Reproduction of MobileNet V3 architecture as described in Searching for MobileNetV3 by Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam on ILSVRC2012 benchmark with PyTorch framework.
Download the ImageNet dataset and move validation images to labeled subfolders. To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh
| Architecture | # Parameters | MFLOPs | Top-1 / Top-5 Accuracy (%) |
|---|---|---|---|
| MobileNetV3-Large | 5.145M | 245.58 | |
| MobileNetV3-Small | 3.112M | 57.08 |
Note: The implemented architecture follows Table 1 and 2 in the paper, yet architectural details are vaguely described, rendering mismatches of both parameters and complexity.
from mobilenetv3 import mobilenetv3_large, mobilenetv3_small
net_large = mobilenetv3_large()
net_small = mobilenetv3_small()
# pretrained models will come soon
net_large.load_state_dict(torch.load('pretrained/mobilenetv3-large.pth'))
net_small.load_state_dict(torch.load('pretrained/mobilenetv3-small.pth'))@ARTICLE{2019arXiv190502244H,
author = {{Howard}, Andrew and {Sandler}, Mark and {Chu}, Grace and
{Chen}, Liang-Chieh and {Chen}, Bo and {Tan}, Mingxing and
{Wang}, Weijun and {Zhu}, Yukun and {Pang}, Ruoming and
{Vasudevan}, Vijay and {Le}, Quoc V. and {Adam}, Hartwig},
title = "{Searching for MobileNetV3}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computer Vision and Pattern Recognition},
year = "2019",
month = "May",
eid = {arXiv:1905.02244},
pages = {arXiv:1905.02244},
archivePrefix = {arXiv},
eprint = {1905.02244},
primaryClass = {cs.CV},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190502244H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}