Just wondering, does iNaturalist currently log what the computer-vision AI thinks the image is, at the moment of uploading, before the user / community weighs in with our human-vision IDs? If so, can we look at these data? I think there’s some cools stories here. If not, please let me know, and I’ll put this in a feature request thread.
Thank you! :)
i don’t think this already exists as its own feature request. however, there’s quite a bit of discussion about this as an alternative suggestion in a feature request thread that proposes something similar: https://forum.inaturalist.org/t/automatic-inat-suggestion-for-unknown-observations-that-reach-a-certain-age/4242/18.
I was also wondering about the results, achievements, improvements of the differnt models as tere is a lot of rumour of using this ai models at observation.org.
For the testrun they have a distribuiton set
But i do agree there are cool stories waiting but i think the best results and reports are by comparing models with using the same training data
I have no idea where this image is from
The actual image i wanted to post i can not find, probably it is from another source (Naturalis, not iNaturalist)
Thanks for the insight, both of you!
@pisum, given the existing discussion in that feature request thread you mentioned, do you think I should still make another feature request? Or would that be redundant?
honestly? i wouldn’t bother because it’s unlikely to be done, i think.
but there’s no harm in writing up a feature request. worst case, the mods will say that the existing feature request covers things already.
Gotcha, thank you!! I will do just that.
I would like to know the difference/improvement in the different models for the different ordes (plants, fungi, birds). Somewhere there was a nice example for it but i can not find it. How do you know that a new model is better than the old one ?
Maybe in one of iNat’s blog posts?
Here is a standard blog for a new CV release.
If -overall- a new computer vision model performs more bad then the old model, I should expect that the old model would not be replaced… So a new model in PRD should always be better?
Well the actual graph i was probably from AINature.eu and showed the performance of each species against the number of available photos and the number of observations.
I showed there was a relation between the accuratenes/prediction of the model and the number of the photos.
I thought it was from a powerpoint for Belgium
but I am not sure if this means that a new model in a new year means a better top-1 accuracy.
I always try to add my input before the CV can make a guess.
What is SOTA?
“…Our main goal has been to keep accuracy about the same as the model gets bigger more complex with more species. This is a few models behind, but the trend has continued…”
nfortunately there’s no tag, but there is a link buried in each post linking back to the previous post. Here’s all of them going back to March 2020:
Probably a jargon acronym for “state of the art”. So, “…dynamic MLP consistently achieves SOTA results” is a way of saying “we didn’t win but we didn’t lose either”.
That is also what I think, but my interpretation of SOTA there is “we’re doing just as good as or better than other models are doing” even though the IDs aren’t perfect, but YMMV.