RockReveal AI — Intelligent Rock Classification System
AI
Deep Learning
Computer Vision
Geology
Education
TensorFlow
Overview
Developed an intelligent rock classification system using computer vision and deep learning to identify rocks from mobile photos. The system democratizes geology education by making rock classification accessible to students, researchers, and enthusiasts through an intuitive mobile experience and Telegram chatbot interface.
Key Responsibilities
- Problem Statement Analysis: Rock and mineral identification typically requires years of geological training and specialized equipment, creating barriers for students, researchers, and hobbyists
- Solution Architecture: Designed an intelligent rock classification system using computer vision and deep learning to identify rocks from mobile photos with high accuracy
- Multi-Platform Development: Built the system as both a pip-installable package and Telegram chatbot for maximum accessibility and usability
- Technical Implementation: Utilized TensorFlow 2.x with transfer learning, employing ResNet, EfficientNet, and MobileNet models for optimal performance
- MLOps Integration: Implemented W&B (Weights & Biases) for experiment tracking, logging, and model version control
- Production Deployment: Containerized the application using Docker and deployed on Railway hosting platform for scalable production use
Technical Achievements
- Built comprehensive rock classification system supporting 7 rock types: Basalt, Granite, Limestone, Marble, Quartzite, Sandstone, and Slate
- Implemented real-time mobile photo classification with sub-second response times
- Developed Telegram chatbot interface for instant rock identification anywhere
- Created pip-installable Python package for programmatic usage
- Integrated advanced computer vision pipeline with data augmentation and hyperparameter optimization
- Set up end-to-end MLOps workflow with experiment tracking and model monitoring
Impact
Educational Democratization: Made geology education accessible to everyone by eliminating the need for specialized equipment and years of training.
Research Acceleration: Enabled automated classification for geological surveys and environmental monitoring applications.
Practical ML Application: Demonstrated successful implementation of transfer learning and computer vision in a specialized domain with real-world utility.