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

The master branch supporting MindSpore 2.0.

• 2023/06/15

1. Support YOLOv3/v4/v5/X/v7/v8 6 models and release 23 corresponding weights, see MODEL ZOO for details.
2. Support MindSpore 2.0.
3. Support deployment on MindSpore lite 2.0.
4. New online documents are available!

See MODEL ZOO.

• mindspore >= 2.0
• 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 current version only supports the Ascend platform, and the GPU platform will be supported later.

See GETTING STARTED

To be supplemented.

⚠️ The current version is based on the static shape of GRAPH. The dynamic shape of the PYNATIVE will be supported later. Please look forward to it.

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

Please refer to CONTRIBUTING.md for the contributing guideline.

MindYOLO is released under the Apache License 2.0.

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.

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}
}