Binary Image Classification - Cat vs. Non-Cat

Overview

This project implements a Deep Neural Network (DNN) for Binary Image Classification, specifically designed to classify images as either containing a cat or not. The model is built using TensorFlow, leveraging deep learning techniques to achieve high classification accuracy.

Features

• Deep Neural Network (DNN): Custom architecture optimized for image classification.
• Binary Classification: Determines whether an image contains a cat or not.
• TensorFlow: Implemented using state-of-the-art deep learning frameworks.
• Dataset Handling: Supports image pre-processing and augmentation for better generalization.
• Performance Metrics: Tracks accuracy, precision, recall, and loss over training epochs.

Installation

To run this project, ensure you have Python installed, then install the required dependencies:

pip install tensorflow numpy matplotlib opencv-python

Usage

1. Clone the repository:

   git clone https://github.com/adityaamehra/Cat-vs-dog.git
   cd Cat-vs-dog

2. Run the Jupyter Notebook:

   jupyter notebook main.ipynb

3. Train the model using the provided dataset and evaluate its performance.

Dataset

The model requires a dataset of labeled images containing cats and non-cat images. Ensure the dataset is structured as follows:

/dataset
   /cats
      cat1.jpg
      cat2.jpg
   /dog
      dog1.jpg
      dog2.jpg

Model Architecture

The deep neural network consists of:

• Convolutional Layers for feature extraction
• Pooling Layers for dimensionality reduction
• Fully Connected Layers for final classification
• Softmax Activation for binary output (Cat/Non-Cat)

Performance Evaluation

The model is evaluated using:

• Accuracy
• Precision & Recall
• Confusion Matrix
• Loss Curves

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.