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MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation

Bowen Cheng, Alexander G. Schwing, Alexander Kirillov

[arXiv] [Project] [BibTeX]

Mask2Former

Checkout Mask2Former, a universal architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas).

Features

• Better results while being more efficient.
• Unified view of semantic- and instance-level segmentation tasks.
• Support major semantic segmentation datasets: ADE20K, Cityscapes, COCO-Stuff, Mapillary Vistas.
• Support ALL Detectron2 models.

Installation

See installation instructions.

Getting Started

See Preparing Datasets for MaskFormer.

See Getting Started with MaskFormer.

Model Zoo and Baselines

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

License

Shield:

The majority of MaskFormer is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

However portions of the project are available under separate license terms: Swin-Transformer-Semantic-Segmentation is licensed under the MIT license.

<a name="CitingMaskFormer"></a>Citing MaskFormer

If you use MaskFormer in your research or wish to refer to the baseline results published in the Model Zoo, please use 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}
}