# Image Semantic Segmentation

This paper proposes a novel perspective by treating Vision Transformer (ViT) as an image segmentation model and explores its potential in image segmentation tasks.

By rethinking the architecture of Vision Transformer (ViT), this model demonstrates its potential in image segmentation tasks.

Lightweight image segmentation model based on MIT-B0 architecture, optimized for scene parsing tasks

A crack segmentation model based on the SegFormer architecture, fine-tuned on a crack segmentation dataset for detecting crack structures in images

A semantic segmentation model fine-tuned on stamp verification dataset based on nvidia/mit-b0, used for precise segmentation of stamp regions in images

An image segmentation model fine-tuned on the bilal01/stamp-verification-test dataset based on nvidia/mit-b0

BEiT is a model based on the Vision Transformer (ViT) architecture, pre-trained on ImageNet-21k through self-supervised learning and fine-tuned on the ADE20k dataset, specifically designed for image semantic segmentation tasks.