Cat VS Dog Classifier

Train a machine to identify whether an image contains a cat or a dog using deep learning techniques. Achieve up to 96% accuracy with transfer learning and the VGG16 model.

Table of Contents

• Introduction
• Technologies/Tools Used
• Description
• Installation
• Usage
• Screenshots
• Contributing
• License

Introduction

Cat VS Dog Classifier is a deep learning project developed with TensorFlow and Streamlit. It uses a pre-trained model to classify images as either cats or dogs, achieving high accuracy rates.

Technologies/Tools Used

• TensorFlow
• Streamlit
• NumPy
• Pandas
• PIL (Python Imaging Library)

Description

The Cat VS Dog Classifier is a Streamlit web application that allows users to upload images and predict whether they contain a cat or a dog. The model is based on a TensorFlow Lite quantized model (quantized_model.tflite), which is loaded using the TensorFlow Lite interpreter. Images are preprocessed using the Python Imaging Library (PIL) and then passed through the model for prediction.

Installation

1. Clone the repository:

   git clone https://github.com/your_username/cat-vs-dog-classifier.git

2. Install dependencies:

   pip install -r requirements.txt

Usage

1. Run the Streamlit web application:

   streamlit run app.py

2. Upload an image using the file uploader.

3. Click the "Predict" button to classify the uploaded image as either a cat or a dog.

Screenshots

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your proposed changes.

License

This project is licensed under the MIT License.