---
title: "How to check sewn garments with computer vision"
description: "Lay each finished piece flat on a table under a fixed camera and steady light, then use a model trained on the factory own photos to mark likely skipped or broken stitches, puckered or uneven seams, open seams, loose threads, stains, missing labels or buttons and parts that look out of place against the approved sample. Each mark points to the spot on the piece and the style and size, so a trained checker can confirm it and send the piece for repair or pass it. The model only flags pieces worth a closer look; it does not replace measurement checks or buyer approval, and the checker makes the final call."
url: https://lidvizion.ai/answers/how-to-check-sewn-garments-with-computer-vision/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Direct answers](https://lidvizion.ai/answers/)

# How can a garment factory use computer vision to check finished pieces before packing?

Lay each finished piece flat on a table under a fixed camera and steady light, then use a model trained on the factory own photos to mark likely skipped or broken stitches, puckered or uneven seams, open seams, loose threads, stains, missing labels or buttons and parts that look out of place against the approved sample. Each mark points to the spot on the piece and the style and size, so a trained checker can confirm it and send the piece for repair or pass it. The model only flags pieces worth a closer look; it does not replace measurement checks or buyer approval, and the checker makes the final call.

## What this means in practice

- Set up a flat inspection table with a fixed camera and steady lighting
- Photograph the approved sample for each style so the model has a reference
- Collect photos of pieces that checkers have already passed or sent for repair, for each style
- Show checkers the marked photo and record their pass or repair decision by line and style
- Add reviewed photos back into training whenever a new style, fabric or color comes in

## How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

## More on Automation & custom AI

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- [How can a restaurant group use computer vision to keep plated dishes consistent across shifts and locations?](https://lidvizion.ai/answers/how-to-check-plating-consistency-in-a-restaurant-kitchen-with-computer-vision/)

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