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History

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

* bisenetv1 on cocostuff164k

* change config_names & delete redundant keys

* pretrain should before lr.

* remove redundancy in bisenetv1_r50-d32

2021-11-17 00:12:02 -08:00

bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py

[Feature] Support BiSeNetV1 (#851 )

2021-09-29 02:12:57 +08:00

bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py

[Feature] Support BiSeNetV1 (#851 )

2021-09-29 02:12:57 +08:00

bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py

[Feature] Support BiSeNetV1 (#851 )

2021-09-29 02:12:57 +08:00

bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py

first commit (#946 )

2021-10-12 20:32:41 -07:00

bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py

[Feature] Support BiSeNetV1 (#851 )

2021-09-29 02:12:57 +08:00

bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

bisenetv1.yml

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

README.md

[Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019 )

2021-11-17 00:12:02 -08:00

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

COCO-Stuff 164k

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
BiSeNetV1 (No Pretrain)	R-18-D32	512x512	160000	-	-	25.45	26.15	config	model \| log
BiSeNetV1	R-18-D32	512x512	160000	6.33	74.24	28.55	29.26	config	model \| log
BiSeNetV1 (No Pretrain)	R-50-D32	512x512	160000	-	-	29.82	30.33	config	model \| log
BiSeNetV1	R-50-D32	512x512	160000	9.28	32.60	34.88	35.37	config	model \| log
BiSeNetV1(No Pretrain)	R-101-D32	512x512	160000	-	-	31.14	31.76	config	model \| log
BiSeNetV1	R-101-D32	512x512	160000	10.36	25.25	37.38	37.99	config	model \| log

Note:

4x8: Using 4 GPUs with 8 samples per GPU in training.
For BiSeNetV1 on Cityscapes dataset, default setting is 4 GPUs with 4 samples per GPU in training.
No Pretrain means the model is trained from scratch.