# Mask Prediction

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

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

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

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

Small-sized Mask2Former model for ADE20k semantic segmentation based on Swin backbone network, using a unified paradigm for image 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.

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

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.

Mask2Former is a Transformer-based unified image segmentation model, utilizing a Swin-Large backbone and fine-tuned on the COCO dataset, specializing in instance segmentation tasks.

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

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

Mask2Former model based on Swin backbone, specifically designed for COCO instance segmentation tasks, using a unified framework to handle segmentation tasks

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.

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