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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.
Under construction...
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
You can see Get Started With MindCV to learn about the key component in MindCV .
It is easy to train your model on standard datasets or your own dataset with MindCV.
You can run train.py
to do training with customized hyper-parameters. Here is the example for training a DenseNet on CIFAR10 dataset.
python train.py --model=densenet121 --optimizer=adam --lr=0.001 \
--dataset=cifar10 --num_classes=10 --dataset_download
All supported hyper-parameters (for data transform, model, loss, optimizer, and others) can be viewed in config.py
To validate, you can run validate.py
as shown in the following example.
python validate.py --model=densenet121 --dataset=cifar10 --val_split=test \
--num_classes=10 --dataset_download
For large datasets like ImageNet, it is necessary to do training in distributed mode on multiple devices, which is well supported in MindCV. The following script is an example for training DenseNet121 on ImageNet with 4 GPUs.
export CUDA_VISIBLE_DEVICES=0,1,2,3 # suppose there are 4 GPUs
mpirun --allow-run-as-root -n 4 python train.py --distribute \
--model=densenet121 --dataset=imagenet --data_dir=./datasets/imagenet
We also provide that yaml config files that yield competitive results on ImageNet for different models in yaml config files. To trigger training using yaml config,
python train.py -c configs/squeezenet/squeezenet_1.0_gpu.yaml
We provide jupyter notebook tutorials for
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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