Wood Anomaly Detection

AI-Powered Quality Control: Developed a Deep Learning-based system to automatically identify surface anomalies and defects in wood materials for industrial quality assurance.

Model Training & Experimentation: Utilized Google Colab environments to train and optimize computer vision models, leveraging Python for advanced data analysis and model tuning.

High-Performance Backend: Engineered a lightweight, asynchronous API using FastAPI to serve model predictions in real-time with high efficiency.

Modern User Interface: Built a responsive React.js frontend to visualize detection results and provide an intuitive dashboard for managing the analysis workflow.