MobilenetV2

My project is using the TensorFlow framework to implement the MobilenetV2 model to classify flower images. Give me a star 🌟 if you like this repo.

Model Architecture

The MobileNetV2 network architecture is shown below. The image below is excerpted from the author's original article

MobileNet V2 still uses depthwise separable convolutions, but its main building block now looks like this:

There are two type of Convolution layers in MobileNet V2 architecture:

1x1 Convolution
3x3 Depthwise Convolution

These are the two different components in MobileNet V2 model:

Stride 1 Block:

Input
1x1 Convolution with Relu6
Depthwise Convolution with Relu6
1x1 Convolution without any linearity
Add

Stride 2 Block:

Input
1x1 Convolution with Relu6
Depthwise Convolution with stride=2 and Relu6
1x1 Convolution without any linearity

Author

I. Set up environment

Step 1: Make sure you have installed Miniconda. If not yet, see the setup document here
Step 2: cd into MobilenetV2 and use command line

conda env create -f environment.yml

Step 3: Run conda environment using the command

conda activate MobilenetV2

II. Set up your dataset

Download the data:

Download dataset here

Extract file and put folder train and validation to ./data by using splitfolders

train folder was used for the training process
validation folder was used for validating training result after each epoch

This library use ImageDataGenerator API from Tensorflow 2.0 to load images. Make sure you have some understanding of how it works via its document Structure of these folders in ./data

train/
...daisy/
......daisy0.jpg
......daisy1.jpg
...dandelion/
......dandelion0.jpg
......dandelion1.jpg
...roses/
......roses0.jpg
......roses1.jpg
...sunflowers/
......sunflowers0.jpg
......sunflowers1.jpg
...tulips/
......tulips0.jpg
......tulips1.jpg

validation/
...daisy/
......daisy2000.jpg
......daisy2001.jpg
...dandelion/
......dandelion2000.jpg
......dandelion2001.jpg
...roses/
......roses2000.jpg
......roses2001.jpg
...sunflowers/
......sunflowers2000.jpg
......sunflowers2001.jpg
...tulips/
......tulips2000.jpg
......tulips2001.jpg

III. Train your model by running this command line

Review training on colab:

Training script:

python train.py --train-folder ${link_to_train_folder} --valid-folder ${link_to_valid_folder} --classes ${num_classes} --epochs ${epochs}

Example:

python train.py  --train-folder ./data/train --valid-folder ./data/val --classes 5 --epochs 100

There are some important arguments for the script you should consider when running it:

train-folder: The folder of training data
valid-folder: The folder of validation data
Mobilenetv2-save: Where the model after training saved
classes: The number of your problem classes.
batch-size: The batch size of the dataset
lr: The learning rate
droppout: The droppout
label-smoothing: The label smoothing
expansion: The expansion factor
image-size: The image size of the dataset
alpha: Width Multiplier. It was mentioned in the paper on page 4 - Mobilenet V1
rho: Resolution Multiplier, It was mentioned in the paper on page 4 - Mobilenet V1

IV. Predict Process

If you want to test your single image, please run this code:

python predict.py --test-file ${link_to_test_image}

V. Result and Comparision

My implementation

Epoch 88/90
207/207 [==============================] - 78s 377ms/step - loss: 0.2962 - acc: 0.9082 - val_loss: 0.3822 - val_acc: 0.8726

Epoch 00088: val_acc did not improve from 0.88889
Epoch 89/90
207/207 [==============================] - 78s 376ms/step - loss: 0.2930 - acc: 0.9115 - val_loss: 0.3681 - val_acc: 0.8780

Epoch 00089: val_acc did not improve from 0.88889
Epoch 90/90
207/207 [==============================] - 78s 375ms/step - loss: 0.3002 - acc: 0.9103 - val_loss: 0.3803 - val_acc: 0.8862

Epoch 00090: val_acc did not improve from 0.88889

VI. Feedback

If you meet any issues when using this library, please let us know via the issues submission tab.

Nguyendat-bit/MobilenetV2