this post was submitted on 26 Jul 2024
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Unfortunately it doesn't quite work that way. The dataset they are training it on contains images of a single tree, so it's ability to generalise to a normal tree of that species will be incredibly limited.
Consider a facial recognition algorithm trained only on images of Nicolas Cage, then being tasked with identifying members of his family. It would do very well at identifying Nicolas Cage in a crowd, but probably not a good job of identifying anyone else.
Would it help if you photoshopped a bunch of trees with different superficial characteristics but kept the defining traits of the subspecies and trained it on those images?
Maybe, if you could reliably render known traits based on descriptions for which we likely don't have photographic evidence.
You risk tainting the model though. If some artefact of the photoshop gets detected well by the model, then it will quickly learn to identify photoshopped trees, not trees that actually look like the target species.
Ah that makes sense. Kind of like the old AI problem where it thought fish had fingers because most of the training material had people holding up the fish.
Yes you make a good point. Perhaps they trained it on other trees of a similar family?