MobileNetV3-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Searching for MobileNetV3.

Table of contents

• MobileNetV3-PyTorch
  • Overview
  • Table of contents
  • Download weights
  • Download datasets
  • How Test and Train
    • Test
    • Train model
    • Resume train model
  • Result
  • Contributing
  • Credit
    • Searching for MobileNetV3

Download weights

• Google Driver
• Baidu Driver

Download datasets

Contains MNIST, CIFAR10&CIFAR100, TinyImageNet_200, MiniImageNet_1K, ImageNet_1K, Caltech101&Caltech256 and more etc.

• Google Driver
• Baidu Driver

Please refer to README.md in the data directory for the method of making a dataset.

How Test and Train

Both training and testing only need to modify the config.py file.

Test

• line 29: model_arch_name change to mobilenet_v3_small.
• line 31: model_mean_parameters change to [0.485, 0.456, 0.406].
• line 32: model_std_parameters change to [0.229, 0.224, 0.225].
• line 34: model_num_classes change to 1000.
• line 36: mode change to test.
• line 89: model_weights_path change to ./results/pretrained_models/MobileNetV3_small-ImageNet_1K-73d198d1.pth.tar.

python3 test.py

Train model

• line 29: model_arch_name change to mobilenet_v3_small.
• line 31: model_mean_parameters change to [0.485, 0.456, 0.406].
• line 32: model_std_parameters change to [0.229, 0.224, 0.225].
• line 34: model_num_classes change to 1000.
• line 36: mode change to train.
• line 50: pretrained_model_weights_path change to ./results/pretrained_models/MobileNetV3_small-ImageNet_1K-73d198d1.pth.tar.

python3 train.py

Resume train model

• line 29: model_arch_name change to mobilenet_v3_small.
• line 31: model_mean_parameters change to [0.485, 0.456, 0.406].
• line 32: model_std_parameters change to [0.229, 0.224, 0.225].
• line 34: model_num_classes change to 1000.
• line 36: mode change to train.
• line 53: resume change to ./samples/mobilenet_v3_small-ImageNet_1K/epoch_xxx.pth.tar.

python3 train.py

Result

Source of original paper results: https://arxiv.org/pdf/1905.02244v5.pdf)

In the following table, the top-x error value in () indicates the result of the project, and - indicates no test.

Model	Dataset	Top-1 error (val)	Top-5 error (val)
mobilenet_v3_small-1.0	ImageNet_1K	32.6%(32.3%)	-(12.5%)
mobilenet_v3_large-1.0	ImageNet_1K	24.8%(24.7%)	-(7.4%)

# Download `MobileNetV3_small-ImageNet_1K-73d198d1.pth.tar` weights to `./results/pretrained_models`
# More detail see `README.md<Download weights>`
python3 ./inference.py 

Input:

Output:

Build `mobilenet_v3_small` model successfully.
Load `mobilenet_v3_small` model weights `/MobileNetV3-PyTorch/results/pretrained_models/MobileNetV3_small-ImageNet_1K-73d198d1.pth.tar` successfully.
tench, Tinca tinca                                                          (19.38%)
barracouta, snoek                                                           (7.93%)
platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus (6.00%)
gar, garfish, garpike, billfish, Lepisosteus osseus                         (4.50%)
triceratops                                                                 (1.97%)

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

Credit

Searching for MobileNetV3

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

Abstract

We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 15% compared to MobileNetV2. MobileNetV3-Small is 4.6% more accurate while reducing latency by 5% compared to MobileNetV2. MobileNetV3-Large detection is 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.

[Paper]

@inproceedings{howard2019searching,
  title={Searching for mobilenetv3},
  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 others},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={1314--1324},
  year={2019}
}