Part counting: Datasets & Models

Manual part counting is slow, error-prone, and pulls skilled operators away from higher-value work, and miscounts in a manufacturing or QC context lead to short shipments, failed audits, and rework. A detection-based approach counts hundreds of parts per frame in a fraction of a second and produces a consistent, auditable result every time. It also scales to high-throughput lines where humans simply cannot keep pace, and it flags discrepancies against expected counts in real time. That means fewer kitting errors, tighter inventory accuracy, and reliable proof of quantity at each stage of production.

Teams get repeatable, high-speed counts that reduce human error and free labor for inspection tasks that actually require judgment. The workflow runs on your existing overhead or inline cameras and integrates with PLCs, MES, or inventory systems through simple API calls. Because it is detection-based, it scales from a single station to an entire plant and adapts to new part types by adding examples to the training set.

1. Upload an image: Drop any photo into the widget above to try part counting for free, right on this page.
2. Preview instant results: The model detects screw, bolt, nut, washer, bearing, gear 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.