvements in speed and accuracy due to its optimized architecture and anchor-free design. For a deeper comparison, refer to the [YOLO11 documentation](../models/yolo11.md) and the [Task pages](../tasks/index.md) for more details on specific tasks.