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.gitUse the model defined in model.py:
from model import ShuffleNet
# running on MNIST
net = ShuffleNet(num_classes=10, in_channels=1)Trained on ImageNet (using the PyTorch ImageNet example) with
groups=3 and no channel multiplier. On the test set, got 62.2% top 1 and
84.2% top 5. Unfortunately, this isn't comparable to Table 5 of the paper,
because they don't run a network with these settings, but it is somewhere
between the network with groups=3 and half the number of channels (42.8%
top 1) and the network with the same number of channels but groups=8
(32.4% top 1). The pretrained state dictionary can be found here, in
the following
format:
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict()
}
Note: trained with the default ImageNet settings, which are actually
different from the training regime described in the paper. Pending running
again with those settings (and groups=8).
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