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