What is the difference between ANN vs CNN?
I am currently engaged in a particular task of leading a particular team of data scientists who are tasked with the development of a computer version solution for a particular retail company to classify product images. However, my team is confused between using Artificial neural networks and convolutional neural networks. How can I guide them?
In the context of data science, here are the differences given between Artificial neural networks and convolutional neural networks:-
Image complexity
If the images are relatively simple and do not have complex patterns then you should use the ANN as it can offer more suitable functionalities as compared to others.
Spatial relationship
If the images have a Spatial relationship between pixels then CNN can be more suitable than ANN.
Feature extraction
CNN is famous for performing feature extraction as a part of their inherent service which would offer you a more well-suited image classification task.
Here is a simplified example given of using a CNN for image classification by using Tensorflow:-
Import tensorflow as tf
From tensorflow.keras import layers, models
# Define a simple CNN model
Model = models.Sequential([
Layers.Conv2D(32, (3, 3), activation=’relu’, input_shape=(224, 224, 3)),
Layers.MaxPooling2D((2, 2)),
Layers.Conv2D(64, (3, 3), activation=’relu’),
Layers.MaxPooling2D((2, 2)),
Layers.Conv2D(128, (3, 3), activation=’relu’),
Layers.MaxPooling2D((2, 2)),
Layers.Flatten(),
Layers.Dense(128, activation=’relu’),
Layers.Dense(num_classes, activation=’softmax’)
])
# Compile the model
Model.compile(optimizer=’adam’,
Loss=’categorical_crossentropy’,
Metrics=[‘accuracy’])
# Train the model
Model.fit(train_images, train_labels, epochs=10, batch_size=32, validation_data=(val_images, val_labels))