🐱🐶 Cat vs Dog Deep Learning Image Classification

A deep learning-based image classification project built using PyTorch and Streamlit, capable of predicting whether an uploaded image contains a Cat or a Dog

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🚀 Live Demo

You can try the live version of the app here:
Cat vs Dog Classification Web App

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📌 Overview

This project is a binary image classification task where a Convolutional Neural Network (CNN) is trained to distinguish between cats and dogs.
The model is built using PyTorch, trained on a labeled dataset of cat and dog images, and deployed via Streamlit for real-time predictions.

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🛠 Features

• 📂 Image upload via a simple web interface.

• 🖼 Image preprocessing (resize & normalization) before prediction.

• 📊 Confidence score for predictions.

• ⚡ GPU support for faster inference.

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📦 Installation

1️⃣ Clone the repository

git clone https://github.com/YamenRM/Cat-VS-Dog-DL-Classification.git
cd Cat-VS-Dog-DL-Classification

2️⃣ Install dependencies

pip install -r requirements.txt

🚀 Running the Streamlit App

streamlit run APP/app.py

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📊 Model Performance

Class	Precision	Recall	F1-Score
Cats	0.70	0.80	0.75
Dogs	0.77	0.65	0.71
Accuracy	0.73	-	-

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📊 Model Details

• Architecture: Custom CNN with convolutional, pooling, and fully connected layers.

• Input size: 64×64 pixels, RGB.

• Output: 2 classes (Cat, Dog).

• Framework: PyTorch.

• Training: Done on custom dataset from Cat vs Dog dataset .

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👨‍💻 Author

• YamenRM

• 📧 Email:yamenrafat132@gmail.com

• Palestine | GAZA

• 3RD YEAR AT UP

💪 Stay Strong