https://github.com/leaderj1001/MobileNetV3-Pytorch

Implementing Searching for MobileNetV3 paper using Pytorch
https://github.com/leaderj1001/MobileNetV3-Pytorch

cifar-10 cifar-100 imagenet mobilenetv3 pytorch searching-for-mobilenetv3

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Implementing Searching for MobileNetV3 paper using Pytorch

• Host: GitHub
• URL: https://github.com/leaderj1001/MobileNetV3-Pytorch
• Owner: leaderj1001
• License: mit
• Created: 2019-05-08T06:18:39.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-07-04T00:41:50.000Z (over 5 years ago)
• Last Synced: 2024-08-01T22:50:11.388Z (3 months ago)
• Topics: cifar-10, cifar-100, imagenet, mobilenetv3, pytorch, searching-for-mobilenetv3
• Language: Python
• Homepage:
• Size: 67.2 MB
• Stars: 290
• Watchers: 9
• Forks: 71
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Implementing Searching for MobileNetV3 paper using Pytorch

- The current model is a very early model. I will modify it as a general model as soon as possible.

## Paper

- [Searching for MobileNetV3 paper](https://arxiv.org/abs/1905.02244)

- Author: Andrew Howard(Google Research), Mark Sandler(Google Research, Grace Chu(Google Research), Liang-Chieh Chen(Google Research), Bo Chen(Google Research), Mingxing Tan(Google Brain), Weijun Wang(Google Research), Yukun Zhu(Google Research), Ruoming Pang(Google Brain), Vijay Vasudevan(Google Brain), Quoc V. Le(Google Brain), Hartwig Adam(Google Research)

## Todo

- Experimental need for ImageNet dataset.

- Code refactoring

## MobileNetV3 Block

![캡처](https://user-images.githubusercontent.com/22078438/57360577-6f30d000-71b5-11e9-89a6-24034a3ecdde.PNG)

## Experiments

- For CIFAR-100 data, I experimented with resize (224, 224).


| Datasets | Model | acc1 | acc5 | Epoch  | Parameters

| :---: | :---: | :---: | :---: | :---: | :---: |

CIFAR-100 | MobileNetV3(LARGE) | 70.44% | 91.34% | 80 | 3.99M

CIFAR-100 | MobileNetV3(SMALL) | 67.04% | 89.41% | 55 | 1.7M

IMAGENET | MobileNetV3(LARGE) WORK IN PROCESS | | | | 5.15M

IMAGENET | MobileNetV3(SMALL) WORK IN PROCESS | | | | 2.94M

## Usage

### Train

```

python main.py

```

- If you want to change hyper-parameters, you can check "python main.py --help"

Options:

- `--dataset-mode` (str) - which dataset you use, (example: CIFAR10, CIFAR100), (default: CIFAR100).

- `--epochs` (int) - number of epochs, (default: 100).

- `--batch-size` (int) - batch size, (default: 128).

- `--learning-rate` (float) - learning rate, (default: 1e-1).

- `--dropout` (float) - dropout rate, (default: 0.3).

- `--model-mode` (str) - which network you use, (example: LARGE, SMALL), (default: LARGE).

- `--load-pretrained` (bool) - (default: False).

- `--evaluate` (bool) - Used when testing. (default: False).

- `--multiplier` (float) - (default: 1.0).

### Test

```

python main.py --evaluate True

```

- Put the saved model file in the checkpoint folder and saved graph file in the saved_graph folder and type "python main.py --evaluate True".

- If you want to change hyper-parameters, you can check "python test.py --help"

Options:

- `--dataset-mode` (str) - which dataset you use, (example: CIFAR10, CIFAR100), (default: CIFAR100).

- `--epochs` (int) - number of epochs, (default: 100).

- `--batch-size` (int) - batch size, (default: 128).

- `--learning-rate` (float) - learning rate, (default: 1e-1).

- `--dropout` (float) - dropout rate, (default: 0.3).

- `--model-mode` (str) - which network you use, (example: LARGE, SMALL), (default: LARGE).

- `--load-pretrained` (bool) - (default: False).

- `--evaluate` (bool) - Used when testing. (default: False).

- `--multiplier` (float) - (default: 1.0).

### Number of Parameters

```python

import torch

from model import MobileNetV3

def get_model_parameters(model):

    total_parameters = 0

    for layer in list(model.parameters()):

        layer_parameter = 1

        for l in list(layer.size()):

            layer_parameter *= l

        total_parameters += layer_parameter

    return total_parameters

tmp = torch.randn((128, 3, 224, 224))

model = MobileNetV3(model_mode="LARGE", multiplier=1.0)

print("Number of model parameters: ", get_model_parameters(model))

```

## Requirements

- torch==1.0.1