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MobileNetV3 in PyTorch

An implementation of MobileNetV3 in PyTorch. MobileNetV3 is an efficient convolutional neural network architecture for mobile devices. For more information check the paper: Searching for MobileNetV3

Usage

Clone the repo:

git clone https://github.com/Randl/MobileNetV3-pytorch
pip install -r requirements.txt

Use the model defined in MobileNetV3.py to run ImageNet example:

python3 -m torch.distributed.launch --nproc_per_node=8 imagenet.py --dataroot "/path/to/imagenet/" --sched clr -b 128 --seed 42 --world-size 8 --sync-bn```

To continue training from checkpoint

python imagenet.py --dataroot "/path/to/imagenet/" --resume "/path/to/checkpoint/folder"

Results

WIP

Inference time is for single 1080 ti per batch of 128.

You can test it with

python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/mobilenetv3large-v1/model_best0.pth.tar" -e

Other implementations

• https://github.com/d-li14/mobilenetv3.pytorch : 73.152% top-1, with more FLOPs
• https://github.com/xiaolai-sqlai/mobilenetv3 : 75.45% top-1, even more FLOPs
• https://github.com/rwightman/gen-efficientnet-pytorch : 75.634% top-1, seems to be right FLOPs

Code used

• DropBlock implementation by miguelvr
• FLOPS calculator by warmspringwinds
• Utility function for divisibility by Tensorflow
• Squeeze-Excitation block by jonnedtc
• Custom cross-entropy by eladhoffer
• Shadow weights implementation by eladhoffer