# Masked Attention Mechanism

Mask2Former is a Transformer-based general-purpose image segmentation model that unifies instance, semantic, and panoptic segmentation tasks.

Mask2Former is a unified image segmentation model based on Transformer, capable of handling instance segmentation, semantic segmentation, and panoptic segmentation tasks.

Mask2Former model trained on the ADE20k panoptic segmentation dataset using a Swin large backbone network, employing a unified paradigm to handle instance segmentation, semantic segmentation, and panoptic segmentation tasks.

A large-scale Mask2Former model based on the Swin backbone network, designed for general image segmentation tasks, unifying instance segmentation, semantic segmentation, and panoptic segmentation.

Mask2Former model based on Swin backbone network, specifically optimized and trained for panoptic segmentation tasks on the Cityscapes dataset

Mask2Former model based on Swin-Tiny backbone, optimized for Cityscapes panoptic segmentation tasks

Mask2Former is a Transformer-based unified image segmentation model supporting instance segmentation, semantic segmentation, and panoptic segmentation tasks, utilizing masked attention mechanism to enhance performance

A large-scale version of Mask2Former based on the Swin backbone network, specifically trained for panoptic segmentation tasks on the COCO dataset