Dog - Cat Classifier

A binary classifier to distinguish between cats and dogs.

I have made use of Convolutional Neural Networks with Keras from scratch and achieved 96% accuracy.

Sample Input Image

I trained the model on 4000 images of each category and tested on a 1000 images.

Input of a CNN contains images with numerical values in each pixel arranged spatially along the width, height and depth (channels). The goal of the total architecture is to get a probability score of an image belonging to a certain class by learning from these numerical values arranged spatially. In the process, we perform operations like pooling and convolutions on these numerical values to squeeze and stretch them along the depth.