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Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation (CVPR 2022)

Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar

[arXiv] [Project] [BibTeX]

<div align="center"> <img src="https://bowenc0221.github.io/images/maskformerv2_teaser.png" width="100%" height="100%"/> </div><br/>

Features

Updates

Installation

See installation instructions.

Getting Started

See Preparing Datasets for Mask2Former.

See Getting Started with Mask2Former.

Run our demo using Colab: Open In Colab

Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo: Hugging Face Spaces

Replicate web demo and docker image is available here: Replicate

Advanced usage

See Advanced Usage of Mask2Former.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Mask2Former Model Zoo.

License

Shield: License: MIT

The majority of Mask2Former is licensed under a MIT License.

However portions of the project are available under separate license terms: Swin-Transformer-Semantic-Segmentation is licensed under the MIT license, Deformable-DETR is licensed under the Apache-2.0 License.

<a name="CitingMask2Former"></a>Citing Mask2Former

If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@inproceedings{cheng2021mask2former,
  title={Masked-attention Mask Transformer for Universal Image Segmentation},
  author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar},
  journal={CVPR},
  year={2022}
}

If you find the code useful, please also consider the following BibTeX entry.

@inproceedings{cheng2021maskformer,
  title={Per-Pixel Classification is Not All You Need for Semantic Segmentation},
  author={Bowen Cheng and Alexander G. Schwing and Alexander Kirillov},
  journal={NeurIPS},
  year={2021}
}

Acknowledgement

Code is largely based on MaskFormer (https://github.com/facebookresearch/MaskFormer).