An implementation of ShuffleNet in PyTorch. ShuffleNet is an efficient convolutional neural network architecture for mobile devices. According to the paper, it outperforms Google's MobileNet by a small percentage.

In one sentence, ShuffleNet is a ResNet-like model that uses residual blocks (called ShuffleUnits), with the main innovation being the use of pointwise, or 1x1, group convolutions as opposed to normal pointwise convolutions.

Clone the repo:

git clone https://github.com/jaxony/ShuffleNet.git

Use the model defined in model.py:

from model import ShuffleNet

# running on MNIST
net = ShuffleNet(num_classes=10, in_channels=1)

The ShuffleNet implementation has been briefly tested on the MNIST dataset and achieves 90+% accuracy within the first 5 epochs, so the model can certainly learn. If anyone has the GPU resources to train ShuffleNet on ImageNet, do share the weights if you manage to train it successfully as in the original paper!