iNaturalist strikes out on its own
baynature.org
baynature.org
I use it to automatically tag pictures that I take. I took up bird photography a few years ago and it's become a very serious hobby. I just run my Python script (which wraps their TF model) and it extracts JPG thumbnails from my RAW photos, automatically crops them based on EXIF data (regarding the focus point and the focus distance) and then feeds it into the model. This cropping was critical - I can't just throw the model a downsampled 45 megapixel image straight from the camera, usually the subject is too small in the frame. I store the results in a sqlite database. So now I can quickly pull up all photos of a given species, and even sort them by other EXIF values like focus distance. I pipe the results of arbitrary sqlite queries into my own custom RAW photo viewer and I can quickly browse the photos. (e.g. "Show me all Green Heron photos sorted by focus distance.") The species identification results aren't perfect, but they are very good. And I store the score in sql too, so I can know how confident the model was.
One cool thing was that it revealed that I had photographed a Blackpoll Warbler in 2020 when I was a new and budding birder. I didn't think I had ever seen one. But I saw it listed in the program results, and was able to confirm by revisiting the photo.
I don't know if they've changed anything recently. Judging by some of their code on GitHub, it looked like they were also working on considering location when determining species, but the model I found doesn't seem to do that.
I can't tell you anything about how the model was actually trained, but this information may still be useful in understanding how the app operates.
Of course, I haven't published any of this code because the model isn't my own work.
> The “Seen Nearby” label on the computer vision suggestions indicates that there is a Research Grade observation, or an observation that would be research grade if it wasn't captive, of that taxon that is:
> - within nine 1-degree grid cells in around the observation's coordinates and
> - observed around that time of year (in a three calendar month range, in any year).
https://www.inaturalist.org/pages/help#computer-vision
For how the model was trained, it's fairly well documented on the blog, including different platforms used as well as changes in training techniques. Previously the model was updated twice per year, as it required several months to train. For the past year they've been operating on a transfer learning method, so the model is trained on the images then updated, roughly once each month, to reflect changes in taxa. The v2.0 model was trained on 60,000 taxa and 30 million photos. There are far more taxa on iNaturalist, however there is a threshold of ~100 observations before a new species is included in the model.
https://www.inaturalist.org/blog/83370-a-new-computer-vision...
https://www.inaturalist.org/blog/75633-a-new-computer-vision...
I do this in fish for very different work and there's a good chance the model for your species does not exist yet. For fish we have 6,000 distribution models based on sightings (aquamaps.org) but there are at least 20,000 species. These models have levels of certainty from 'expert had a look and fixed it slightly manually' to 'automatically made based on just three sightings' to 'no model as we don't have great sightings data'. So it may be that the model uses location, just not for the species you have?
Merlin's bird detection system is almost non-functional without location.
https://www.awkwardzombie.com/comic/angle-tangle
It's not likely to work well on actual photos of actual wildlife.
As a birder I have thousands of bird photos and would pay for this service.
Also why not just darktable / digikam?
Apple: 4.8
Google Play: 3.4
The most common issue mentioned by negative Play store reviews is the camera not focusing on the right thing, and needing to try many different angles before something is recognized correctly. This is probably nothing to do with the underlying model, which I guess is the same on both platforms.
And yes, it often takes as much as a minute to identify a species, because you have to keep adjusting zoom and angle and trying to catch every important feature.
That said, once you are used to it, it becomes less noticeable and just feels like part of the game.
And then there's the issue of many plants being slightly mis-identified, many really common flowers being incorrectly identified. I don't really know how people get remotely close to wild animals to identify them, I had no luck with animal life with this and after a while I started mistrusting its results and cross referencing with google lens anyway.
PlantNet often works better for trees.
The training imagery is also really inconsistent in inaturalist and again for plants it's hard to establish context. These are mostly smartphone pictures that non experts have captured in the app. While someone might have verified that a plant is a particular species, because there isn't any foreground/background segmentation, there are often confounding objects in the frame. On top of that you only get a 300px input to classify from. With animals I'd say it's much more common for photographers to isolate the subject. There's also massive class imbalance in inat, a large number of the observations are things like mallards (ie common species in city parks).
I guess the best solution would be to heavily incorporate geographic priors and expected species in the location (which I think is partly done already).
I am guessing. Please tell me if that is correct. How do they prevent false labels ?
A lot of iNaturalist data is open data, see https://github.com/inaturalist/inaturalist-open-data
(It's up to each user to decide how they want to license their observations, photos, and sounds. The options are all-rights-reserved [no license], CC0, CC-BY, and various other CC license.)
[1]: https://www.inaturalist.org/pages/developers
[2]: https://www.gbif.org/dataset/50c9509d-22c7-4a22-a47d-8c48425...
(tldr: uses Google Maps and analytics)
[1] https://apt.izzysoft.de/fdroid/index/info [2] https://gitlab.com/IzzyOnDroid/repo/#what-are-the-requiremen...
I just limp along with aurora, though.
Feels like that'd be useful for all sorts of stuff.
If you have kids I highly recommend pulling out the app during a nature walk.
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https://www.inaturalist.org/observations/155914507
https://www.inaturalist.org/observations/136708118
https://www.inaturalist.org/observations/132443637
https://www.inaturalist.org/observations/169121886
https://www.inaturalist.org/observations/8443142
https://www.inaturalist.org/observations/176100012
Also that cottonwood dagger - whoah! I checked out your lifelist, you've found a lot of really interesting insect species, I'm in Austin and the differences are pretty dramatic and quite interesting. You can see my insect life list here: https://www.inaturalist.org/lifelists/steven_bach?view=tree&...
Almost looks like an image from an electron microscope the way the grass stands up.
I'm sure it's an issue of the image not having enough data, but Apple seems to always try and pin down a species or two whereas Seek seems to only tell me what it knows.
Downsides are that it requires service, and obviously it doesn't work for animals. Seek works perfectly in the middle of the wilderness with zero service, it always feels incredible.
Very positive to see
I have used iNaturalist since 2014, the quality wasn't great initially but with the continued observations adding high quality annotations to the data their training improved pretty quickly, by 2017-18 it was already really good.
Dang. I was just hoping maybe I could identify the beetles that come out and crawl around my floor around dusk.
I've been trying Seek on them, and when it identifies a species it says "Strawberry Seed Beetle", but that seems unlikely unless strawberry seed beetles are common household pests.
There's ~64.000 mammal species total, insects clock in at ~1,000,000. Of insects Coleoptera, the beetles, are estimated at 360-400k species. There's about 2,000,000 species in Animalia total and a bit less than a quarter are beetles.
If you use iNaturalist it's more helpful on that count than Seek since some beetles are more identifiable than others, and there are entomologists who might be able to help confirm some ids, I've had luck with a number of ground beetles and some other species.
The harpalus identification seems pretty safe. Identifying the species from a photo seems like a hopeless task, so further identification would presumably have to rely on background knowledge like "there's only one common harpalus species in your area".
The strawberry seed beetle, as far as I can tell, is in fact nearly visually identical, so in some sense Seek made a respectable guess.
I’ve had less success with trees but it’s still pretty good.
The focus on images is a bit iffy. Usually I take the photo using the phones native camera app to overcome.
Or more generally, whats the generation of a species? It can’t be just whether they can produce offspring together because some different species has been shown to produce viable offspring.
So there are lots of different models for different types of organisms. And some reproduce asexually so reproductive compatibility would never be enough.
Two simple options are evolutionary lineage (effectively genetic distance) or physical characteristics (morphology): https://bio.libretexts.org/Courses/University_of_California_...
Great service, and great app.