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PoolFormer

README.md

PoolFormer: MetaFormer is Actually What You Need for Vision, arxiv

PaddlePaddle training/validation code and pretrained models for PoolFormer.

The official PyTorch implementation is here.

This implementation is developed by PaddleViT.

drawing

PoolFormer Model Overview

Update

  • Update (2021-12-15): Code and weights are updated.
  • Update (2021-12-10): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
poolformer_s12 77.24 93.51 11.9M 1.8G 224 0.9 bicubic google/baidu(zcv4)
poolformer_s24 80.33 95.05 21.3M 3.4G 224 0.9 bicubic google/baidu(nedr)
poolformer_s36 81.43 95.45 30.8M 5.0G 224 0.9 bicubic google/baidu(fvpm)
poolformer_m36 82.11 95.69 56.1M 8.9G 224 0.95 bicubic google/baidu(whfp)
poolformer_m48 82.46 95.96 73.4M 11.8G 224 0.95 bicubic google/baidu(374f)

*The results are evaluated on ImageNet2012 validation set.

Notebooks

We provide a few notebooks in aistudio to help you get started:

*(coming soon)*

Requirements

Data

ImageNet2012 dataset is used in the following folder structure:

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

Usage

To use the model with pretrained weights, download the .pdparam weight file and change related file paths in the following python scripts. The model config files are located in ./configs/.

For example, assume the downloaded weight file is stored in ./poolformer_s12.pdparams, to use the poolformer_s12 model in python:

from config import get_config
from poolformer import build_poolformer as build_model
# config files in ./configs/
config = get_config('./configs/poolformer_s12.yaml')
# build model
model = build_model(config)
# load pretrained weights, .pdparams is NOT needed
model_state_dict = paddle.load('./poolformer_s12')
model.set_dict(model_state_dict)

Evaluation

To evaluate PoolFormer model performance on ImageNet2012 with a single GPU, run the following script using command line:

sh run_eval.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
    -cfg='./configs/poolformer_s12.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./poolformer_s12'
Run evaluation using multi-GPUs: 
sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg='./configs/poolformer_s12.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \
    -eval \
    -pretrained='./poolformer_s12'

Training

To train the PoolFormer model on ImageNet2012 with single GPU, run the following script using command line:

sh run_train.sh

or

CUDA_VISIBLE_DEVICES=0 \
python main_single_gpu.py \
  -cfg='./configs/poolformer_s12.yaml' \
  -dataset='imagenet2012' \
  -batch_size=32 \
  -data_path='/dataset/imagenet' \
Run training using multi-GPUs: 
sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python main_multi_gpu.py \
    -cfg='./configs/poolformer_s12.yaml' \
    -dataset='imagenet2012' \
    -batch_size=16 \
    -data_path='/dataset/imagenet' \

Visualization Attention Map

(coming soon)

Reference

@article{yu2021metaformer,
  title={MetaFormer is Actually What You Need for Vision},
  author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2111.11418},
  year={2021}
}