[Project page] [Paper]
Code for 'Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images', Daan de Geus and Gijs Dubbelman, WACV 2023.
This code applies Intra-Batch Supervision to Mask2Former, and is built upon the official Mask2Former code.
See installation instructions.
- See Preparing Datasets for Mask2Former.
- See Getting Started with Mask2Former.
- To prepare the datasets for our crop sampling, run these two commands:
python mask2former/data/datasets/prepare_cityscapes_sampling.pypython mask2former/data/datasets/prepare_mapillary_sampling.py
Results and models on Cityscapes.
| Method | Crop sampling | Backbone | Iters | PQ | PQ_th | PQ_st | Acc_th | Prec_th | config | model |
|---|---|---|---|---|---|---|---|---|---|---|
| Mask2Former | no | R50 | 90k | 62.1 | 55.2 | 67.2 | 87.1 | 93.3 | config | TBD |
| Mask2Former + IBS | yes | R50 | 90k | 62.4 | 55.7 | 67.3 | 87.6 | 94.1 | config | TBD |
Results and models on Mapillary Vistas.
| Method | Crop sampling | Backbone | Iters | PQ | PQ_th | PQ_st | Acc_th | Prec_th | config | model |
|---|---|---|---|---|---|---|---|---|---|---|
| Mask2Former | no | R50 | 300k | 41.5 | 33.3 | 52.4 | 71.7 | 78.8 | config | TBD |
| Mask2Former + IBS | yes | R50 | 300k | 42.2 | 34.9 | 52.0 | 75.7 | 84.1 | config | TBD |
Shield:
This code builds upon the official Mask2Former code. 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.
Please consider citing our work if it is useful for your research.
@inproceedings{degeus2023ibs,
title={Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images},
author={{de Geus}, Daan and Dubbelman, Gijs},
booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year={2023}
}
If you use Mask2Former in your research or wish to refer to the baseline results published in the Model Zoo, please also refer to the original Mask2Former paper.
@inproceedings{cheng2022mask2former,
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}
}Code is largely based on Mask2Former, which is largely based on MaskFormer (https://github.com/facebookresearch/MaskFormer).