An implementation of MobileNetv2 in PyTorch. MobileNetv2 is an efficient convolutional neural network architecture for mobile devices. For more information check the paper:
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation
Clone the repo:
git clone https://github.com/Randl/MobileNetV2-pytorch
pip install -r requirements.txtUse the model defined in model.py to run ImageNet example:
python imagenet.py --dataroot "/path/to/imagenet/"To run continue training from checkpoint
python imagenet.py --dataroot "/path/to/imagenet/" --resume "/path/to/checkpoint/folder"For x1.0 model I achieved 0.3% higher top-1 accuracy than claimed.
| Classification Checkpoint | MACs (M) | Parameters (M) | Top-1 Accuracy | Top-5 Accuracy | Claimed top-1 | Claimed top-5 |
|---|---|---|---|---|---|---|
| [mobilenet_v2_1.0_224] | 300 | 3.47 | 72.10 | 90.48 | 71.8 | 91.0 |
| [mobilenet_v2_0.5_160] | 50 | 1.95 | 60.61 | 82.87 | 61.0 | 83.2 |
You can test it with
python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/mobilenet_v2_1.0_224/model_best.pth.tar" -e
python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/mobilenet_v2_0.5_160/model_best.pth.tar" -e --scaling 0.5 --input-size 160