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Build Type | Linux |
MacOS |
Windows |
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Build Status | Not tested |
English | 中文
Introduction |
Installation |
Get Started |
Tutorials |
Model List |
Supported Algorithms |
Notes
MindCV is an open source toolbox for computer vision research and development based on MindSpore. It collects a series of classic and SoTA vision models, such as ResNet and SwinTransformer, along with their pretrained weights. SoTA methods such as AutoAugment are also provided for performance improvement. With the decoupled module design, it is easy to apply or adapt MindCV to your own CV tasks.
>>> import mindcv
# create a dataset
>>> dataset = mindcv.create_dataset('cifar10', download=True)
# create a model
>>> network = mindcv.create_model('resnet50', pretrained=True)
Users can customize and launch their transfer learning or training task in one command line.
# transfer learning in one command line
>>> !python train.py --model=swin_tiny --pretrained --opt=adamw --lr=0.001 --data_dir={data_dir}
Coming soon.
To install the dependency, please run
pip install -r requirements.txt
MindSpore can be easily installed by following the official instruction 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.
The released version of MindCV can be installed via PyPI
as follows:
pip install mindcv
The latest version of MindCV can be installed as follows:
pip install git+https://github.com/mindspore-lab/mindcv.git
To get started with MindCV, please see the transfer learning tutorial, which will give a quick tour on each key component and the train/validate/predict pipelines in MindCV.
Below is a few code snippets for your taste.
>>> import mindcv
# List and find a pretrained vision model
>>> mindcv.list_models("swin*", pretrained=True)
['swin_tiny']
# Create the model object
>>> network = mindcv.create_model('swin_tiny', pretrained=True)
# Validate its accuracy
>>> !python validate.py --model=swin_tiny --pretrained --dataset=imagenet --val_split=validation
{'Top_1_Accuracy': 0.808343989769821, 'Top_5_Accuracy': 0.9527253836317136, 'loss': 0.8474242982580839}
Image classification demo
Infer the input image with a pretrained SoTA model,
>>> !python infer.py --model=swin_tiny --image_path='./tutorials/data/test/dog/dog.jpg'
{'Labrador retriever': 0.5700152, 'golden retriever': 0.034551315, 'kelpie': 0.010108651, 'Chesapeake Bay retriever': 0.008229004, 'Walker hound, Walker foxhound': 0.007791956}
The top-1 prediction result is labrador retriever (拉布拉多犬), which is the breed of this cut dog.
It is easy to train your model on standard datasets or your own dataset with MindCV. Model training, transfer learning, or evaluaiton can be done using one or a few line of code with flexible configuration.
It is easy to do model training with train.py
. Here is an example for training a DenseNet on CIFAR10 dataset using one computing device (i.e., standalone GPU).
python train.py --model=resnet50 --dataset=cifar10 --dataset_download
For more parameter description, please run `python train.py --help'. You can define change model, optimizer, and other hyper-parameters easily.
Validation while training. To track the validation accuracy change during traing, please enable --val_while_train
, for example
python train.py --model=resnet50 --dataset=cifar10 \
--val_while_train --val_split=test --val_interval=1
The training loss and validation accuracy for each epoch will be saved in {ckpt_save_dir}/results.log
.
Resume training. To resume training, please specify --ckpt_path
for the checkpoint where you want to resume and --ckpt_save_dir
. The optimizer state including learning rate of the last epoch will also be recovered.
python train.py --model=resnet50 --dataset=cifar10 \
--ckpt_save_dir=checkpoints --ckpt_path=checkpoints/resnet50_30-100.ckpt
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
You can configure your model and other components either by specifying external parameters or by using a yaml config file. Here is an example for training using a preset yaml file.
mpirun --allow-run-as-root -n 4 python train.py -c configs/squeezenet/squeezenet_1.0_gpu.yaml
Well-defined config files for training SoTA models are placed in the configs
folder, along with their performance reported on ImageNet dataset.
It is easy to validate a trained model with validate.py
.
# validate a trained checkpoint
python validate.py --model=resnet50 --dataset=imagenet --val_split=validation --ckpt_path='./ckpt/densenet121-best.ckpt'
By default, the training pipeline (train.py
) is run in graph mode, which is optimized for efficienty and speed but may not be flexible enough for debugging. You may alter the parameter --mode
to switch to pure pynative mode for debugging purpose.
Pynative mode with ms_function is a mixed mode for comprising flexibity and efficiency in MindSpore. To switch to pynative mode with ms_function, please use train_with_func.py
instead, for example:
python train_with_func.py --model=resnet50 --dataset=cifar10 --dataset_download --epoch_size=10
Note:
train_with_func.py
is an experimental training pipeline and is less stable thantrain.py
. It will be improved after MindSpore 2.0 released.
For more examples, see examples/scripts.
We provide the following jupyter notebook tutorials to help users learn to use MindCV.
Currently, MindCV supports the model families listed below. More models with pretrained weights are under development and will be released soon.
Please see configs for the details about model performance and pretrained weights.
--aug_repeats
to be a value larger than 1 (typically, 3 or 4 is a common choice).We appreciate all kind of contributions including issues and PRs to make MindCV better.
Please refer to CONTRIBUTING.md for the contributing guideline. Please follow the Model Template and Guideline for contributing a model that fits the overall interface :)
This project follows the Apache License 2.0 open-source license.
MindCV is an open-source project jointly developed by the MindSpore team, Xidian University, and Xi'an Jiaotong University.
Sincere thanks to all participating researchers and developers for their hard work on this project.
We also acknowledge the computing resources provided by OpenI.
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/mindspore-lab/mindcv/}},
year={2022}
}
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