• MindYOLO
• • What is New
  • Benchmark and Model Zoo
  • Installation
  • • Dependency
  • Getting Started
  • Learn More about MindYOLO
  • Notes
  • • How to Contribute
    • License
    • Acknowledgement
    • Citation

MindYOLO

MindYOLO is MindSpore Lab's software system that implements state-of-the-art YOLO series algorithms, support list and benchmark. It is written in Python and powered by the MindSpore deep learning framework.

The master branch works with MindSpore 1.8.1.

What is New

• 2023/03/30

1. Currently, the models supported by the first release include the basic specifications of YOLOv3/YOLOv5/YOLOv7;
2. Models can be exported to MindIR/AIR format for deployment.
3. ⚠️ The current version is based on the static shape of GRAPH. The dynamic shape of the PYNATIVE will be added later. Please look forward to it.
4. ⚠️ The current version only supports the Ascend platform, and the GPU platform will support it later.

Benchmark and Model Zoo

Seed MODEL ZOO.

Supported Algorithms

• YOLOv7
• YOLOv5
• YOLOv3
• YOLOv8
• YOLOv6
• YOLOv4
• YOLOX

Installation

Dependency

• mindspore >= 1.8.1
• numpy >= 1.17.0
• pyyaml >= 5.3
• openmpi 4.0.3 (for distributed mode)

To install the dependency, please run

pip install -r requirements.txt

MindSpore can be easily installed by following the official instructions where you can select your hardware platform for the best fit. To run in distributed mode, openmpi is required to install.

The following instructions assume the desired dependency is fulfilled.

Getting Started

See GETTING STARTED

Learn More about MindYOLO

To be supplemented.

Notes

How to Contribute

We appreciate all contributions including issues and PRs to make MindYOLO better.

Please refer to CONTRIBUTING.md for the contributing guideline.

License

MindYOLO is released under the Apache License 2.0.

Acknowledgement

MindYOLO is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new realtime object detection methods.

Citation

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

@misc{MindSpore Object Detection YOLO 2023,
    title={{MindSpore Object Detection YOLO}:MindSpore Object Detection YOLO Toolbox and Benchmark},
    author={MindSpore YOLO Contributors},
    howpublished = {\url{https://github.com/mindspore-lab/mindyolo}},
    year={2023}
}