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all 14 comments

[–]VapingSwede 11 points12 points  (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 points  (1 child)

They got their 5 years but had to drop the GIS lookup due to time constraints.

[–]gwern[S] 2 points3 points  (0 children)

(Reference: https://xkcd.com/1425/ )

[–][deleted]  (2 children)

[deleted]

    [–]mindbleach 1 point2 points  (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 point  (0 children)

    Animal vs not-animal

    [–]brombaer3000 0 points1 point  (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 points  (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 points  (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 point  (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 points  (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 points  (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

    [–]gwern[S] 2 points3 points  (0 children)

    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 point  (1 child)

    That sounds really promising!

    [–]gwern[S] 0 points1 point  (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.