https://github.com/facebookresearch/mask2former

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"
https://github.com/facebookresearch/mask2former

Last synced: about 1 month ago
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Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

• Host: GitHub
• URL: https://github.com/facebookresearch/mask2former
• Owner: facebookresearch
• License: mit
• Created: 2021-11-24T16:00:44.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-07-29T11:47:14.000Z (3 months ago)
• Last Synced: 2024-10-09T13:23:58.421Z (about 1 month ago)
• Language: Python
• Size: 416 KB
• Stars: 2,489
• Watchers: 28
• Forks: 380
• Open Issues: 158
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

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

[Bowen Cheng](https://bowenc0221.github.io/), [Ishan Misra](https://imisra.github.io/), [Alexander G. Schwing](https://alexander-schwing.de/), [Alexander Kirillov](https://alexander-kirillov.github.io/), [Rohit Girdhar](https://rohitgirdhar.github.io/)

[[`arXiv`](https://arxiv.org/abs/2112.01527)] [[`Project`](https://bowenc0221.github.io/mask2former)] [[`BibTeX`](#CitingMask2Former)]



  




### Features

* A single architecture for panoptic, instance and semantic segmentation.

* Support major segmentation datasets: ADE20K, Cityscapes, COCO, Mapillary Vistas.

## Updates

* Add Google Colab demo.

* Video instance segmentation is now supported! Please check our [tech report](https://arxiv.org/abs/2112.10764) for more details.

## Installation

See [installation instructions](INSTALL.md).

## Getting Started

See [Preparing Datasets for Mask2Former](datasets/README.md).

See [Getting Started with Mask2Former](GETTING_STARTED.md).

Run our demo using Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1uIWE5KbGFSjrxey2aRd5pWkKNY1_SaNq)

Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/Mask2Former)

Replicate web demo and docker image is available here: [![Replicate](https://replicate.com/facebookresearch/mask2former/badge)](https://replicate.com/facebookresearch/mask2former)

## Advanced usage

See [Advanced Usage of Mask2Former](ADVANCED_USAGE.md).

## Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the [Mask2Former Model Zoo](MODEL_ZOO.md).

## License

Shield: [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

The majority of Mask2Former is licensed under a [MIT License](LICENSE).

However portions of the project are available under separate license terms: Swin-Transformer-Semantic-Segmentation is licensed under the [MIT license](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/LICENSE), Deformable-DETR is licensed under the [Apache-2.0 License](https://github.com/fundamentalvision/Deformable-DETR/blob/main/LICENSE).

## Citing Mask2Former

If you use Mask2Former in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.

```BibTeX

@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.

```BibTeX

@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).