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Introduction

MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.6+.

Major features

• Fair and convenient algorithm evaluation

  MMYOLO unifies the modules of various YOLO algorithms and provides a unified benchmark process. Users can compare and analyze in a fair and convenient way.

• Detailed introductory and advanced documentation

  MMYOLO provides a series of documents from getting started, to model deployment, advanced guidelines, and algorithm analysis, making it easy for different users to get started and make extensions quickly.

• Modular Design

  MMYOLO decompose the framework into different components and users can easily construct a customized model by combining different modules and training and testing strategies.

The picture is provided by RangeKing@GitHub, thank you very much!

What's New

v0.1.0 was released on 21/9/2022:

• Unified component interfaces based on OpenMMLab 2.0 and MMDetection 3.0
• Support for YOLOv5/YOLOX training and deployment, support for YOLOv6 inference and deployment
• Refactored YOLOX for MMDetection to provide faster training and inference
• Detailed introductory and advanced tutorials are provided, see the English tutorial

For release history and update details, please refer to changelog.

Installation

MMYOLO relies on PyTorch, MMCV, MMEngine, and MMDetection. Below are quick steps for installation. Please refer to the Install Guide for more detailed instructions.

conda create -n open-mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate open-mmlab
pip install openmim
mim install mmengine
mim install "mmcv>=2.0.0rc1"
mim install "mmdet>=3.0.0rc0"
# for albumentations
pip install -r requirements/albu.txt
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
mim install -e .

Tutorial

MMYOLO is based on the MMDetection and uses the same code organization and design approach. To get better use of this, please read MMDetection Overview for the first understanding of MMDetection.

MMYOLO usage is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn about MMDetection User Guide and Advanced Guide.

For different sections than MMDetection, we have also prepared user guides and advanced guides, please read our documentation.

• User Guides

  • Train & Test
    • Learn about Configs with YOLOv5
  • From getting started to deployment
    • From getting started to deployment with YOLOv5
  • Useful Tools
    • Visualization
    • Useful Tools

• Algorithm description

  • Essential Basics
    • Model design-related instructions
  • Algorithm principles and implementation
    • Algorithm principles and implementation with YOLOv5

• Advanced Guides

  • How to

Overview of Benchmark and Model Zoo

Results and models are available in the model zoo.

Supported Algorithms

• YOLOv5
• YOLOX
• YOLOv6(Inference only)

Module Components

Backbones	Necks	Loss	Common
YOLOv5CSPDarknet YOLOXCSPDarknet EfficientRep	YOLOv5PAFPN YOLOv6RepPAFPN YOLOXPAFPN	IoULoss

FAQ

Please refer to the FAQ for frequently asked questions.

Contributing

We appreciate all contributions to improving MMYOLO. Ongoing projects can be found in our GitHub Projects. Welcome community users to participate in these projects. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

MMYOLO is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors.

Citation

If you find this project useful in your research, please consider cite:

@misc{mmyolo2022,
    title={{MMYOLO: OpenMMLab YOLO} series toolbox and benchmark},
    author={MMYOLO Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmyolo}},
    year={2022}
}

License

This project is released under the GPL 3.0 license.

Projects in OpenMMLab

• MMEngine: OpenMMLab foundational library for training deep learning models.
• MMCV: OpenMMLab foundational library for computer vision.
• MIM: MIM installs OpenMMLab packages.
• MMClassification: OpenMMLab image classification toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
• MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
• MMPose: OpenMMLab pose estimation toolbox and benchmark.
• MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
• MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
• MMRazor: OpenMMLab model compression toolbox and benchmark.
• MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMFlow: OpenMMLab optical flow toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.