Convolutional neural networks using augmented image data

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision by demonstrating exceptional performance in various image-related tasks such as object detection, image classification, and segmentation. However, the availability of large, annotated datasets for training CNNs is often a challenge. Fortunately, one effective approach to overcome this limitation is through the use of image data augmentation techniques.

Let’s start from scratch and explain what CNNs are and how they work. Imagine you have a picture, say a photo of a cat, and you want to teach a computer how to recognize that it’s a cat. CNNs are like a special type of computer program that helps computers understand and recognize things in images, just like how you recognize objects in photos.

An image is made up of tiny dots called pixels. Each pixel has a color, and when you put them all together, you get an image. The more...