lamthanhnguyen/image-classification-using-cnn

A convolutional neural network approach to classify image using the CIFAR-10 image classification dataset.

Image Classification Using CNN

Image Classification Using CNN Canadian Institute for Advanced Research (CIFAR) provides a dataset that consists of 60000 32x32x3 color images of 10 classes, known as CIFAR-10, with 6000 images per class. There are 50000 training images and 10000 test images. To classify those 10 classes of images a convolutional neural network (CNN) is used here. CNN achieved 85.0% accuracy in the test dataset. The block diagram of the CNN is shown below.

Block Diagram

CNN Architecture

model = Sequential()

# Block 01
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(32, 32, 3)))
model.add(LeakyReLU(alpha=alpha))
model.add(Conv2D(32, (3, 3)))
model.add(LeakyReLU(alpha=alpha))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# Block 02
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(LeakyReLU(alpha=alpha))
model.add(Conv2D(64, (3, 3)))
model.add(LeakyReLU(alpha=alpha))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# Block 03
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(LeakyReLU(alpha=alpha))
model.add(Conv2D(64, (3, 3)))
model.add(LeakyReLU(alpha=alpha))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# Block 04
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
return model

Batch size is chosen 256 and the network is trained for 120 epochs.

Loss curve

Accuracy curve

Output

This is how the outputs look like.

Output for all the images in the 'IMAGES/' folder.

Summary

• Load the CIFAR-10 dataset.
• Normalize training and test data.
• Change labels from integer to categorical.
• Build the model.
• Compile the model.
• Train the model.
• Save the model.
• Classify new test image using the trained model.