# Swin Backbone Network

Large-scale Mask2Former model based on Swin backbone network, specifically trained for Cityscapes semantic segmentation tasks, using a unified architecture for image segmentation tasks

A fine-tuned semantic segmentation model based on the Mask2Former framework, suitable for road scene understanding and autonomous driving applications

A video instance segmentation model trained on the YouTubeVIS-2021 dataset, utilizing the Swin Transformer backbone and Mask2Former unified segmentation architecture

Video Mask2Former model trained on the YouTubeVIS-2021 dataset for video instance segmentation tasks, using Swin Transformer as the backbone network.

A tiny video instance segmentation model trained on the YouTubeVIS-2019 dataset, utilizing the Swin Transformer backbone and Mask2Former unified segmentation architecture

A tiny video instance segmentation model trained on the YouTubeVIS-2021 dataset, utilizing a Swin Transformer backbone network

Mask2Former is a Transformer-based universal image segmentation model, fine-tuned on the COCO dataset for instance segmentation tasks

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

UperNet is a framework for semantic segmentation, utilizing Swin Transformer as the backbone network to achieve pixel-level semantic label prediction.

UperNet is a semantic segmentation framework that uses Swin Transformer as the backbone network, enabling pixel-level semantic label prediction.

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 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.

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 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.

Mask2Former is a general-purpose image segmentation model based on Transformer architecture, this version is specifically fine-tuned for instance segmentation tasks on the Cityscapes dataset

Mask2Former is a unified image segmentation model based on Transformer, using mask attention mechanism to improve performance

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

A mini version of the Mask2Former instance segmentation model trained on the COCO dataset, utilizing the Swin backbone network to handle segmentation tasks uniformly

OneFormer is the first multi-task universal image segmentation framework, achieving semantic segmentation, instance segmentation, and panoptic segmentation tasks with a single model

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

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

A semantic segmentation model trained on the ADE20k dataset, using a unified framework to handle instance/semantic/panoptic segmentation tasks

MaskFormer semantic segmentation model trained on the ADE20k dataset, using a Swin backbone network to unify instance/semantic/panoptic segmentation tasks

A semantic segmentation model trained on the ADE20k dataset, using a unified framework to handle instance/semantic/panoptic segmentation tasks

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

Semantic segmentation model trained on the ADE20k dataset, using a unified framework for instance segmentation, semantic segmentation, and 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