mmsegmentation/configs/bisenetv1

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

Introduction

Official Repo

Code Snippet

BiSeNetV1 (ECCV'2018)

@inproceedings{yu2018bisenet,
  title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},
  author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={325--341},
  year={2018}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
BiSeNetV1 (No Pretrain)	R-18-D32	1024x1024	160000	5.69	31.77	74.44	77.05	config	model | log
BiSeNetV1	R-18-D32	1024x1024	160000	5.69	31.77	74.37	76.91	config	model | log
BiSeNetV1 (4x8)	R-18-D32	1024x1024	160000	11.17	31.77	75.16	77.24	config	model | log
BiSeNetV1 (No Pretrain)	R-50-D32	1024x1024	160000	3.3	7.71	76.92	78.87	config	model | log
BiSeNetV1	R-50-D32	1024x1024	160000	15.39	7.71	77.68	79.57	config	model | log

Note:

• 4x8: Using 4 GPUs with 8 samples per GPU in training.
• Default setting is 4 GPUs with 4 samples per GPU in training.
• No Pretrain means the model is trained from scratch.