🏆 Animal Classifier Web Application

A web-based application for classifying images of animals using deep learning. The application can classify images into five categories: dog, cow, cat, lamb, and zebra.

Project Group No.: 1
Group Members:

• Fahim Muntashir (2021183642)
• Md Abul Bashar Nirob (2022198042)
• Tausib Abrar (2121446642)
• Muhammad Omar Mahin Jinnat (2012826642)

📌 Project Overview

This project consists of two main components:

1. A deep learning model trained to classify animal images
2. A web application that provides a user-friendly interface for image classification

Features

• 🖼️ Drag-and-drop image upload
• 📱 Responsive design for all devices
• ⚡ Real-time image classification
• 📊 Confidence score display
• 🎨 Modern and intuitive UI
• 🔄 Support for multiple image formats (PNG, JPG, JPEG, GIF)

🎥 Demo

Watch how the Animal Classifier works in action!

🚀 Running the Project

Local Setup

Follow the installation steps below to run the project locally.

Cloud Platforms

The project can also be run on cloud platforms:

Google Colab

• Access free GPU resources
• Run the web application using ngrok
• See colab_kaggle_setup.md for detailed instructions

Kaggle

• Use Kaggle's GPU resources
• Deploy the web application
• See colab_kaggle_setup.md for detailed instructions

��️ Technical Stack

Backend

• Python 3.x
• Flask - Web framework
• TensorFlow/Keras - Deep learning framework
• Pillow - Image processing

Frontend

• HTML5
• CSS3 with custom styling
• JavaScript (Vanilla)
• Bootstrap 5 - UI framework
• Font Awesome - Icons

📂 Project Structure

animal-classifier/
├── static/
│   ├── css/
│   │   └── style.css
│   ├── js/
│   │   └── main.js
│   └── uploads/
├── templates/
│   └── index.html
├── images/
│   ├── cat/
│   ├── cow/
│   ├── dog/
│   ├── lamb/
│   └── zebra/
├── app.py
├── preprocess.py
├── train.py
├── predict.py
├── requirements.txt
└── README.md

🚀 How to Run the Project

Prerequisites

• Python 3.x
• pip (Python package manager)
• Virtual environment (recommended)

Installation Steps

1. Clone the repository

git clone https://github.com/FahimMuntashir/animal-classifier.git
cd animal-classifier

2. Create and activate virtual environment

# On Windows
python -m venv venv
venv\Scripts\activate

# On macOS/Linux
python -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Train the model (if not already trained)

python train.py

5. Run the web application

python app.py

6. Access the application

• Open your web browser
• Go to http://localhost:5000

💻 Using the Application

1. Upload an Image

   • Drag and drop an image into the upload zone
   • Or click the upload zone to select an image

2. Classify the Image

   • Click the "Classify Image" button
   • Wait for the prediction to be processed

3. View Results

   • The predicted animal class will be displayed
   • The confidence score will be shown
   • You can upload another image to try again

🎯 Model Performance

• Accuracy Target: 90%+
• Classes Supported: 5 (dog, cow, cat, lamb, zebra)
• Model Architecture: MobileNetV2 (Transfer Learning)
• Training Data: 100 images per class (500 total)

🔧 Development

Adding New Features

1. Fork the repository
2. Create a new branch
3. Make your changes
4. Submit a pull request

Customizing the UI

• Modify static/css/style.css for styling changes
• Edit templates/index.html for layout changes
• Update static/js/main.js for functionality changes

📝 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📞 Support

For support, please open an issue in the GitHub repository or contact the project maintainers.

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Made with ❤️ by Group 1