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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 (2022-03-30): Code is refactored.
  • 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
poolformer_s24 80.33 95.05 21.3M 3.4G 224 0.9 bicubic google/baidu
poolformer_s36 81.43 95.45 30.8M 5.0G 224 0.9 bicubic google/baidu
poolformer_m36 82.11 95.69 56.1M 8.9G 224 0.95 bicubic google/baidu
poolformer_m48 82.46 95.96 73.4M 11.8G 224 0.95 bicubic google/baidu

*The results are evaluated on ImageNet2012 validation set.

Data Preparation

ImageNet2012 dataset is used in the following file structure:

│imagenet/
├──train_list.txt
├──val_list.txt
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......
  • train_list.txt: list of relative paths and labels of training images. You can download it from: google/baidu
  • val_list.txt: list of relative paths and labels of validation images. You can download it from: google/baidu

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 weight file is downloaded 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
model_state_dict = paddle.load('./poolformer_s12.pdparams')
model.set_state_dict(model_state_dict)

Evaluation

To evaluate model performance on ImageNet2012, run the following script using command line:

sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/poolformer_s12.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./poolformer_s12.pdparams' \
-amp

Note: if you have only 1 GPU, change device number to CUDA_VISIBLE_DEVICES=0 would run the evaluation on single GPU.

Training

To train the model on ImageNet2012, run the following script using command line:

sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/poolformer_s12.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-amp

Note: it is highly recommanded to run the training using multiple GPUs / multi-node GPUs.

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}
}