| VAN | |
| This model is in maintenance mode only, we don't accept any new PRs changing its code. | |
| If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0. | |
| You can do so by running the following command: pip install -U transformers==4.30.0. | |
| Overview | |
| The VAN model was proposed in Visual Attention Network by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu. | |
| This paper introduces a new attention layer based on convolution operations able to capture both local and distant relationships. This is done by combining normal and large kernel convolution layers. The latter uses a dilated convolution to capture distant correlations. | |
| The abstract from the paper is the following: | |
| While originally designed for natural language processing 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, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc. Code is available at this https URL. | |
| Tips: | |
| VAN does not have an embedding layer, thus the hidden_states will have a length equal to the number of stages. | |
| The figure below illustrates the architecture of a Visual Attention Layer. Taken from the original paper. | |
| This model was contributed by Francesco. The original code can be found here. | |
| Resources | |
| A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with VAN. | |
| [VanForImageClassification] is supported by this example script and notebook. | |
| See also: Image classification task guide | |
| If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. | |
| VanConfig | |
| [[autodoc]] VanConfig | |
| VanModel | |
| [[autodoc]] VanModel | |
| - forward | |
| VanForImageClassification | |
| [[autodoc]] VanForImageClassification | |
| - forward |