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MengzhangLI d966f98f83 [Feature] Support ICNet (#884 ) * add icnet backbone * add icnet head * add icnet configs * nclass -> num_classes * Support ICNet * ICNet * ICNet * Add ICNeck * Add ICNeck * Add ICNeck * Add ICNeck * Adding unittest * Uploading models & logs * Uploading models & logs * add comment * smaller test_swin.py * try to delete test_swin.py * delete test_unet.py * delete test_unet.py * temp * smaller test_unet.py Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>		2021-09-30 09:31:57 -07:00
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README.md	[Feature] Support ICNet (#884 )	2021-09-30 09:31:57 -07:00
bisenetv1.yml	[Feature] Support ICNet (#884 )	2021-09-30 09:31:57 -07:00
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_r50-d32_4x4_1024x1024_160k_cityscapes.py	[Feature] Support BiSeNetV1 (#851 )	2021-09-29 02:12:57 +08:00
bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py	[Feature] Support BiSeNetV1 (#851 )	2021-09-29 02:12:57 +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

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.