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