Fastener counting: Datasets & Models
Automated counting of fasteners, screws, bolts, nuts, and washers, in kits, trays, or assembly stations using object detection. Verifies quantities before packaging or assembly to catch shortages early.
Fastener counting
This detection workflow locates and counts individual fasteners in an image, identifying screws, bolts, nuts, washers, and rivets on trays, kits, or assembly stations. It accepts a still image or camera frame as input and returns per-class counts with bounding boxes, giving you the labeled fastener dataset output needed to verify quantities before packaging or assembly.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate fastener counting
Manual fastener counting is slow, error-prone, and hard to scale during high-volume production, miscounts in a hardware kit lead to line stoppages, customer complaints, and costly rework in downstream assembly. Automating the count with a detection model gives operators an instant, repeatable verification step that flags shortages or overages before parts leave the station. For manufacturing and QC teams, this closes the gap between visual inspection and traceable, logged inventory checks.
Key benefits
Counting runs in real time against existing inspection cameras or line-side hardware, so there's no need to re-tool the station. A model trained on a representative fastener dataset generalizes across part types and bin layouts, and the same pipeline scales from a single QC bench to multiple packaging lines. Detection output integrates cleanly with MES, ERP, or PLC systems for automatic pass/fail routing and audit records.
How to use it
- Upload an image: Drop any photo into the widget above to try fastener counting for free, right on this page.
- Preview instant results: The model detects screw, bolt, nut, washer, rivet 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.
- 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.
- 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.
Recommended model for fastener counting
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for fastener counting, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Fastener Counting project to Roboflow Agent.