🧠 Image Classification with Keras & TensorFlow

Deployed link - ✨Here

This repository contains a deep learning project built with Keras and TensorFlow for image classification.
The model is trained using convolutional neural networks (CNNs) on a custom dataset and demonstrates the full workflow of preprocessing, model training, evaluation, and saving the trained model.

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📌 Features

• Data preprocessing and augmentation
• Convolutional Neural Network (CNN) model built with Keras
• Training loop with validation monitoring
• Checkpoint saving & early stopping
• Accuracy and loss visualization
• Easily extendable for other datasets

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📂 Project Structure

├── trainFERmodel.ipynb           # Main Jupyter Notebook for training
├── requirements.txt           # Python dependencies
├── README.md                  # Project documentation
├── .gitignore                 # Ignored files/folders
└── realtimedetection.py       # Dataset (ignored in git)

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⚙️ Installation

1. Clone the repository

   git clone https://github.com/BleeGleeWee/Face-Emotion-Recognition.git
   cd Face-Emotion-Recognition
   
   

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

   python -m venv venv
   source venv/bin/activate   # Linux/Mac
   venv\Scripts\activate      # Windows

3. Install dependencies

   pip install -r requirements.txt

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🚀 Usage

1. Place your dataset inside the data/ folder.

   • Training and validation data should be structured as:

     data/
       train/
         class1/
         class2/
         ...
       val/
         class1/
         class2/
         ...
     

2. Open the Jupyter Notebook:

   jupyter notebook trainFERmodel.ipynb

3. Run all cells to train the model.

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📊 Results

• Training accuracy: ~74%
• Validation accuracy: ~63%
• Final model saved in saved_models/ (ignored in git by default).

📌 Training logs and accuracy/loss plots are included in the notebook.

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🛠️ Technologies Used

• Python 3.x
• TensorFlow / Keras
• NumPy, Pandas
• Matplotlib, Seaborn (for visualization)
• Jupyter Notebook

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🤝 Contributing

This project is open for contributions. Feel free to fork the repo, create a branch, and submit pull requests with improvements (e.g., better models, hyperparameter tuning, visualization).

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📜 License

This project is for educational and research purposes only. Feel free to use, modify, and share with attribution.

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🙌 Acknowledgements

• TensorFlow/Keras community
• Open source datasets used for training
• Inspiration from various deep learning research papers and tutorials