# Panoptic Segmentation

A general-purpose image segmentation model based on Swin Transformer, unifying instance/semantic/panoptic segmentation tasks

Mask2Former is a unified image segmentation framework capable of handling instance segmentation, semantic segmentation, and panoptic segmentation tasks. This model is based on the Swin-Tiny backbone network and has been fine-tuned for semantic segmentation on the Cityscapes dataset.

Small version of Mask2Former based on Swin backbone network, specifically trained for Cityscapes semantic segmentation tasks

Mask2Former is a general-purpose image segmentation model based on Transformer architecture, capable of handling instance segmentation, semantic segmentation, 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.

A large-scale version based on the Swin backbone network, trained on the ADE20k semantic segmentation dataset, employing a unified paradigm for image segmentation tasks.

Mask2Former is a universal image segmentation model capable of handling instance segmentation, semantic segmentation, and panoptic segmentation tasks by predicting a set of masks and their corresponding labels.

A general-purpose image segmentation model trained on the ADE20k dataset, using a unified framework to handle instance/semantic/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.

A large-scale Mask2Former model based on the Swin backbone network, specifically trained for Cityscapes semantic segmentation tasks, adopting a unified architecture for various image segmentation tasks.

A compact Mask2Former model based on Swin backbone network, optimized for panoptic segmentation tasks on the Cityscapes dataset

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 small-scale version of Mask2Former based on Swin backbone network, optimized for panoptic segmentation tasks on the COCO dataset

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

The Mask2Former model based on the Swin backbone network, trained on the COCO panoptic segmentation dataset, adopts a unified paradigm to handle instance segmentation, semantic segmentation, and panoptic segmentation tasks.

A unified single Transformer architecture for image segmentation, supporting three major tasks: semantic segmentation, instance segmentation, and panoptic segmentation

The first multi-task universal image segmentation framework supporting semantic/instance/panoptic segmentation with a single model

DETR is an end-to-end object detection model based on Transformer architecture, using ResNet-50 as the backbone network, trained on the COCO dataset, and supports object detection and panoptic segmentation tasks.

Large-scale MaskFormer model based on Swin backbone network, unifying instance/semantic/panoptic segmentation tasks

A small MaskFormer model based on the Swin backbone network, trained on the COCO dataset for panoptic segmentation tasks.

A panoptic segmentation model based on the Swin backbone network, trained on the COCO dataset, unifying instance/semantic/panoptic segmentation tasks

A panoptic segmentation model trained on the COCO dataset, using a unified paradigm to handle instance/semantic/panoptic segmentation tasks