LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference, arxiv

PaddlePaddle training/validation code and pretrained models for the model released in CVPR2022: TopFormer (classification backbone).

The official PyTorch implementation is here.

This implementation is developed by PaddleViT.

LeViT Model Overview

Update

• Update (2022-04-30): Code is released and ported weights are uploaded.

Models Zoo

Model	Acc@1	Acc@5	#Params	FLOPs	Image Size	Crop_pct	Interpolation	Link
levit_128s	76.52	92.92	7.8M	0.3G	224	0.9	bicubic	google/baidu
levit_128	78.58	93.94	9.3M	0.4G	224	0.9	bicubic	google/baidu
levit_192	79.87	94.74	11.0M	0.6G	224	0.9	bicubic	google/baidu
levit_256	81.60	95.45	19.0M	1.1G	224	0.9	bicubic	google/baidu
levit_384	82.60	95.96	39.2M	2.2G	224	0.9	bicubic	google/baidu

Teacher Model	Link
RegNet_Y_160	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 ./levit_128s.pdparams, to use the levit_128s model in python:

from config import get_config
from levit import build_levit as build_model
# config files in ./configs/
config = get_config('./configs/levit_128s.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./levit_128s.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_distill.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu_distill.py \
-cfg='./configs/levit_128s.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./levit_128s.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_distill.py \
-cfg='./configs/levit_128s.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-teacher_model_path='./regnety_160.pdparams' \
-amp

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

Reference

@inproceedings{graham2021levit,
  title={LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference},
  author={Graham, Benjamin and El-Nouby, Alaaeldin and Touvron, Hugo and Stock, Pierre and Joulin, Armand and J{\'e}gou, Herv{\'e} and Douze, Matthijs},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12259--12269},
  year={2021}
}