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 |
