FocalNet (tiny sized model) FocalNet model trained on ImageNet 1k at resolution 224x224. It was introduced in the paper Focal Modulation Networks by Yang et al. and first released in this repository. Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team. Model description Focul Modulation Networks are an alternative to Vision Transformers, where self attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its content, and (iii) element wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state of the art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentat…
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