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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) 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