---
comments: true
description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics.
keywords: Ultralytics YOLO, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, SAM, YOLO-NAS, RT-DETR, object detection, instance segmentation, detection transformers, real-time detection, computer vision, CLI, Python
---

# Models

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.

In this documentation, we provide information on four major models:

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](./yolov3.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 tradeoffs compared to previous versions.
4. [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.
5. [YOLOv7](./yolov7.md): Updated YOLO models released in 2022 by the authors of YOLOv4.
6. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
7. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
7. [Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md): MobileSAM for mobile applications by Kyung Hee University.
8. [Fast Segment Anything Model (FastSAM)](./fast-sam.md): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
9. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
10. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.

You can use many of 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:

## CLI Example

Use the `model` argument to pass a model YAML such as `model=yolov8n.yaml` or a pretrained *.pt file such as `model=yolov8n.pt`

```bash
yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml epochs=100
```

## Python Example

PyTorch pretrained models as well as model YAML files can also be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in python:

```python
from ultralytics import YOLO

model = YOLO("yolov8n.pt")  # load a pretrained YOLOv8n model

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.