Parcel counting: Datasets & Models

Manual parcel counting is slow, error-prone, and does not scale with peak-season volume; staff tallying packages by hand or relying on scattered barcode scans miss items and create reconciliation gaps between what was received, sorted, and shipped. Automating parcel detection with computer vision gives logistics and warehousing teams a continuous, objective count that runs on the cameras already watching the floor. It catches miscounts at induction, verifies truck loads and unloads, and flags discrepancies before they turn into lost inventory or chargebacks. Builders get a repeatable pipeline instead of periodic manual audits.

Accurate, always-on package counts reduce reconciliation errors and free staff from tedious tallying, while integrating with existing IP cameras and warehouse infrastructure rather than requiring new hardware. RF-DETR handles the dense, overlapping parcels common in high-throughput environments and scales across multiple camera locations from a single trained model. Detections plug directly into WMS dashboards, throughput analytics, and alerting so teams act on data instead of collecting it.

1. Upload an image: Drop any photo into the widget above to try parcel counting for free, right on this page.
2. Preview instant results: The model detects parcel, cardboard box, envelope, padded mailer, pallet 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.