charlespikachu / cssegmentation

CSSegmentation: An Open Source Continual Semantic Segmentation Toolbox Based on PyTorch.

Documents: https://cssegmentation.readthedocs.io/en/latest/

Introduction

CSSegmentation: An Open Source Continual Semantic Segmentation Toolbox Based on PyTorch. You can star this repository to keep track of the project if it's helpful for you, thank you for your support.

Major Features

• High Performance

  The performance of re-implemented CSS algorithms is better than or comparable to the original paper.

• Modular Design and Unified Benchmark

  Various CSS methods are unified into several specific modules. Benefiting from this design, CSSegmentation can integrate a great deal of popular and contemporary continual semantic segmentation frameworks and then, train and test them on unified benchmarks.

• Fewer Dependencies

  CSSegmentation tries its best to avoid introducing more dependencies when reproducing novel continual semantic segmentation approaches.

Benchmark and Model Zoo

Supported Encoder

Encoder	Model Zoo	Paper Link	Code Snippet
ResNet	click	CVPR 2016	click

Supported Decoder

Decoder	Model Zoo	Paper Link	Code Snippet
Deeplabv3	click	ArXiv 2017	click

Supported Runner

Runner	Model Zoo	Paper Link	Code Snippet
ILT	click (under developing)	ICCVW 2019	click
MIB	click (under developing)	CVPR 2020	click
SDR	click (under developing)	CVPR 2021	click
PLOP	click (under developing)	CVPR 2021	click
CAF	click (under developing)	TMM 2022	click
RCIL	click (under developing)	CVPR 2022	click
REMINDER	click (under developing)	CVPR 2022	click
UCD	click (under developing)	TPAMI 2022	click
EWF	click (under developing)	CVPR 2023	click

Supported Datasets

Dataset	Project Link	Paper Link	Code Snippet
ADE20k	Click	CVPR 2017	Click
PASCAL VOC	Click	IJCV 2010	Click

Citation

If you use this framework in your research, please cite this project:

@misc{csseg2023,
    author = {Zhenchao Jin},
    title = {CSSegmentation: An Open Source Continual Semantic Segmentation Toolbox Based on PyTorch},
    year = {2023},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/SegmentationBLWX/cssegmentation}},
}