Pushing the Limit of Post-Training Quantization by Block Reconstruction
Pytorch implementation of BRECQ, ICLR 2021
@inproceedings{
li&gong2021brecq,
title={BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction},
author={Yuhang Li and Ruihao Gong and Xu Tan and Yang Yang and Peng Hu and Qi Zhang and Fengwei Yu and Wei Wang and Shi Gu},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=POWv6hDd9XH}
}
Pretrained models
We provide all the pretrained models and they can be accessed via torch.hub
For example: use res18 = torch.hub.load('yhhhli/BRECQ', model='resnet18', pretrained=True) to get the pretrained ResNet-18 model.
If you encounter URLError when downloading the pretrained network, it’s probably a network failure. An alternative way is to use wget to manually download the file, then move it to ~/.cache/torch/checkpoints, where the load_state_dict_from_url function will check before