Dog vs Cat Classification

This is a simple web application built with Flask, TensorFlow, and OpenCV to classify images as either a dog or a cat. The model used for classification is a Convolutional Neural Network (CNN) trained on a dog vs cat dataset.

Vist here

• https://dog-vs-cat-classification.onrender.com/

Features

• Upload an image (PNG, JPG, or JPEG format) of a dog or a cat.
• The model classifies the image as either "Dog" or "Cat".
• Flask-based web interface for user interaction.

Tech Stack

• Backend: Flask (Python web framework)
• Machine Learning: TensorFlow, Keras
• Image Processing: OpenCV
• Deployment: Heroku (or any other preferred platform)

Prerequisites

Before running the application, make sure you have the following installed:

• Python 3.7+
• pip (Python package installer)

Installation

Follow these steps to run the project on your local machine:

1. Clone the repository:

• git clone https://github.com/your-username/dog-vs-cat-classification.git

2. Navigate to the project directory:

• cd dog-vs-cat-classification

3. Create and activate a virtual environment (optional but recommended):

• On Windows:
  • python -m venv venv
  • .\venv\Scripts\activate
• On macOS/Linux:
  • python -m venv venv
  • source venv/bin/activate

4. Install the required dependencies:
   • pip install -r requirements.txt

Running the Application

To run the app locally, use the following command:

• python app.py
• Visit http://127.0.0.1:5000 in your browser to interact with the application.

How to Use

1. Open the application in your web browser.
2. Upload an image of a dog or a cat.
3. The model will process the image and display whether the image is a "Dog" or "Cat".

Model Training

The model used for classification is a pre-trained Keras model (dog_vs_cat_classification.h5). If you want to retrain the model, follow these steps:

1. Gather a dataset of dog and cat images.
2. Preprocess the images (resize to 224x224 and normalize).
3. Train a Convolutional Neural Network (CNN) on the dataset using Keras/TensorFlow.
4. Save the model as dog_vs_cat_classification.h5 and place it in the project directory.