Features:
Multi-class image classification using TensorFlow. OpenCV for image preprocessing. Streamlit for an interactive web-based UI. Supports uploading images for real-time predictions.
Live Demo
🔗 https://stimageclassifyhs.streamlit.app/
Installation & Setup
Follow these steps to set up the project locally:
Clone the Repository:
git clone https://github.com/Strange0000/Image-Classification-Project.git cd Image-Classification-Project
Set Up a Virtual Environment:
python -m venv .venv
source .venv/bin/activate # On Windows, use .venv\Scripts\activate
Install Dependencies: pip install -r requirements.txt
Run the Streamlit App: streamlit run app.py
Model Details
The model is built using TensorFlow/Keras and trained on labeled image data. The trained model is stored as happysadmodel.keras and loaded at runtime. Uses OpenCV for image processing before classification.
File Structure
Image-Classification-Project/ │── models/ | │ ├── happysadmodel.keras # Trained model | | │── app.py # Main Streamlit app | | │── requirements.txt # Dependencies | | │── README.md # Project documentation│── .gitattributes # LFS tracking for large files |
Issues & Contributions If you encounter any issues, feel free to open an issue. Contributions are welcome!
License This project is licensed under the MIT License.