Pet Image Classifier

Welcome to the Pet Image Classifier! This is a web application that leverages a ResNet Convolutional Neural Network (CNN) type model trained on the Oxford-IIIT Pet Images Dataset to accurately classify images of pets. The application is built using different frameworks and libraries such as Streamlit, TensorFlow.

Table of Contents

• Introduction
• Features
• Model Details
• Usage
  • Prerequisites
• License

Introduction

The Pet Image Classifier is an image classification web app designed to identify various breeds of pets from images. Utilizing the powerful ResNet architecture, our model is trained on the comprehensive Oxford-IIIT Pet Images Dataset, ensuring high accuracy and reliability.

Features

• High Accuracy: Leveraging ResNet CNN for precise classification.
• Scalable: Designed as a web app for easy deployment and accessibility.
• Comprehensive Dataset: Trained on the Oxford-IIIT Pet Images Dataset.

Model Details

The classifier uses a ResNet architecture, which is a deep residual network known for its high performance in image classification tasks. The model is trained on the Oxford-IIIT Pet Images Dataset, which includes 37 breeds of cats and dogs.

Usage

To get started with the Pet Image Classifier, follow the instructions below.

1. Clone the repository.

git clone https://github.com/Pet-Image-classifier.git
cd Pet-Image-Classifier

2. Install the required packages.

pip install -r requirements.txt

3. Run the application.

streamlit run app.py

Prerequisites

• Python 3.7+

License

This project is licensed under the MIT License. See the LICENSE file for details.