I’m building an MVP that inspects coal quality from both photos and short videos. The system must return a simple breakdown—e.g., “Coal 75 % / Stone 25 %”—and lay the groundwork for optional size-distribution analysis in later releases. What you’ll have to work with • A batch of truck-loading videos and corresponding lab quality reports. Phased scope Phase 1 – Extract still frames from the supplied videos and organise them into a clean, labelled image set. Phase 2 – Train a computer-vision model (YOLO or a comparable CNN in Python) able to distinguish coal from stone. Baseline accuracy is sufficient for now; we can iterate later. Both real-time and batch inference modes should be supported. Phase 3 – Wrap the model in a lightweight web interface where a user can upload media and immediately see the percentage split. The interface also needs: • File upload history • Downloadable analysis reports • Basic user authentication Preferred stack & tools Python, OpenCV, YOLOv5/YOLOv8 (or similar), Flask/FastAPI for the back end, plus any front-end framework you deem lean and quick to deploy. To help me shortlist quickly, please send: • Links or screenshots of past AI/computer-vision projects (especially anything involving material classification or industrial inspection) • A brief outline of your proposed approach, timeline, and milestone costs If this MVP proves its value, the next stage will be a full mobile application and model refinement, so there’s plenty of room to grow the collaboration.