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News[N] iNaturalist: a smartphone CNN app for identifying photos of 13k species of wild animals/insects/plants (baynature.org)
submitted 2 years ago by gwern
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[–]VapingSwede 11 points12 points13 points 2 years ago (2 children)
So they got the five years and a development team? Also does it track if you're in a national park or not?
[–]recastrodiaz 2 points3 points4 points 2 years ago (1 child)
They got their 5 years but had to drop the GIS lookup due to time constraints.
[–]gwern[S] 2 points3 points4 points 2 years ago (0 children)
(Reference: https://xkcd.com/1425/ )
[–][deleted] 2 years ago* (2 children)
[deleted]
[–]mindbleach 1 point2 points3 points 2 years ago (0 children)
Based on the name, I sure thought "hot dogs" would be involved.
Oh no wait that's naturists. Mea culpa.
[–]NotAlphaGo 0 points1 point2 points 2 years ago (0 children)
Animal vs not-animal
[–]brombaer3000 0 points1 point2 points 2 years ago (2 children)
Apparently the automatic classification is iOS exclusive for now. I can't find it in the Android app.
I'd be interested to know if the classification CNN runs locally on the phone or as a cloud service (judging from the 32 MB size of the iOS app, probably the latter).
[–]wzdd 2 points3 points4 points 2 years ago (1 child)
Seems like it's cloud. Tried it with the iOS app, and in aeroplane mode, I get "Cannot load suggestions. The Internet connection appears to be offline".
[–]MyBrainIsAI 4 points5 points6 points 2 years ago (0 children)
Shouldnt the app be considered a "CNN client"? Tired of all these apps claiming to do xyz but require net access to do the actual work. It's basically a camera app that does a RPC for results.
[–][deleted] 0 points1 point2 points 2 years ago (5 children)
Not sure if I trust its quality. There's been a couple of apps promising more than they can deliver in this space, and the "natural" thing to do when you've sunk a lot of training time into it and get rotten accuracy is to push it to the app store anyway.
[–]gwern[S] 2 points3 points4 points 2 years ago (4 children)
I'm not a naturalist nor have I field-tested this app, but I submitted it because their methods sound reasonable and the results sounded plausible: they restrict identifiable species to ones with n>20 expert-labeled samples (so at least 260k images), apparently do hierarchical regression from genus down, use known geographical presence as a prior, present top k categories for the user to decide which species if any the photo is of, is an area we already known standard CNNs work really well in (eg dog breeds in ImageNet), the expert users quoted say it works much better than chance in their new wild photos but still regularly makes mistakes as one would expect, and as part of a community website it'll benefit from active learning as people upload & label the samples it gets wrong. So they have adequate sounding data, benefit from structured priors, would be expected to work well, and should get better over time.
[–]fgvc2017 4 points5 points6 points 2 years ago (1 child)
Take a look at the recent iNaturalist Challenge on Kaggle for an idea of performance on a slightly smaller version of the dataset: https://www.kaggle.com/c/inaturalist-challenge-at-fgvc-2017
Even bigger dataset than I thought:
The iNat Challenge 2017 dataset contains 5,089 species, with a combined training and validation set of 675,000 images that have been collected and verified by multiple users from inaturalist.org.
And the current leaderboard has a top-5 error of 0.04875 - that sounds awesome although I suppose it's picking up a lot of accuracy on easily-recognized common species and doesn't do so well for the rarer species that people most need help with identifying?
[–][deleted] 0 points1 point2 points 2 years ago (1 child)
That sounds really promising!
[–]gwern[S] 0 points1 point2 points 2 years ago (0 children)
Yes, I hope they succeed. It may not be earthshaking but it's still a good thing to have which will make a lot of people's lives somewhat easier.
π Rendered by PID 11904 on r2-app-0405508d2807c9378 at 2020-02-18 01:20:40.728819+00:00 running c9ae864 country code: US.
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