Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (arxiv)

This is a Pytorch implementation of our paper ViP, IEEE TPAMI 2022. MindSpore and Jittor code will be released soon. We present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. We show that our Vision Permutators are formidable competitors to convolutional neural networks (CNNs) and vision transformers.

We hope this work could encourage researchers to rethink the way of encoding spatial information and facilitate the development of MLP-like models.

Compare

Basic structure of the proposed Permute-MLP layer. The proposed Permute-MLP layer contains three branches that are responsible for encoding features along the height, width, and channel dimensions, respectively. The outputs from the three branches are then combined using element-wise addition, followed by a fully-connected layer for feature fusion.

Our code is based on the pytorch-image-models, Token Labeling, T2T-ViT

Comparison with Recent MLP-like Models

Model	Parameters	Throughput	Image resolution	Top 1 Acc.	Download	Logs
EAMLP-14	30M	711 img/s	224	78.9%
gMLP-S	20M	-	224	79.6%
ResMLP-S24	30M	715 img/s	224	79.4%
ViP-Small/7 (ours)	25M	719 img/s	224	81.5%	link	log
EAMLP-19	55M	464 img/s	224	79.4%
Mixer-B/16	59M	-	224	78.5%
ViP-Medium/7 (ours)	55M	418 img/s	224	82.7%	link	log
gMLP-B	73M	-	224	81.6%
ResMLP-B24	116M	231 img/s	224	81.0%
ViP-Large/7	88M	298 img/s	224	83.2%	link	log

The throughput is measured on a single machine with V100 GPU (32GB) with batch size set to 32.

Training ViP-Small/7 takes less than 30h on ImageNet for 300 epochs on a node with 8 A100 GPUs.

Requirements

torch>=1.4.0
torchvision>=0.5.0
pyyaml
timm==0.4.5
apex if you use 'apex amp'

data prepare: ImageNet with the following folder structure, you can extract imagenet by this script.

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

Validation

Replace DATA_DIR with your imagenet validation set path and MODEL_DIR with the checkpoint path

CUDA_VISIBLE_DEVICES=0 bash eval.sh /path/to/imagenet/val /path/to/checkpoint

Training

Command line for training on 8 GPUs (V100)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet --model vip_s7 -b 256 -j 8 --opt adamw --epochs 300 --sched cosine --apex-amp --img-size 224 --drop-path 0.1 --lr 2e-3 --weight-decay 0.05 --remode pixel --reprob 0.25 --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-lr 1e-6 --warmup-epochs 20

Reference

You may want to cite:

@article{hou2022vision,
  title={Vision permutator: A permutable mlp-like architecture for visual recognition},
  author={Hou, Qibin and Jiang, Zihang and Yuan, Li and Cheng, Ming-Ming and Yan, Shuicheng and Feng, Jiashi},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}

License

This repository is released under the MIT License as found in the LICENSE file. Code in this repo is for non-commercial use only.