Few-Shot Defect Detection AI

Catch defects you have barely seen: train anomaly detection on images of good parts only, and grow a defect model from a handful of examples.

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AI system highlights a dent on a silver industrial component with a purple overlay and magnified view.

Few-Shot Defect Detection AI for the Toughest Parts, Lines, and Rare Defects

Deploy Anywhere, Run Everywhere
Run anomaly and defect models on line cameras, AI1, and edge devices, on-prem, in your VPC, or via API, with Roboflow Inference serving every model.
One Platform, Full Adoption
Tools every plant team can adopt, from quality and manufacturing engineers to line supervisors, starting from images of good parts, no separate ML team required.
Secure, Compliant, and Audit-Ready
Data stays safe with SOC 2 Type II compliance, encrypted data, HIPAA compliance, and an uptime SLA, with an anomaly score and heatmap behind every rejected part.

“The vision AI solutions we developed with Roboflow have been a core component of connecting our teams and unlocking their ability to collaborate.”

Lou Stocco

Director of Manufacturing Advanced Analytics, USG

Read the full story See customer stories

Talk to a Vision AI engineer who's shipped defect detection with almost no defect data.

Bring us the defect you have only seen a few times and we'll map a working solution.

Ask us about:

Solution architecture that fits your parts, lighting, cameras, and line
A live demo on your own good and defective part images
Deployment options: edge, on-prem, air-gapped, or VPC
ROI modeling against escapes, false rejects, and manual inspection hours

Over 16,000 organizations build with Roboflow.

Rivian
Pella
Chobani
USG Corporation
BNSF Railway
American Woodmark

Start where you are

Vision AI is transforming businesses

Customers across the board are solving complex challenges and driving meaningful impact.

Automotive customer
$10 million
Saved by automatically detecting defects on the production line
Logistics & freight company
90%
Less time spent manually tracking shipping inventory
Building materials supplier
60%
Lower customer return rate with improved product quality

Catch Every Defect, from the First Good Part to Full Production

Add a reliable inspection layer before you have a defect library with Roboflow few-shot defect detection. Built for manufacturers launching new parts and lines, where good parts are plentiful, real defects are rare, and nobody can wait months to collect enough bad examples to train a traditional model.

Learn from good parts:

Train anomaly detection with PatchCore or FoundAD (opens in a new tab), both fitted on images of your normal product only, so you can start inspecting before you have collected a single defect.
Get an anomaly score, a threshold, and a normal or anomalous verdict for every part, plus an optional heatmap that shows exactly where the part looks wrong.
Flag defect types nobody has seen yet, like a new scratch pattern or a foreign object, the core of anomaly detection.

Start from a handful of defects:

Label faster with Box Prompting, which suggests matching boxes from a single example, and Auto Label with SAM 3, which labels defects from text prompts.
Grow the model with rare-defect techniques: tile high-resolution images so hairline defects stay visible, add 10-20% synthetic defects to fill gaps, and include hard negatives, then fine-tune RF-DETR.

Improve on the line:

Route uncertain parts to a reviewer with active learning, and turn every confirmed defect into new training data.
Keep rare classes from getting drowned out with the class imbalance fixes we tested on a 50:1 defect dataset.
Send pass and reject signals to your PLC over OPC UA, Modbus TCP, or EtherNet/IP, so a flagged part is pulled before it ships.

Bring intelligence to every new line today. Start inspecting from your first good parts.

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Frequently asked questions

What is few-shot defect detection?

Few-shot defect detection finds defects with very little defect data, either by learning what good parts look like and flagging anything different, or by training a defect model from a handful of labeled examples.

Can I start without any defect images?

Yes. PatchCore and FoundAD anomaly detection models are fitted on images of your normal product only, and flag parts that differ from them with an anomaly score, a verdict, and an optional heatmap.

Should I use anomaly detection or a defect model?

Use anomaly detection to catch anything unusual, including defects you have never seen. Use a defect model when you need to name the defect type, like scratch, dent, or missing feature. Many lines run both, with anomaly detection catching the unknowns and the defect model classifying the known ones.

Can it integrate with our line and PLC?

Yes. Roboflow sends reject signals to PLCs over OPC UA, Modbus TCP, and EtherNet/IP, and sends scores, heatmaps, and images to MES, QMS, and traceability systems.

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Few-Shot Defect Detection AI