VISION PROOF LABS @visionprooflabs
Independent evaluation of computer-vision models. We find where your vision AI breaks before your customers do. Manufacturing QC · inspection AI. visionprooflabs.com Working with teams in US/UK Joined October 2026-
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How many defect images was that measured on? Count the defects, not the total images. A 10,000-image test set with 40 defects can't prove 99.9%.
We hold ourselves to it too. In our circuit-board benchmark, one model caught 80/80 defects. We reported it as 95–100%, not 100%. 80 images can't prove more than that.
Model caught 60/60 defects in testing. Is it 100% accurate? No. The real miss rate could still be up to 5%. That's 1 in 20 defects shipped to a customer. The rule every vision AI team should know 🧵 #MachineLearning #ComputerVision
Tested 4 vision models on 160 circuit boards. 2 were basically guessing (AUROC 0.56 and 0.57; 0.5 is a coin flip). The other 2, one a frontier model, both scored 0.95. Same score. Yet one missed 7 defective boards and the other wrongly rejected 39 good ones. How?
If you've run vision AI on a real line: what's the most "accurate" model you've watched fail?
That's the work we do at VisionProof Labs. We test computer-vision models independently, on real images, and show every failure. This week we'll post our first one: 4 models, 160 circuit boards, and some surprising results.
A defect detector that never flags a single part can score 99% accuracy. On a line where 1 in 100 parts is defective, "do nothing" is 99% accurate. That's the problem with how vision AI gets judged. What to ask instead🧵 #computervision
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