Reproduce ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices on ImageNet.
This is a 40Mflops ShuffleNet,
roughly corresponding to ShuffleNet 0.5x (arch2) g=8 in the paper.
But detailed architecture may not be the same.
After 100 epochs it reaches top-1 error of 42.62.
Print flops with tensorflow:
./shufflenet.py --flopsIt will print about 80Mflops, because TF counts FMA as 2 flops while the paper counts it as 1 flop.
Train (takes 24 hours on 8 Maxwell TitanX):
./shufflenet.py --data /path/to/ilsvrc/