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:
- ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
| 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:
./shufflenet.py --flops [--other-flags]Download and evaluate a pretrained model:
wget http://models.tensorpack.com/ImageNetModels/ShuffleNetV2-0.5x.npz
./shufflenet.py --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 ./alexnet.py --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 reproduces the VGG experiments in the paper Group Normalization (more code about this paper can be found at GroupNorm-reproduce).
See ./vgg16.py --help for usage.
| No Normalization | Batch Normalization | Group Normalization |
|---|---|---|
| 29~30% (large variation with random seed) | 28% | 27.6% |
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.