Awesome

ResFormer: Scaling ViTs with Multi-Resolution Training

Official PyTorch implementation of ResFormer: Scaling ViTs with Multi-Resolution Training, CVPR2023 | Paper

Overview

<p align="center"> <img src="./imgs/network.png" width=100% height=100% class="center"> </p> We introduce, ResFormer, a framework that is built upon the seminal idea of multi-resolution training for improved performance on a wide spectrum of, mostly unseen, testing resolutions. In particular, ResFormer operates on replicated images of different resolutions and enforces a scale consistency loss to engage interactive information across different scales. More importantly, to alternate among varying resolutions effectively, especially novel ones in testing, we propose a global-local positional embedding strategy that changes smoothly conditioned on input sizes.

Installation

Image Classification

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install timm==0.5.4
pip install tensorboard

Scripts

Training on ImageNet-1k

The default script for training ResFormer-S-MR with training resolutions of 224, 160 and 128.

python -m torch.distributed.launch --nproc_per_node 8 main.py  --data-path  YOUR_DATA_PATH  --model resformer_small_patch16  --output_dir YOUR_OUTPUT_PATH --batch-size 128 --pin-mem --input-size 224 160 128 --auto-resume  --distillation-type 'smooth-l1' --distillation-target cls --sep-aug

The default script for training ResFormer-B-MR with training resolutions of 224, 160 and 128.

python -m torch.distributed.launch --nproc_per_node 8 main.py  --data-path  YOUR_DATA_PATH  --model resformer_base_patch16  --output_dir YOUR_OUTPUT_PATH --batch-size 128 --pin-mem --input-size 224 160 128 --auto-resume  --distillation-type 'smooth-l1' --distillation-target cls --sep-aug --epochs 200 --drop-path 0.2  --lr 8e-4 --warmup-epochs 20 --clip-grad 5.0 --epochs 200  --cooldown-epochs 0

Model Zoo

Image Classification on ImageNet-1k

name	Training Res	Top-1(96)	Top-1(128)	Top-1(160)	Top-1(224)	Top-1(384)	Top-1(512)	model
ResFormer-T-MR	128, 160, 224	61.40	67.78	71.09	73.85	75.04	73.77	google
ResFormer-S-MR	128, 160, 224	73.59	78.24	80.39	82.16	82.72	82.00	google
ResFormer-S-MR	128, 224, 384	72.92	77.84	80.09	82.28	83.70	83.86	google
ResFormer-B-MR	128, 160, 224	75.86	79.74	81.52	82.72	83.29	82.63	google

Catalog

image classification
object detection
semantic segmentation
action recognition

License

This project is released under the MIT license. Please see the LICENSE file for more information.

Citation

@inproceedings{tian2022resformer,
  title={ResFormer: Scaling ViTs with Multi-Resolution Training},
  author={Tian, Rui and Wu, Zuxuan and Dai, Qi and Hu, Han and Qiao, Yu and Jiang, Yu-Gang},
  booktitle={CVPR},
  year={2023}
}