Visual Attention Network (VAN) paper pdf
This is a PyTorch implementation of VAN proposed by our paper "Visual Attention Network".
Figure 1: Compare with different vision backbones on ImageNet-1K validation set.
@article{guo2022visual,
title={Visual Attention Network},
author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
journal={arXiv preprint arXiv:2202.09741},
year={2022}
}
2022.02.25 Supported by Jimm
2022.03.15 Supported by Hugging Face.
2022.04 Supported by PaddleCls.
2022.05 Supported by OpenMMLab.
For More Code, please refer to Paper with code.
2022.07.08 Update paper on ArXiv. (ImageNet-22K results, SOTA for panoptic segmentation (58.2 PQ). Segmentation models are available.
Classification models see Here. We are working on it.
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
Figure 2: Decomposition diagram of large-kernel convolution. A standard convolution can be decomposed into three parts: a depth-wise convolution (DW-Conv), a depth-wise dilation convolution (DW-D-Conv) and a 1×1 convolution (1×1 Conv).
Figure 3: The structure of different modules: (a) the proposed Large Kernel Attention (LKA); (b) non-attention module; (c) the self-attention module (d) a stage of our Visual Attention Network (VAN). CFF means convolutional feed-forward network. The difference between (a) and (b) is the element-wise multiply. It is worth noting that (c) is designed for 1D sequences. .
Data prepare: ImageNet with the following folder structure.
│imagenet/
├──train/
│ ├── n01440764
│ │ ├── n01440764_10026.JPEG
│ │ ├── n01440764_10027.JPEG
│ │ ├── ......
│ ├── ......
├──val/
│ ├── n01440764
│ │ ├── ILSVRC2012_val_00000293.JPEG
│ │ ├── ILSVRC2012_val_00002138.JPEG
│ │ ├── ......
│ ├── ......
| Model | #Params(M) | GFLOPs | Top1 Acc(%) | Download |
|---|---|---|---|---|
| VAN-B0 | 4.1 | 0.9 | 75.4 | Google Drive, Tsinghua Cloud, Hugging Face 🤗 |
| VAN-B1 | 13.9 | 2.5 | 81.1 | Google Drive, Tsinghua Cloud, Hugging Face 🤗 |
| VAN-B2 | 26.6 | 5.0 | 82.8 | Google Drive, Tsinghua Cloud,Hugging Face 🤗, |
| VAN-B3 | 44.8 | 9.0 | 83.9 | Google Drive, Tsinghua Cloud, Hugging Face 🤗 |
| VAN-B4 | TODO | TODO | TODO | TODO |
1. Pytorch >= 1.7
2. timm == 0.4.12
We use 8 GPUs for training by default. Run command (It has been writen in train.sh):
MODEL=van_tiny # van_{tiny, small, base, large}
DROP_PATH=0.1 # drop path rates [0.1, 0.1, 0.1, 0.2] for [tiny, small, base, large]
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash distributed_train.sh 8 /path/to/imagenet \
--model $MODEL -b 128 --lr 1e-3 --drop-path $DROP_PATHRun command (It has been writen in eval.sh) as:
MODEL=van_tiny # van_{tiny, small, base, large}
python3 validate.py /path/to/imagenet --model $MODEL \
--checkpoint /path/to/model -b 128
Our implementation is mainly based on pytorch-image-models and PoolFormer. Thanks for their authors.
This repo is under the Apache-2.0 license. For commercial use, please contact the authors.
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