Awesome

A Transformer-based Decoder for Semantic Segmentation with Multi-level Context Mining

Official implementation of the paper "A Transformer-based Decoder for Semantic Segmentation with Multi-level Context Mining",

by Bowen Shi*, Dongsheng Jiang*, Xiaopeng Zhang, Han Li, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian.

[Paper] [Appendix]

Installation

Our code is based on MMSegmentation. For install and data preparation, please refer to the guidelines in MMSegmentation.

Training

Example: train SegFormer-B1 + SegDeformer on ADE20K:

python start_local_train.py --config_file segformer/segformer_mit-b1_512x512_160k_ade20k_segdeformer3.py

Results

ADE20K

Method	Backbone	Crop Size	Lr schd	mIoU	config	log
SegFormer-B1	MiT-B1	512x512	160000	40.97	-	-
SegFormer-B1 + SegDeformer	MiT-B1	512x512	160000	44.12	config	log
SegFormer-B2	MiT-B2	512x512	160000	45.58	-	-
SegFormer-B2 + SegDeformer	MiT-B2	512x512	160000	47.34	config	log
SegFormer-B5	MiT-B5	512x512	160000	49.13	-	-
SegFormer-B5 + SegDeformer	MiT-B5	512x512	160000	50.34	config	log

Note:

We adapt our code to the latest version of MMSegmentation (v0.29.1), while the pretrained MiT models we used are still the old version provided by MMSegmentation (20210726 version) to keep consistent with our paper. Details can be found in this link.
The performance is sensitive to the seed values used, so the results might fluctuate.

Acknowledgement

This reposity is based on the MMSegmentation repository. Thanks for their contributions to the community.

Citation

If you find this repository/work helpful in your research, welcome to cite the paper.

@inproceedings{shi2022transformer,
  title={A Transformer-Based Decoder for Semantic Segmentation with Multi-level Context Mining},
  author={Shi, Bowen and Jiang, Dongsheng and Zhang, Xiaopeng and Li, Han and Dai, Wenrui and Zou, Junni and Xiong, Hongkai and Tian, Qi},
  booktitle={European Conference on Computer Vision},
  pages={624--639},
  year={2022},
  organization={Springer}
}