2 dépôts
Embedding modules that divide images into overlapping patches to preserve local continuity and reduce boundary artifacts.
Distinct from Patch Embedding Modules: Specifically implements overlapping patches to avoid boundary artifacts, whereas general patch embedding may use non-overlapping grids.
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Code release for ConvNeXt model
Replaces standard patch embedding with a convolutional layer processing overlapping image patches.
SegFormer is a semantic segmentation framework and transformer-based model designed for pixel-level image classification. It provides a deep learning architecture that assigns class labels to pixels using a hierarchical transformer encoder and a multi-layer perceptron decoder. The framework utilizes a hierarchical transformer encoder to process multi-scale features through a pyramid of blocks and an all-MLP decoder to aggregate these features without complex attention mechanisms. It incorporates overlap patch embedding to preserve local continuity and sequential self-attention reduction to ma
Incorporates overlap patch embedding to preserve local continuity and reduce boundary artifacts during tokenization.