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MindSpore Computer Vision is an open source computer vision research toolbox based on MindSpore in computer vision direction. It is mainly used for the development of image tasks and includes a large number of classic and cutting-edge deep learning classification models, such as ResNet, ViT, and SwinTransformer.
smooth_factor
to label_smoothing
MindSpore Computer Vision, a MindSpore base Python package, provides high-level features:
The following instructions assume that you have desired dependency installed and working.
pip install https://github.com/mindlab-ai/mindcv/releases/download/v0.0.1-alpha/mindcv-0.0.1a0-py3-none-any.whl
# Clone the mindcv repository.
git clone https://github.com/mindlab-ai/mindcv.git
cd mindcv
# Install
python setup.py install
See Get Started With MindCV to learn about basic usage.
This project is released under the Eclipse Public License 1.0.
The dynamic version is still under development, if you find any issue or have an idea on new features, please don't hesitate to contact us via issue.
MindSpore 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 computer vision methods.
We appreciate all contributions to improve MindSpore Vision. Please refer to CONTRIBUTING.md for the contributing guideline.
If you find this project useful in your research, please consider citing:
@misc{MindSpore Computer Vision 2022,
title={{MindSpore Computer Vision}:MindSpore Computer Vision Toolbox and Benchmark},
author={MindSpore Vision Contributors},
howpublished = {\url{https://github.com/mindlab-ai/mindcv/}},
year={2022}
}
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