mmyolo - OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.

 

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📘Documentation | 🛠️Installation | 👀Model Zoo | 🆕Update News | 🤔Reporting Issues

Introduction

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

The master branch works with PyTorch 1.6+.

Major features

• Unified and convenient benchmark

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

• Rich and detailed documentation

  MMYOLO provides rich documentation for getting started, model deployment, advanced usages, and algorithm analysis, making it easy for users at different levels to get started and make extensions quickly.

• Modular Design

  MMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies.

The figure above is contributed by RangeKing@GitHub, thank you very much!

And the figure of P6 model is in model_design.md.

What's New

Highlight

We are excited to announce our latest work on real-time object recognition tasks, RTMDet, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the technical report. Pre-trained models are here.

MMYOLO currently only implements the object detection algorithm, but it has a significant training acceleration compared to the MMDeteciton version. The training speed is 2.6 times faster than the previous version.

💎 v0.3.0 was released on 8/1/2023:

1. Implement fast version of RTMDet. RTMDet-s 8xA100 training takes only 14 hours. The training speed is 2.6 times faster than the previous version.
2. Support PPYOLOE training
3. Support iscrowd attribute training in YOLOv5
4. Support YOLOv5 assigner result visualization

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>=0.3.1"
mim install "mmcv>=2.0.0rc1,<2.1.0"
mim install "mmdet>=3.0.0rc5,<3.1.0"
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
# Install albumentations
pip install -r requirements/albu.txt
# Install MMYOLO
mim install -v -e .

Tutorial

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

The usage of MMYOLO 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 parts from 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
    • Custom Dataset
    • 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

• Deployment Guides

  • Basic Deployment Guide
    • Basic Deployment Guide
  • Deployment Tutorial
    • YOLOv5 Deployment

• Advanced Guides

  • Data flow
  • How to
  • Plugins

Overview of Benchmark and Model Zoo

Results and models are available in the model zoo.

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.
• MMEval: OpenMMLab machine learning evaluation library.