GoS_AI_Project

🧠 Overview

GoS_AI_Project is a deep learning system combining image classification and object detection. Designed for smart educational or security applications, this project uses powerful neural networks to recognize and detect objects in images, with a focus on deployment efficiency and accuracy.

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✨ Features

• ✅ Image classification using pretrained CNN models (ResNet, VGG16, MobileNet)
• 🎯 Image Classification with custom datasets
• 📊 Performance metrics visualization (Accuracy, F1-Score, etc.)
• 🧠 Future-ready with support for self-supervised learning and edge deployment

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🛠️ Tech Stack

🔹 Frontend

• Angular (for future real-time monitoring dashboard or visual results display)

🔹 Backend

• Django (REST API for serving predictions and managing models)

🔹 Deep Learning Frameworks

• TensorFlow / Keras
• PyTorch

🔹 Other Tools

• OpenCV – Image manipulation
• Matplotlib, Seaborn – Visualization
• Pandas, NumPy – Data manipulation
• Scikit-learn – Evaluation metrics
• Flask / FastAPI – Lightweight deployment
• Unsloth / LoRA – LLM fine-tuning
• Ollama – Advanced language model fine-tuning and inference

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🚀 Getting Started

Prerequisites

• Node.js (v18+)
• Python (v3.10+)
• Django (v4+)
• Angular CLI (v15+)

Installation Steps

1. Clone the repository

git clone https://github.com/RawCooked/GoS_AI_Project.git
cd GoS_AI_Project

2. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver

3. Frontend Setup

cd ../frontend
npm install
ng serve

4. Access the App Open http://localhost:4200 in your browser for the frontend and http://localhost:8000 for the backend.

2️⃣ GitHub Organization

This is how this repo is organized:

/dataset
     /Act
          /Artistical-talent-detection
               /Datasets
               /Notebooks
          /Mathematical-logical-thinking
               /Notebooks
          /Singing-talent-detection
               /Notebooks
               /Datasets
                    /Audio-Preview
     /Engage
     /Investigate

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🚧 Future Improvements

• 📚 Self-supervised learning for semi-labeled datasets
• ⚡ Real-time optimization (quantization, pruning)
• 🧠 Edge deployment on Raspberry Pi
• 🔋 Energy-efficient architectures

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🙌 Acknowledgments

• Inspired by Our Dear Professors (❁´◡`❁) & Personal Experiences
• Special thanks to ESPRIT School of Engineering for their continuous support and guidance

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👥 Authors

• Idriss Ben Moussa (RawCooked) | GitHub
• Achref Baaboura | GitHub
• Khalil Tombari | GitHub
• Heni Ouerfelli | GitHub
• Maha Mansi | GitHub
• Azer Fattouch | GitHub