Shelf monitoring: Datasets & Models

Manual shelf audits in retail are slow, inconsistent, and expensive, associates walk aisles with clipboards or scanners, and by the time a report is compiled the shelf state has already changed. Out-of-stock products directly cost sales, and misplaced items erode planogram compliance that brands pay to maintain. Automating shelf monitoring with computer vision gives near real-time visibility into stock gaps and lets staff focus restocking effort where it actually matters. For retail tech teams, detection-based monitoring scales the same audit across hundreds of stores without adding headcount.

A detection model like RF-DETR delivers accurate, repeatable shelf reads that plug into existing camera feeds and mobile capture apps, so you can start monitoring without new hardware. Teams gain faster gap detection, measurable improvements in on-shelf availability, and audit data that scales across many locations. Because the output is structured bounding-box data, it integrates cleanly with inventory dashboards and replenishment alerts.

1. Upload an image: Drop any photo into the widget above to try shelf monitoring for free, right on this page.
2. Preview instant results: The model detects product package, empty shelf space, price tag, bottle, box within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.