Inventory counting: Datasets & Models

Manual inventory counting is slow, error-prone, and pulls staff off higher-value work, and cycle counts often lag behind what is actually on the floor. A detection model gives you near real-time visibility into stock levels, flags discrepancies between the WMS and physical reality, and scales across multiple aisles or facilities without adding headcount. For high-throughput warehouses, this means fewer stockouts, faster audits, and reconciliation that keeps pace with receiving and shipping.

Automating counts with computer vision reduces labor hours spent on stock takes and improves count accuracy across large SKU volumes. The workflow runs on existing warehouse cameras and mobile hardware, integrates with WMS and ERP systems through simple API calls, and scales from a single dock door to an entire distribution network. Training on a domain-specific inventory dataset with RF-DETR delivers reliable detection even in cluttered, densely stacked scenes.

1. Upload an image: Drop any photo into the widget above to try inventory counting for free, right on this page.
2. Preview instant results: The model detects cardboard box, pallet, shipping carton, plastic bin, shrink-wrapped package 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.