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bisenetv1

README.md

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 15.39 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.