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---
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license: apache-2.0
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tags:
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- image-classification
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datasets:
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- imagenet
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---
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# VAN-Large
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VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
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## Description
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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.
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## Evaluation Results
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| Model | #Params(M) | GFLOPs | Top1 Acc(%) | Download |
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| :-------- | :--------: | :----: | :---------: | :----------------------------------------------------------: |
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| VAN-Tiny | 4.1 | 0.9 | 75.4 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Tiny) |
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| VAN-Small | 13.9 | 2.5 | 81.1 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Small) |
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| VAN-Base | 26.6 | 5.0 | 82.8 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Base), |
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| VAN-Large | 44.8 | 9.0 | 83.9 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Large) |
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### BibTeX entry and citation info
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```bibtex
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@article{guo2022visual,
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title={Visual Attention Network},
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author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
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journal={arXiv preprint arXiv:2202.09741},
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year={2022}
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}
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``` |
