Empty shelf detection: Datasets & Models

Manually auditing shelves for out-of-stock items is slow, inconsistent, and hard to scale across a large store footprint, and gaps often go unnoticed until a customer walks away empty-handed. Automating empty shelf detection with computer vision gives retail teams near-real-time visibility into on-shelf availability without adding labor hours to every aisle. Instead of relying on periodic walkthroughs, cameras and edge devices continuously monitor stock levels and surface problems the moment they appear. This turns shelf gaps into actionable alerts that route directly to floor staff or replenishment systems.

Faster gap detection means quicker restocking, fewer lost sales, and a more consistent in-store experience across every location. The workflow integrates with existing store cameras, mobile devices, and inventory or planogram compliance tools, so retailers can deploy without new hardware in many cases. Because detection runs the same way in one aisle or a thousand stores, the approach scales cleanly across a retail chain.

1. Upload an image: Drop any photo into the widget above to try empty shelf detection for free, right on this page.
2. Preview instant results: The model detects empty shelf, product package, price tag, shelf gap, stocked shelf 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.