# ADE20K Adaptation

UPerNet is a semantic segmentation model based on the ConvNeXt-Tiny architecture, suitable for image segmentation tasks.

UPerNet image segmentation model based on ConvNeXt architecture, suitable for semantic segmentation tasks

A Transformer-based semantic segmentation model optimized for the ADE20K dataset

SegFormer encoder fine-tuned on ImageNet-1k, featuring a hierarchical Transformer architecture for semantic segmentation tasks

SegFormer is a Transformer-based semantic segmentation model whose encoder has been fine-tuned on Imagenet-1k.

SegFormer is a Transformer-based semantic segmentation model featuring a hierarchical encoder and lightweight MLP decoder design, excelling in benchmarks like ADE20K and Cityscapes.

SegFormer is a semantic segmentation model based on Transformer architecture, featuring a hierarchical encoder and lightweight MLP decoder design.