Dogs vs. Cats Image Classification

This project demonstrates an image classification task using a Convolutional Neural Network (CNN) to classify images of dogs and cats. The model is trained on the Dogs vs. Cats dataset.

Getting Started

Follow these instructions to set up and run the project on your local machine.

Prerequisites

• Python 3.x
• TensorFlow
• Keras
• OpenCV
• Kaggle API Key (if using Kaggle datasets)

Installation

1. Clone the repository:

   git clone https://github.com/anchal9670/dogs-vs-cats-Classification.git
   cd dogs-vs-cats

2. Download the Dogs vs. Cats dataset from Kaggle:

   pip install kaggle
   kaggle datasets download -d salader/dogs-vs-cats
   unzip dogs-vs-cats.zip -d dataset/
   

Model Architecture

The CNN model consists of multiple convolutional layers, batch normalization, max-pooling, and fully connected layers. The architecture is defined in the model.py file.

Results

After training for a certain number of epochs, the model achieves a validation accuracy of approximately XX%.