In this post we will use a standard computer vision dataset - Dogs vs. Cats dataset that involves classifying photos as either containing a dog or cat.

Although, the dataset seems to be pretty simple, the goal would be to outline the steps required to solve image processing and classification using pytorch and the same pipeline can be later used to apply to any image classification problem at hand.

DataSet

The dogs vs cats dataset refers to a dataset used for a Kaggle machine learning competition held in 2013.

The dataset is comprised of photos of dogs and cats provided as a subset of photos from a much larger dataset of 3 million manually annotated photos. The dataset was developed as a partnership between Petfinder.com and Microsoft.

The dataset was originally used as a CAPTCHA (or Completely Automated Public Turing test to tell Computers and Humans Apart), that is, a task that it is believed a human finds trivial, but cannot be solved by a machine, used on websites to distinguish between human users and bots.

Data Loading and Preprocessing

Let’s first call all the heavenly gods of python (import necessary libraries).

#deep-learning #image-classification #pytorch