History

xieenze 073b02d986 update		2021-06-13 00:32:37 +08:00
..
README.md	update	2021-06-13 00:32:37 +08:00
deeplabv3_m-v2-d8_512x512_160k_ade20k.py	update	2021-06-13 00:32:37 +08:00
deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py	update	2021-06-13 00:32:37 +08:00
deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py	update	2021-06-13 00:32:37 +08:00
deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py	update	2021-06-13 00:32:37 +08:00
fcn_m-v2-d8_512x512_160k_ade20k.py	update	2021-06-13 00:32:37 +08:00
fcn_m-v2-d8_512x1024_80k_cityscapes.py	update	2021-06-13 00:32:37 +08:00
pspnet_m-v2-d8_512x512_160k_ade20k.py	update	2021-06-13 00:32:37 +08:00
pspnet_m-v2-d8_512x1024_80k_cityscapes.py	update	2021-06-13 00:32:37 +08:00

README.md

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Introduction

[ALGORITHM]

@inproceedings{sandler2018mobilenetv2,
  title={Mobilenetv2: Inverted residuals and linear bottlenecks},
  author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4510--4520},
  year={2018}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	download
FCN	M-V2-D8	512x1024	80000	3.4	14.2	61.54	-	model \| log
PSPNet	M-V2-D8	512x1024	80000	3.6	11.2	70.23	-	model \| log
DeepLabV3	M-V2-D8	512x1024	80000	3.9	8.4	73.84	-	model \| log
DeepLabV3+	M-V2-D8	512x1024	80000	5.1	8.4	75.20	-	model \| log

ADE20k

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	download
FCN	M-V2-D8	512x512	160000	6.5	64.4	19.71	-	model \| log
PSPNet	M-V2-D8	512x512	160000	6.5	57.7	29.68	-	model \| log
DeepLabV3	M-V2-D8	512x512	160000	6.8	39.9	34.08	-	model \| log
DeepLabV3+	M-V2-D8	512x512	160000	8.2	43.1	34.02	-	model \| log