MindYOLO¶
MindYOLO implements state-of-the-art YOLO series algorithms based on MindSpore.
The following is the corresponding mindyolo
versions and supported mindspore
versions.
mindyolo | mindspore |
---|---|
master | master |
0.4 | 2.3.½.3.0 |
0.3 | 2.2.10 |
0.2 | 2.0 |
0.1 | 1.8 |
Benchmark and Model Zoo¶
See Benchmark Results.
supported model list¶
- YOLOv10 (welcome to contribute)
- YOLOv9 (welcome to contribute)
- YOLOv8
- YOLOv7
- YOLOX
- YOLOv5
- YOLOv4
- YOLOv3
Installation¶
See INSTALLATION for details.
Getting Started¶
See QUICK START for details.
Notes¶
⚠️ The current version is based on the static shape of GRAPH. The dynamic shape will be supported later. Please look forward to it.
How to Contribute¶
We appreciate all contributions including issues and PRs to make MindYOLO better.
Please refer to CONTRIBUTING 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 support the growing research community, reimplement existing methods, and develop their own new real-time object detection methods by providing a flexible and standardized toolkit.
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}
}