Waiter_group/CNN Plates Classification.md

CNN Plates Classification

Author: Weronika Skowrońska, s444523

As my individual project, I decided to perform a classification of plates images using a Convolutional Neural Network. The goal of the project is to classify a photo of the client's plate as empty, full or dirty, and assign an appropriate value to the given instance of the "Table" class.

Architecture

Architecture of my CNN is very simple. I decided to use two convolutions, each using 32 feature detectors of size 3 by 3, followed by the ReLU activation function and MaxPooling of size 2 by 2.

classifier = Sequential()

classifier.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
classifier.add(MaxPooling2D(pool_size = (2,2)))

classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Flatten())

After flattening, I added a fully connected layer of size 128 (again with ReLU activation function). The output layer consists of 3 neurons with softmax activation function, as I am using the Network for multiclass classification (3 possible outcomes).

classifier.add(Dense(units = 128, activation = "relu"))
classifier.add(Dense(units = 3, activation = "softmax"))

The optimizer of my network is adam, and categorical cross entropy was my choice for a loss function.

classifier.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])

Library

I used keras to implement the network. It let me add some specific features to my network, such as early stopping and a few methods of data augmentation.

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        width_shift_range=0.2,
        height_shift_range=0.1,
        fill_mode='nearest')

This last issue was very important to me, as I did not have many photos to train the network with (altogether there were approximately 1200 of them).

Project implementation

After training the Network, I saved the model which gave me the best results (two keras callbacks, EarlyStopping and ModelCheckpoint were very useful) to a file named "best_model.h5".

# callbacks:
es = EarlyStopping(monitor='val_loss', mode='min', baseline=1, patience = 10)
mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose = 1, period = 10)

Then, I imported the model into our project (The Waiter) using "load_model" utility of keras.models:

from keras.models import load_model
...
saved_model = load_model('best_model.h5')

After coming to each table, the Agent (the waiter) evaluates a randomly selected photo of a plate using the saved model, and assigns the number of predicted class into the "state" attribute of a given table. This information will let perform further actions, based on the predicted outcome.