Computer vision

I set out to train a small local model to reproduce Apple's photo-quality score so Loupe could rank photos for anyone. It matched Apple almost perfectly. Then a cheap probe showed what the score actually measures, and it was not beauty.

Loupe can now search photos by describing them, with every model running on my own hardware. The interesting work was not the model. It was proving that the index machine and the query machine compute exactly the same numbers, and finding out the plan's premise was false.

For an app that looks at your private photos, the machine learning cannot phone home, so every model in Loupe runs on your own machine, on a single consumer graphics card.

Culling a hundred-thousand-photo library should never come down to one mysterious score, so I split the decision into three plain roles that each answer a different question.

The marketing demo stopped being a screenshot tour and became the actual Loupe app running over a hand-authored fixture, every view wired in owner mode, real detected face crops, a library full of believable junk frames, and a production deploy.