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谢昕辰 725d5aa002 [Feature] support mim (#549) 
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* complement inference time
 		2021-05-31 15:07:24 -07:00
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README.md comment tag (#505) 2021-04-24 09:58:59 -07:00
deeplabv3_m-v2-d8_512x512_160k_ade20k.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00
deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00
deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00
deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00
fcn_m-v2-d8_512x512_160k_ade20k.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00
fcn_m-v2-d8_512x1024_80k_cityscapes.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00
metafile.yml [Feature] support mim (#549) 2021-05-31 15:07:24 -07:00
pspnet_m-v2-d8_512x512_160k_ade20k.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00
pspnet_m-v2-d8_512x1024_80k_cityscapes.py [Feature] Support MobileNetV2 backbone (#86) 2020-09-04 15:35:52 +08:00

README.md

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Introduction

@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) config download
FCN M-V2-D8 512x1024 80000 3.4 14.2 61.54 - config model | log
PSPNet M-V2-D8 512x1024 80000 3.6 11.2 70.23 - config model | log
DeepLabV3 M-V2-D8 512x1024 80000 3.9 8.4 73.84 - config model | log
DeepLabV3+ M-V2-D8 512x1024 80000 5.1 8.4 75.20 - config model | log

ADE20k

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