Awesome

AMixer

Created by Yongming Rao, Wenliang Zhao, Jie Zhou, Jiwen Lu

This repository contains PyTorch implementation for AMixer.

AMixer is a family of self-attention free vision Transfromers using adaptive weight mixing to generate attention weights without token-token interactions.

🔥 News

• Our new work HorNet has been accepted by NeurIPS 2022. HorNet achieves 87.7% top-1 accuracy on ImageNet, 57.9% mIoU on ADE20K and 59.2% bounding box AP on COCO. Welcome to try the more powerful and generic model.

ImageNet Classification

Requirements

• torch==1.8.0
• torchvision==0.9.0
• timm==0.4.12
• tensorboardX

Data preparation: download and extract ImageNet images from http://image-net.org/. The directory structure should be

│ILSVRC2012/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

Training

To train AMixer models on ImageNet from scratch, run:

AMixer-DeiT-S

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model amixer_deit_s  \  
--batch_size 128 --lr 1e-3 --update_freq 1 \
--model_ema true --model_ema_eval true \
--data_path /path/to/imagenet-1k \
--output_dir ./logs/amixer_deit_s

AMixer-T

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model amixer_t  \ 
--batch_size 128 --lr 1e-3 --update_freq 1 \
--model_ema true --model_ema_eval true \
--data_path /path/to/imagenet-1k \
--output_dir ./logs/amixer_t

AMixer-S

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model amixer_s \ 
--batch_size 64 --lr 1e-3 --update_freq 2 \
--model_ema true --model_ema_eval true \
--data_path /path/to/imagenet-1k \
--output_dir ./logs/amixer_s

AMixer-B

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model amixer_b  \
--batch_size 32 --lr 1e-3 --update_freq 4 \
--model_ema true --model_ema_eval true \
--data_path /path/to/imagenet-1k \
--output_dir ./logs/amixer_b

License

MIT License

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{rao2022amixer,
  title={AMixer: Adaptive Weight Mixing for Self-attention Free Vision Transformers},
  author={Rao, Yongming and Zhao, Wenliang and Zhou, Jie and Lu, Jiwen},
  booktitle={European Conference on Computer Vision},
  pages={50--67},
  year={2022},
  organization={Springer}
}