🐱🐶 Image Classification: Cats vs. Dogs with Transfer Learning

This tutorial demonstrates how to classify images of cats and dogs using transfer learning from a pre-trained deep learning model.

🚀 Overview

A pre-trained model is a saved neural network previously trained on a large dataset, typically for image classification tasks. Instead of training from scratch, we leverage this model’s learned feature maps to classify new images efficiently.

🔹 Key Concepts Covered

Transfer Learning: Adapting a pre-trained network to a new task. Feature Extraction: Using learned patterns from large datasets. Fine-Tuning: Customizing the model for better accuracy.

📂 What’s Inside?

Code to load and preprocess a dataset of cats and dogs 🐾 Steps to apply transfer learning using popular models (e.g., MobileNetV2, ResNet, VGG16) Training and evaluation strategies Guide to improve classification accuracy

🛠️ Requirements

Python TensorFlow/Keras NumPy, Matplotlib, OpenCV (for image processing)

📖 Getting Started

Clone the repo and follow the step-by-step tutorial to build and train your classifier.

bash Copy Edit git clone https://github.com/yourusername/cat-dog-classification.git cd cat-dog-classification pip install -r requirements.txt python train.py

🎯 Why Transfer Learning?

Training a deep model from scratch requires huge datasets and computational power. By using a pre-trained model, we: ✅ Save time ✅ Improve accuracy ✅ Reduce training costs

📌 Contribute

Feel free to fork this project, improve the model, or test different architectures!

📢 Star ⭐ this repo if you find it useful!