@ -105,7 +105,7 @@ YOLOv8 benchmarks below were run by the Ultralytics team on 4 different model fo
The benchmarking results below are for reference and might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run.
All benchmarks run with `openvino`python package version [2023.0.1](https://pypi.org/project/openvino/2023.0.1/).
All benchmarks run with `openvino`Python package version [2023.0.1](https://pypi.org/project/openvino/2023.0.1/).
description: Learn about the YOLO family, SAM, MobileSAM, FastSAM, YOLO-NAS, and RT-DETR models supported by Ultralytics, with examples on how to use them via CLI and Python.
description: Explore the diverse range of YOLO family, SAM, MobileSAM, FastSAM, YOLO-NAS, and RT-DETR models supported by Ultralytics. Get started with examples for both CLI and Python usage.
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.
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).
In this documentation, we provide information on four major models:
## Featured 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).
8. [Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md): MobileSAM for mobile applications by Kyung Hee University.
9. [Fast Segment Anything Model (FastSAM)](./fast-sam.md): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
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:
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)**: 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).
8. **[Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
9. **[Fast Segment Anything Model (FastSAM)](./fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
<strong>Watch:</strong> Run Ultralytics YOLO models in just a few lines of code.
</p>
This example provides simple inference code for YOLO, SAM and RTDETR models. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using models with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
## Getting Started: Usage Examples
!!! 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:
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
@ -62,4 +71,20 @@ This example provides simple inference code for YOLO, SAM and RTDETR models. For
For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
## 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).
Ultralytics YOLOv8 is not just another object detection model; it's a versatile framework designed to cover the entire lifecycle of machine learning models—from data ingestion and model training to validation, deployment, and real-world tracking. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine executors. Let's take a look at the Trainer engine.
Both the Ultralytics YOLO command-line and Python interfaces are simply a high-level abstraction on the base engine executors. Let's take a look at the Trainer engine.
Welcome to the Ultralytics' YOLOv5 🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.
Welcome to the Ultralytics' [YOLOv5](https://github.com/ultralytics/yolov5) 🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.
<br><br>
Built on PyTorch, this powerful deep learning framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your computer vision projects. Let's get started!
@ -67,24 +67,17 @@ This badge signifies that all [YOLOv5 GitHub Actions](https://github.com/ultraly
@ -14,7 +14,7 @@ You can now use Roboflow to organize, label, prepare, version, and host your dat
## Upload
You can upload your data to Roboflow via [web UI](https://docs.roboflow.com/adding-data), [rest API](https://docs.roboflow.com/adding-data/upload-api), or [python](https://docs.roboflow.com/python).
You can upload your data to Roboflow via [web UI](https://docs.roboflow.com/adding-data), [REST API](https://docs.roboflow.com/adding-data/upload-api), or [Python](https://docs.roboflow.com/python).
Glenn Jocher
1 year ago
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