Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer

Pytorch implementation of our Q-ViT accepted by NeurIPS2022.

Tips

Any problem, please contact the first author (Email: yanjingli@buaa.edu.cn).

Our code is heavily borrowed from DeiT (https://github.com/facebookresearch/deit).

Dependencies

Python 3.8
Pytorch 1.7.1
Torchvision 0.8.2
timm 0.4.12

Evaluation:

Eval Q-ViT Deit-S 2bits: (72.0% Top-1 Acc.):

> python -m torch.distributed.launch --master_port=1234 --nproc_per_node=1 --use_env main.py --model twobits_deit_small_patch16_224 --weight-decay 0. --batch-size 64  --data-path /dataset/ImageNet --output_dir ./eval --resume ./best_checkpoint_2bit.pth --eval

Eval Q-ViT Deit-S 3bits: (79.1% Top-1 Acc.):

> python -m torch.distributed.launch --master_port=1234 --nproc_per_node=1 --use_env main.py --model threebits_deit_small_patch16_224 --weight-decay 0. --batch-size 64  --data-path /dataset/ImageNet --output_dir ./eval --resume ./best_checkpoint_3bit.pth --eval

Methods	Top-1 acc	Top-5 acc	Quantized model link
Q-DeiT-S (3-bit)	79.1	90.3	Model
Q-Deit-S (2-bit)	72.0	94.2	Model