Image Classification & Digit Detection

🚀 Overview

This project implements image classification and digit detection using machine learning (ML) and deep learning (DL). It leverages convolutional neural networks (CNNs) for digit recognition and traditional ML models for broader classification tasks. Real-time detection is supported using OpenCV.

📌 Features

• Digit Detection: Recognizes handwritten or printed digits using CNNs.
• Image Classification: Classifies images into different categories using ML/DL models.
• Real-time Detection: Uses OpenCV to detect digits and classify objects in real-time.
• Model Training: Trains models on datasets like MNIST, CIFAR-10, or custom datasets.
• Evaluation: Analyzes model accuracy using confusion matrices and performance metrics.
• Deployment: Flask-based API for easy integration.

🛠 Tech Stack

• Programming Language: Python 🐍
• Libraries: TensorFlow/Keras, OpenCV, NumPy, Matplotlib, Scikit-learn
• Framework: Flask (for API deployment)

📂 Project Structure

📦 Image-Classification-Digit-Detection
├── 📁 datasets       # Dataset storage
├── 📁 models         # Pretrained and trained models
├── 📁 notebooks      # Jupyter notebooks for experimentation
├── 📁 src           # Source code for training & detection
│   ├── train.py     # Model training script
│   ├── detect.py    # Digit detection & classification
│   ├── utils.py     # Helper functions
├── 📄 requirements.txt  # Dependencies
├── 📄 README.md     # Project documentation

🔧 Installation

1. Clone the Repository

   git clone https://github.com/yourusername/Image-Classification-Digit-Detection.git
   cd Image-Classification-Digit-Detection

2. Create a Virtual Environment (Optional but Recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

📌 Usage

1️⃣ Train the Model

python src/train.py

2️⃣ Run Digit Detection & Classification

python src/detect.py --image path/to/image.jpg

3️⃣ Deploy API (Optional)

python src/app.py

📊 Results & Evaluation

• Accuracy Reports
• Confusion Matrices
• Model Performance Charts

🤝 Contributions

Feel free to fork, contribute, and submit PRs! 😊

📜 License

This project is licensed under the MIT License.

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📩 Need Help? Open an issue or reach out! 🚀