ImageNetModels

ImageNet training code of ResNet, Inception, VGG, ShuffleNet, DoReFa-Net with tensorpack.

To train any of the models, just do ./{model}.py --data /path/to/ilsvrc. Expected format of data directory is described in docs. Pretrained models can be downloaded at tensorpack model zoo.

ShuffleNet

Reproduce ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices on ImageNet.

This is a 38Mflops ShuffleNet, corresponding to ShuffleNet 0.5x g=3 in version 2 of the paper. After 240 epochs (36 hours on 8 P100s) it reaches top-1 error of 42.32%, better than the paper's number.

To print flops:

./shufflenet.py --flops

It will print about 75Mflops, because the paper counts multiply+add as 1 flop.

Evaluate the pretrained model:

./shufflenet.py --eval --data /path/to/ilsvrc --load /path/to/model

Inception-BN, VGG16

This Inception-BN script reaches 27% single-crop error after 300k steps with 6 GPUs.

This VGG16 script, when trained with 32x8 batch size, reaches 29~30% single-crop error after 100 epochs (30h with 8 P100s), 28% with BN, and 27.6% with GN.

ResNet, DoReFa-Net

See ResNet examples and DoReFa-Net examples.