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.
Expected format of data directory is described in docs.
Pretrained models can be downloaded at tensorpack model zoo.
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 the
2nd arxiv version of the paper.
After 240 epochs (36 hours on 8 P100s) it reaches top-1 error of 42.32%,
matching the paper's number.
To print flops:
./shufflenet.py --flopsIt 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
This AlexNet script is quite close to the settings in its original
paper.
Trained with 64x2 batch size, the script reaches 58% single-crop validation
accuracy after 100 epochs (21 hours on 2 V100s).
It also puts in tensorboard the first-layer filter visualizations similar to the paper.
See ./alexnet.py --help for usage.
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.
This VGG16 script, when trained with 32x8 batch size, 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 ./vgg16.py --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 have not been individually tuned for best accuracy.
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.