By Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang and Enhua Wu. NeurIPS 2022. [arXiv link]
- Python 3.7
- PyTorch 1.7.0
- timm 0.3.2
- torchprofile 0.0.4
- Numpy
- tqdm
- pandas
- matplotlib
- wandb (optional)
- apex (optional)
| Raw Image | Grad-Cam HearMap |
 |
 |
More details can be checked in Notebook.
- ViG
| Model | Params (M) | FLOPs (B) | Top-1 | Github Release |
|---|---|---|---|---|
| ViG-Ti | 7.1 | 1.3 | 73.9 | Github Release |
| ViG-S | 22.7 | 4.5 | 80.4 | Github Release |
| ViG-B | 86.8 | 17.7 | 82.3 | Github Release |
- Pyramid ViG
| Model | Params (M) | FLOPs (B) | Top-1 | BaiduDisk URL | Github Release |
|---|---|---|---|---|---|
| Pyramid ViG-Ti | 10.7 | 1.7 | 78.5 | [BaiduDisk], Password: chae | Github Release |
| Pyramid ViG-S | 27.3 | 4.6 | 82.1 | [BaiduDisk], Password: 81mg | Github Release |
| Pyramid ViG-M | 51.7 | 8.9 | 83.1 | [BaiduDisk], Password: prd3 | Github Release |
| Pyramid ViG-B | 82.6 | 16.8 | 83.7 | [BaiduDisk], Password: rgm4 | Github Release |
Data preparation follows the official pytorch example
- Evaluate example:
python train.py /path/to/imagenet/ --model pvig_s_224_gelu -b 128 --pretrain_path /path/to/pretrained/model/ --evaluate
- Training ViG on 8 GPUs:
python -m torch.distributed.launch --nproc_per_node=8 train.py /path/to/imagenet/ --model vig_s_224_gelu --sched cosine --epochs 300 --opt adamw -j 8 --warmup-lr 1e-6 --mixup .8 --cutmix 1.0 --model-ema --model-ema-decay 0.99996 --aa rand-m9-mstd0.5-inc1 --color-jitter 0.4 --warmup-epochs 20 --opt-eps 1e-8 --repeated-aug --remode pixel --reprob 0.25 --amp --lr 2e-3 --weight-decay .05 --drop 0 --drop-path .1 -b 128 --output /path/to/save/models/
- Training Pyramid ViG on 8 GPUs:
python -m torch.distributed.launch --nproc_per_node=8 train.py /path/to/imagenet/ --model pvig_s_224_gelu --sched cosine --epochs 300 --opt adamw -j 8 --warmup-lr 1e-6 --mixup .8 --cutmix 1.0 --model-ema --model-ema-decay 0.99996 --aa rand-m9-mstd0.5-inc1 --color-jitter 0.4 --warmup-epochs 20 --opt-eps 1e-8 --repeated-aug --remode pixel --reprob 0.25 --amp --lr 2e-3 --weight-decay .05 --drop 0 --drop-path .1 -b 128 --output /path/to/save/models/
@inproceedings{han2022vig,
title={Vision GNN: An Image is Worth Graph of Nodes},
author={Kai Han and Yunhe Wang and Jianyuan Guo and Yehui Tang and Enhua Wu},
booktitle={NeurIPS},
year={2022}
}
This repo partially uses code from deep_gcns_torch and timm. Our code is modified and adapted on these great repositories:
- Vision_GNN (NeurIPS 2022)
- deep_gcns_torch (ICCV 2019)
- timm
- PyTorch Metric Learning
If you use this method or this code in your research, please cite as:
@inproceedings{han2022vig,
title={Vision GNN: An Image is Worth Graph of Nodes},
author={Kai Han and Yunhe Wang and Jianyuan Guo and Yehui Tang and Enhua Wu},
booktitle={NeurIPS},
year={2022}
}