Latest commit 5772d5f Nov 2, 2018

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

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


Reproduce ImageNet results of the following two papers:

Model Flops Top-1 Error Paper's Error Flags
ShuffleNetV1 0.5x ⬇️ 40M 40.8% 42.3% -r=0.5
ShuffleNetV1 1x ⬇️ 140M 32.6% 32.4% -r=1
ShuffleNetV2 0.5x ⬇️ 41M 39.5% 39.7% -r=0.5 --v2
ShuffleNetV2 1x ⬇️ 146M 30.6% 30.6% -r=1 --v2

To print flops:

./ --flops [--other-flags]

Download and evaluate a pretrained model:

./ --eval --data /path/to/ilsvrc --load ShuffleNetV2-0.5x.npz --v2 -r=0.5


This AlexNet script is quite close to the settings in its original paper. Trained with 2 GPUs and 64 batch size per GPU, the script reaches 58% single-crop validation accuracy after 100 epochs (21h on 2 V100s). It also puts in tensorboard the first-layer filter visualizations similar to the paper. See ./ --help for usage.


This VGG16 script, when trained with 8 GPUs and 32 batch size per GPU, reaches the following validation error after 100 epochs (30h with 8 P100s). This is the code for the VGG experiments in the paper Group Normalization. See ./ --help for usage.

No Normalization Batch Normalization Group Normalization
29~30% (large variation with random seed) 28% 27.6%

Note that the purpose of this experiment in the paper is not to claim GroupNorm is better than BatchNorm, therefore the training settings and hyperpameters may not be the best for BatchNorm.


This Inception-BN script reaches 27% single-crop validation error after 300k steps with 6 GPUs. The training recipe is very different from the original paper because the paper is a bit vague on these details.


See ResNet examples. It includes variants like pre-activation ResNet, squeeze-and-excitation networks.


See DoReFa-Net examples. It includes other quantization methods such as Binary Weight Network, Trained Ternary Quantization.