Skip to content
Implementing Searching for MobileNetV3 paper using Pytorch
Branch: master
Clone or download
Latest commit 80beb97 May 26, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
checkpoint [Update] Large acc1: 70.44%, Small acc1: 67.04% May 10, 2019
LICENSE Create LICENSE May 26, 2019
README.md Update README.md May 14, 2019
main.py Update main.py May 24, 2019
model.py Update model.py May 24, 2019
preprocess.py [Update] add imagenet dataset training May 11, 2019

README.md

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
  • 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

Experiments

  • For CIFAR-100 data, I experimented with resize (224, 224).
  • Lack of GPUs... T.T
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

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
You can’t perform that action at this time.