Shufflenet-v2-Pytorch
Introduction
This is a Pytorch implementation of faceplusplus's ShuffleNet-v2. For details, please read the following papers:
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Pretrained Models on ImageNet
We provide pretrained ShuffleNet-v2 models on ImageNet,which achieve slightly better accuracy rates than the original ones reported in the paper.
The top-1/5 accuracy rates by using single center crop (crop size: 224x224, image size: 256xN):
Network Top-1 Top-5 Top-1(reported in the paper)
ShuffleNet-v2-x0.5 60.646 81.696 60.300
ShuffleNet-v2-x1 69.402 88.374 69.400
Evaluate Models
python eval.py -a shufflenetv2 --width_mult=0.5 --evaluate=./shufflenetv2_x0.5_60.646_81.696.pth.tar ./ILSVRC2012/
python eval.py -a shufflenetv2 --width_mult=1.0 --evaluate=./shufflenetv2_x1_69.390_88.412.pth.tar ./ILSVRC2012/
Version:
Python2.7
torch0.3.1
torchvision0.2.1
Dataset prepare Refer to https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset