Awesome
CoMFormer: Continual Learning in Semantic and Panoptic Segmentation
Fabio Cermelli, Matthieu Cord, Arthur Douillard
Installation
See installation instructions.
Getting Started
Prepare the datasets
See Preparing Datasets for Mask2Former.
How to configure the methods:
Per-Pixel baseline:
MODEL.MASK_FORMER.PER_PIXEL True
Mask-based methods:
MODEL.MASK_FORMER.SOFTMASK True MODEL.MASK_FORMER.FOCAL True
CoMFormer:
CONT.DIST.PSEUDO True CONT.DIST.KD_WEIGHT 10.0 CONT.DIST.UKD True CONT.DIST.KD_REW True
MiB:
CONT.DIST.KD_WEIGHT 200.0 CONT.DIST.UKD True CONT.DIST.UCE True
PLOP:
CONT.DIST.PSEUDO True CONT.DIST.PSEUDO_TYPE 1 CONT.DIST.POD_WEIGHT 0.001
How to run experiments:
ADE Semantic Segmenation:
- Use config file:
cfg_file=configs/ade20k/semantic-segmentation/maskformer2_R101_bs16_90k.yaml
- 100-50:
CONT.BASE_CLS 100 CONT.INC_CLS 50 CONT.MODE overlap
(see examples inscripts/ade.sh
) - 100-10:
CONT.BASE_CLS 100 CONT.INC_CLS 10 CONT.MODE overlap
(see examples inscripts/ade10.sh
) - 100-5:
CONT.BASE_CLS 100 CONT.INC_CLS 5 CONT.MODE overlap
(see examples inscripts/ade5.sh
)
ADE Panoptic Segmenation:
- Use config file:
cfg_file=configs/ade20k/panoptic-segmentation/maskformer2_R50_bs16_90k.yaml
- 100-50:
CONT.BASE_CLS 100 CONT.INC_CLS 50 CONT.MODE overlap
(see examples inscripts/adps.sh
) - 100-10:
CONT.BASE_CLS 100 CONT.INC_CLS 10 CONT.MODE overlap
(see examples inscripts/adps10.sh
) - 100-5:
CONT.BASE_CLS 100 CONT.INC_CLS 5 CONT.MODE overlap
(see examples inscripts/adps5.sh
)
<a name="Citing"></a>Citing CoMFormer
If you use CoMFormer in your research, please use the following BibTeX entry.
@article{cermelli2023comformer,
title={CoMFormer: Continual Learning in Semantic and Panoptic Segmentation},
author={Fabio Cermelli and Matthieu Cord and Arthur Douillard},
journal={IEEE/CVF Computer Vision and Pattern Recognition Conference},
year={2023}
}
Acknowledgement
The code is largely based on Mask2Former.