Project Introduction to MMYOLO
MMYOLO is an innovative open-source toolbox designed for the YOLO series of algorithms, built on top of PyTorch and the MMDetection library. As part of the OpenMMLab project, it provides a powerful platform for those interested in real-time object detection tasks. This guide aims to provide an in-depth understanding of MMYOLO's features, installation process, and usage.
What's New
MMYOLO has recently released version 0.6.0, bringing several exciting updates:
- Support for YOLOv5 instance segmentation.
- Integration of YOLOX-Pose based on MMPose.
- A quick-start guide for instance segmentation in just 15 minutes.
- Enhanced YOLOv5 with mask annotation support.
- Comprehensive documentation for multi-scale training and testing.
They also introduced RTMDet, a modern family of fully convolutional single-stage detectors, praised for its excellent parameter-accuracy balance and state-of-the-art performance in instance segmentation and rotated object detection.
Major Features
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Unified and Convenient Benchmark: MMYOLO provides a standardized benchmark for various YOLO algorithms, simplifying comparisons and analyses.
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Comprehensive Documentation: It offers extensive documentation covering everything from getting started to advanced usage and algorithm analysis.
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Modular Design: The framework is divided into components, easing customization and development of models with various training and testing strategies.
Installation
To get started with MMYOLO, you'll need PyTorch, MMCV, MMEngine, and MMDetection. Here's a quick installation guide:
conda create -n mmyolo python=3.8 pytorch=1.10.1 torchvision=0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate mmyolo
pip install openmim
mim install "mmengine>=0.6.0"
mim install "mmcv>=2.0.0rc4,<2.1.0"
mim install "mmdet>=3.0.0,<4.0.0"
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
pip install -r requirements/albu.txt
mim install -v -e .
For detailed instructions, refer to the Install Guide.
Tutorial
MMYOLO adopts a design and structure similar to MMDetection, ensuring easy usage for those familiar with it. Refer to the MMDetection Overview to understand the foundational concepts. Additional tutorials available include:
- Object detection and instance segmentation in 15 minutes.
- Tutorial on training and testing tricks.
- Guidelines on replacing the backbone network.
- Details on deploying models.
Benchmark and Model Zoo
MMYOLO supports a variety of tasks and algorithms, with extensive results and pre-trained models available in the model zoo. Supported tasks include object detection and rotated object detection. The platform also caters to several popular datasets like COCO, VOC, CrowdHuman, and DOTA 1.0.
Contributing
The project welcomes contributions from developers and researchers. Community engagement and participation in ongoing projects can be found on their GitHub Projects. Consult the CONTRIBUTING.md for more details on how to contribute.
Conclusion
MMYOLO stands out as a versatile and robust toolbox for those working with YOLO algorithms, offering a wealth of features, comprehensive documentation, and an expansive community. Its modular architecture allows for tailored solutions, making it an exceptional choice for professionals in object detection and related fields.