Convolutional-Image-Recognition
Introduction
In 2013, Kaggle hosted one of their favorite for-fun competitions: Dogs vs. Cats. Much has since changed in the machine learning landscape, particularly in deep learning and image analysis. Back then, a tensor flow was the diffusion of the creamer in a bored mathematician's cup of coffee. Now, even the cucumber farmers are neural netting their way to a bounty.
At that time, the literature suggested machine classifiers can score about 82.7% accuracy on this task.
Model
Today, we experiment using the full Kaggle dataset (12,500 for dogs and 12,500 for cats) to build a simple Convolutional Neural Network for illustrating the Image Recognition task.
Performance
Our simple 3 level CNN model achieves 85% accuracy.
We can easily apply Transfer Learning for sophiscated model to achieve 97% accuracy, but it is out of scope of this work.
