ShuffleNet-V2 for both PyTorch and Caffe.

This project supports both Pytorch and Caffe. Supported model width are 0.25, 0.33, 0.5, 1.0, 1.5 or 2.0, other model width are not supported.

Just use shufflenet_v2.py as following.

import torch
import shufflenet_v2
num_classes = 1000
model_width = 0.5
net = shufflenet_v2.Network(num_classes, model_width)
params = torch.load('shufflenet_v2_x0.5.pth', map_location=lambda storage, loc: storage)
net.load_state_dict(params)
input = torch.randn(1,3,224,224)
output = net(input)

Prototxt files can be generated by shufflenet_v2.py

python shufflenet_v2.py --save_caffe net --num_classes 1000 --model_width 1.0

python shufflenet_v2.py --load_pytorch net.pth --save_caffe net --num_classes 1000 --model_width 1.0

Pretrained models can be downloaded from: https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe/releases

• shufflenet_v2_x0.25, Top-1 Acc = 46.04%. Unofficial.
• shufflenet_v2_x0.33, Top-1 Acc = 51.40%. Unofficial.
• shufflenet_v2_x0.50, Top-1 Acc = 58.93%. This accuracy is 1.37% lower compared with the result in the official paper.

1. All ImageNet images are resized by a short edge size of 256 (bicubic interpolation by PIL). And then each of them are pickled by Python and stored in a LMDB dataset.
2. Training is done by PyTorch 0.4.0
3. data augmentation: 224x224 random crop and random horizontal flip. No image mean extraction is used here, which is done automatically by data/bn layers in the network.
4. As in my codes, networks are initialized by nn.init.kaiming_normal_(m.weight, mode='fan_out').
5. A SGD with nesterov momentum (0.9) is used for optimizing. The batch size is 1024. Models are trained by 300000 iterations, while the learning rate decayed linearly from 0.5 to 0.

1. Models are trained by PyTorch and converted to Caffe. Thus, you should use scale parameter in Caffe's data layer to make sure all input images are rescaled from [0, 255] to [0, 1].
2. The RGB~BGR problem is not very crucial, you may just ignore the difference if you are use these models as pretrained models for other tasks.

All these years, I barely achieved same or higher results of different kinds of complex ImageNet models reported in papers. If you got a better accuracy, please tell me.