Textile flaw detection: Datasets & Models

Manual fabric inspection is slow, subjective, and hard to sustain across shifts, human inspectors typically catch only a fraction of defects at production speeds, and fatigue makes results inconsistent. In manufacturing and QC operations, a missed hole or stain that ships as first-quality cloth means costly returns and scrapped downstream garments. Automating flaw detection with computer vision gives you consistent, high-speed inspection that flags defects the moment they appear, letting you stop the line, mark the roll, or route material for repair. It also produces a searchable record of every defect and its location for traceability and process improvement.

A trained detection model runs continuously on existing inspection cameras and line-scan hardware, catching defects at production speed without adding inspector headcount. RF-DETR provides strong accuracy on small, subtle flaws against busy fabric textures, and the workflow scales across multiple lines, fabric types, and facilities once the pipeline is in place. Detailed per-defect logging supports yield tracking and root-cause analysis over time.

1. Upload an image: Drop any photo into the widget above to try textile flaw detection for free, right on this page.
2. Preview instant results: The model detects hole, stain, broken thread, snag, misweave 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.