leaderj1001 / Mobilenetv3 Pytorch
Licence: mit
Implementing Searching for MobileNetV3 paper using Pytorch
Stars: ✭ 243
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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
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
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
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