PyTorch implementation of DAQ

This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021.

For more information, checkout the project site [website].

Getting started

Dependencies

• Python 3.6
• PyTorch = 1.5.0

Datasets

• Cifar-10
  • This can be automatically downloaded by learning our code

Training & Evaluation

First, clone our github repository.

$ git clone https://github.com/cvlab-yonsei/DAQ.git

Cifar-10 dataset (ResNet-20 architecture)

# Cifar-10 & ResNet-20 W1A1 model
$ python cifar10_train.py --config configs/DAQ/resnet20_DAQ_W1A1.yml
# Cifar-10 & ResNet-20 W1A32 model
$ python cifar10_train.py --config configs/DAQ/resnet20_DAQ_W1A32.yml

Using the pretrained models

• ResNet-20
  • You can use the pretrained models (W1A1, W1A32) in [here]

---

Citation

@inproceedings{kim2021daq,
    author={Kim, Dohyung  and Lee, Junghyup and Ham, Bumsub},
    title={Distance-aware Quantization},
    booktitle={Proceedings of International Conference on Computer Vision},
    year={2021},
}

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Credit

• ResNet-20 model: [ResNet on CIFAR10] [IRNet]
• Quantized modules: [DSQ]