在 pytorch 中实现 ShuffleNetV2
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
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"
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
暂无开放 Issues，或尚未同步最近议题。