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https://github.com/Randl/ShuffleNetV2-pytorch
Implementation of ShuffleNetV2 for pytorch
https://github.com/Randl/ShuffleNetV2-pytorch
cnn-model pytorch shufflenetv2
Last synced: 3 days ago
JSON representation
Implementation of ShuffleNetV2 for pytorch
- Host: GitHub
- URL: https://github.com/Randl/ShuffleNetV2-pytorch
- Owner: Randl
- License: mit
- Created: 2018-08-02T13:06:48.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-04-17T04:44:41.000Z (over 2 years ago)
- Last Synced: 2024-08-01T22:50:09.301Z (3 months ago)
- Topics: cnn-model, pytorch, shufflenetv2
- Language: Python
- Size: 10.2 MB
- Stars: 189
- Watchers: 9
- Forks: 52
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-pytorch : https://github.com/Randl/ShuffleNetV2-pytorch
- awesome-image-classification - unofficial-pytorch : https://github.com/Randl/ShuffleNetV2-pytorch
README
# ShuffleNetv2 in PyTorch
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](https://arxiv.org/abs/1807.11164)
## Usage
Clone the repo:
```bash
git clone https://github.com/Randl/ShuffleNetV2-pytorch
pip install -r requirements.txt
```
Use the model defined in `model.py` to run ImageNet example:
```bash
python imagenet.py --dataroot "/path/to/imagenet/"
```
To continue training from checkpoint
```bash
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
```bash
python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/shufflenet_v2_0.5/model_best.pth.tar" -e --scaling 0.5
```