MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation
Bowen Cheng, Alexander G. Schwing, Alexander Kirillov
### Mask2Former Checkout [Mask2Former](https://github.com/facebookresearch/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](INSTALL.md). ## Getting Started See [Preparing Datasets for MaskFormer](datasets/README.md). See [Getting Started with MaskFormer](GETTING_STARTED.md). ## Model Zoo and Baselines We provide a large set of baseline results and trained models available for download in the [MaskFormer Model Zoo](MODEL_ZOO.md). ## License Shield: [![CC BY-NC 4.0][cc-by-nc-shield]][cc-by-nc] The majority of MaskFormer is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE). [![CC BY-NC 4.0][cc-by-nc-image]][cc-by-nc] [cc-by-nc]: http://creativecommons.org/licenses/by-nc/4.0/ [cc-by-nc-image]: https://licensebuttons.net/l/by-nc/4.0/88x31.png [cc-by-nc-shield]: https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg 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). ## Citing MaskFormer If you use MaskFormer 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{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} } ```