Shelf Item Counting POC

I need a compact proof-of-concept that can look at a single photo of a store shelf and return one simple number: the total quantity of products it finds. No bounding boxes, no section breakdowns—just the grand total per image displayed on a lightweight web dashboard.

You will receive a small batch of sample shelf photos to start with. I’m open to whichever computer-vision stack you prefer (Python, OpenCV, TensorFlow, PyTorch, YOLO, etc.) as long as the final flow is clear and reproducible:

• I upload or drag-and-drop an image.
• Your model processes it and sends back the count.
• The dashboard records and displays that number next to the image thumbnail.

Accuracy does not need to be retail-grade yet, but the proof must show that the pipeline works end-to-end and can be fine-tuned later for brand recognition or section-level analytics if we extend the project.

Deliverables:
1. Source code with brief setup notes (virtualenv or Docker preferred).
2. Running demo (local or small cloud instance).
3. Short read-me summarising model choice, training approach, and areas for improvement.

I’ll test using a separate set of photos; if the dashboard reports a reasonable count within a few seconds per image, the POC is accepted.