ShuffleNetv2 in PyTorch
August 11, 2018 ยท View on GitHub
An implementation of ShuffleNetv2 in PyTorch. ShuffleNetv2 is an efficient convolutional neural network architecture for mobile devices. For more information check the paper:
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Usage
Clone the repo:
git clone https://github.com/Randl/ShuffleNetV2-pytorch
pip install -r requirements.txt
Use the model defined in model.py to run ImageNet example:
python imagenet.py --dataroot "/path/to/imagenet/"
To continue training from checkpoint
python imagenet.py --dataroot "/path/to/imagenet/" --resume "/path/to/checkpoint/folder"
Results
For x0.5 model I achieved 0.4% lower top-1 accuracy than claimed.
| Classification Checkpoint | MACs (M) | Parameters (M) | Top-1 Accuracy | Top-5 Accuracy | Claimed top-1 | Claimed top-5 |
|---|---|---|---|---|---|---|
| [shufflenet_v2_0.5] | 41 | 1.37 | 59.86 | 81.63 | 60.3 | - |
You can test it with
python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/shufflenet_v2_0.5/model_best.pth.tar" -e --scaling 0.5