Features

  • Easy-to-Use. MindCV decomposes the vision framework into various configurable components. It is easy to customize your data pipeline, models, and learning pipeline with MindCV:

>>> 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}

  • State-of-The-Art. MindCV provides various CNN-based and Transformer-based vision models including SwinTransformer. Their pretrained weights and performance reports are provided to help users select and reuse the right model.

  • Flexibility and efficiency. MindCV is bulit on MindSpore which is an efficent DL framework that can run on different hardward platforms (GPU/CPU/Ascend). It supports both graph mode for high efficiency and pynative mode for flexibity.