MNASNet in PyTorch

An implementation of MNASNet in PyTorch. MNASNet is an efficient convolutional neural network architecture for mobile devices, developed with architectural search. For more information check the paper: MnasNet: Platform-Aware Neural Architecture Search for Mobile

The model is is implemented by billhhh and the initial idea of reproducing MNASNet is by snakers4

Usage

Clone the repo:

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

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

python3 -m torch.distributed.launch --nproc_per_node=8 imagenet.py --dataroot "/path/to/imagenet/" --warmup 5 --sched cosine -lr 0.2 -b 128 -d 5e-5 --world-size 8 --seed 42

To continue training from checkpoint

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

Results

Initially I've got 72+% top-1 accuracy, but the checkpointing didn't work properly. I believe the results are reproducable.

Classification Checkpoint	MACs (M)	Parameters (M)	Top-1 Accuracy	Top-5 Accuracy	Claimed top-1	Claimed top-5

You can test it with

python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/shufflenet_v2_0.5/model_best.pth.tar" -e

Other implementations

• Mnasnet.MXNet -- A Gluon implementation of Mnasnet, 73.6% top-1 and 91.52% top-5
• MnasNet-pytorch-pretrained -- A PyTorch implementation of Mnasnet, 70.132% top-1 and 89.434% top-5