It could be fun to see how much of this is automatable: I have a camera roll that goes back to early 2012 combined with my selections for each year. That’s a decent amount of annotated data!
I don’t think phrasing it as a classification problem is necessarily the best approach. It may be that there are many very similar images and which one out of a cluster you pick is fairly arbitrary, so any attempt at a classifying ’picked/‘not picked’ wouldn’t tell you much, but clustering/sorting still makes it much easier/pleasant to do the curation.
(Speaking of classification and images and blogs, you might find it useful to know that we just launched the InvertOrNot.com API (HN) for dark-mode images.)
If you want to make it more efficient and spend less time fast-forwarding through redundant images, you could experiment with clustering and sorting images in a NN embedding space: https://github.com/MaartenGr/Concept https://every.to/napkin-math/6-new-theories-about-ai https://gwern.net/design#sort-by-magic
It could be fun to see how much of this is automatable: I have a camera roll that goes back to early 2012 combined with my selections for each year. That’s a decent amount of annotated data!
I don’t think phrasing it as a classification problem is necessarily the best approach. It may be that there are many very similar images and which one out of a cluster you pick is fairly arbitrary, so any attempt at a classifying ’picked/‘not picked’ wouldn’t tell you much, but clustering/sorting still makes it much easier/pleasant to do the curation.
(Speaking of classification and images and blogs, you might find it useful to know that we just launched the InvertOrNot.com API (HN) for dark-mode images.)