# ADE20K Dataset

UPerNet semantic segmentation model based on Swin Transformer architecture, suitable for high-precision image segmentation tasks

UPerNet semantic segmentation model based on Swin Transformer small architecture, suitable for scene parsing tasks like ADE20K

UPerNet semantic segmentation model based on ConvNeXt-Large encoder, suitable for scene parsing tasks like ADE20K

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

UPerNet image segmentation model based on ConvNeXt-Tiny encoder, suitable for semantic segmentation tasks

PyTorch-based Segformer model for semantic segmentation tasks, pre-trained on the ADE20K dataset

PyTorch-based Segformer image segmentation model, suitable for semantic segmentation tasks on the ADE20K dataset

This is a Mask2Former instance segmentation model fine-tuned on the ADE20K-mini dataset, capable of identifying and segmenting different object instances in images.