https://github.com/d-li14/mobilenetv3.pytorch

74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet
https://github.com/d-li14/mobilenetv3.pytorch

iccv2019 imagenet mobilenetv3 pretrained-models pytorch-implementation

Last synced: 21 days ago
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74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet

• Host: GitHub
• URL: https://github.com/d-li14/mobilenetv3.pytorch
• Owner: d-li14
• License: mit
• Created: 2019-05-08T17:01:22.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-03-08T04:53:24.000Z (almost 2 years ago)
• Last Synced: 2024-11-21T01:15:49.926Z (23 days ago)
• Topics: iccv2019, imagenet, mobilenetv3, pretrained-models, pytorch-implementation
• Language: Python
• Homepage: https://arxiv.org/abs/1905.02244
• Size: 106 MB
• Stars: 522
• Watchers: 8
• Forks: 125
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-image-classification - unofficial-pytorch : https://github.com/d-li14/mobilenetv3.pytorch
• awesome-image-classification - unofficial-pytorch : https://github.com/d-li14/mobilenetv3.pytorch

README

        
# PyTorch Implementation of MobileNet V3

Reproduction of MobileNet V3 architecture as described in [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) 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](pytorch.org) framework.

# Requirements

## Dataset

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

# Training recipe

* *batch size* 1024

* *epoch* 150

* *learning rate* 0.4 (ramps up from 0.1 to 0.4 in the first 5 epochs)

* *LR decay strategy* cosine

* *weight decay* 0.00004

* *dropout rate* 0.2 (0.1 for Small-version 0.75)

* *no weight decay* biases and BN

* *label smoothing* 0.1 (only for Large-version)

# Models

| Architecture      | # Parameters | MFLOPs | Top-1 / Top-5 Accuracy (%) |

| ----------------- | ------------ | ------ | -------------------------- |

| [MobileNetV3-Large 1.0](https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-1cd25616.pth) | 5.483M | 216.60 | 74.280 / 91.928 |

| [MobileNetV3-Large 0.75](https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-0.75-9632d2a8.pth) | 3.994M | 154.57 | 72.842 / 90.846 |

| [MobileNetV3-Small 1.0](https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-small-55df8e1f.pth) | 2.543M |  56.52 | 67.214 / 87.304 |

| [MobileNetV3-Small 0.75](https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-small-0.75-86c972c3.pth) | 2.042M |  43.40 | 64.876 / 85.498 |

```python

from mobilenetv3 import mobilenetv3_large, mobilenetv3_small

net_large = mobilenetv3_large()

net_small = mobilenetv3_small()

net_large.load_state_dict(torch.load('pretrained/mobilenetv3-large-1cd25616.pth'))

net_small.load_state_dict(torch.load('pretrained/mobilenetv3-small-55df8e1f.pth'))

```

# Citation

```

@InProceedings{Howard_2019_ICCV,

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},

booktitle = {The IEEE International Conference on Computer Vision (ICCV)},

month = {October},

year = {2019}

}

```

If you find this implementation helpful in your research, please also consider citing:

```

@InProceedings{Li_2019_ICCV,

author = {Li, Duo and Zhou, Aojun and Yao, Anbang},

title = {HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions},

booktitle = {The IEEE International Conference on Computer Vision (ICCV)},

month = {October},

year = {2019}

}

```