Pytorch implementation of DoReFa-Net
This repository is the pytorch implementation of DoReFa-Net for neural network compression. The code is inspired by the original tensorpack implementation. This implementation supports k-bit quantization for both weights and activations. (I have not test the activation quantization yet, but it should work as expected)
Requirements:
- python>=3.5
- pytorch>=0.4.1
- tensorboardX
CIFAR-10:
(Quantized models are trained from scratch.)
| Model | W-bit | A-bit | Accuracy |
|---|---|---|---|
| ResNet-20 | 32 | 32 | 92.13 |
| ResNet-20 | 4 | 32 | 91.46 |
| ResNet-20 | 2 | 32 | 91.05 |
| ResNet-20 | 1 | 32 | 90.54 |
ImageNet2012
(Quantized models are finetuned from pretrained model.)
| Model | W-bit | A-bit | Top-1 Accuracy | Top-5 Accuracy |
|---|---|---|---|---|
| AlexNet | 32 | 32 | 56.50% | 79.01% |
| AlexNet | 1 | 32 | 53.31% | 76.72% |