Building a Cat vs Dog Classification Model: A Step-by-Step Guide

Building a Cat vs Dog Classification Model: A Step-by-Step Guide 🐾🐶🏃‍♂️👩‍💻📊⚙️

In this article, we will take on an exciting project and build a cat vs dog image classification model using a subset of the popular Caggle dataset. Let's get started!

Why Choose Cat vs Dog Image Classification? 🤔

• Binary classification problem: just two classes (cats or dogs)
• Color images with three channels (R, G, B)
• Real-world application: pet identification and more

Getting Started 🏃‍♂️

1. Download the dataset: Caggle Cat vs Dog Dataset contains 4000 images in total: 2000 for training, 1000 for validation, and 1000 for testing.
2. Structure the data: The dataset consists of three main folders (Train, Valid, Test). Each folder contains two subfolders (cats and dogs), with 1000 images each in the training set, 500 images each in the validation set, and 1000 images for testing.

Model Building 👩‍💻

Step 1: Creating a CNN Model (70-75% accuracy) 📊

• Create a CNN model with four convolutional layers paired with pooling layers.
• This model will be able to achieve validation accuracy in the range of 70 to 75%.

Step 2: Data Augmentation (80%+ accuracy) 📈

• Improve performance by augmenting data using linear transformations, rotations, zooming, etc.
• Run the model again to achieve an accuracy over 80%.

Step 3: Using Pre-Trained Architectures (90%+ accuracy) 🚀

• Utilize one of the pre-learned architectures to classify the cat vs dog dataset.
• Achieve an accuracy over 90% using a pre-trained architecture.

Wrapping Up 🎉

You will now have a better understanding of:
- Importing images
- Running binary classification with CNNs
- Using pre-trained architectures to solve image classification problemsRead more about the Caggle Cat vs Dog DatasetRead more about the Caggle Cat vs Dog DatasetCheck out our other machine learning projectsCheck out our other machine learning projects