Vision Updates! 2017-12-05

We just updated our computer vision-assisted species suggestions! It's been almost six months since we quietly launched the first version, and despite not making much of a hullabaloo about it, you folks definitely noticed, as did the media. One of our favorite parts about our approach to teaching computers to recognize species in images is that our system is constantly learning from the iNat community. If someone chooses a suggestion that ends up being wrong, that turns into new training data, and if someone observes something the system doesn't know about, that turns into new training data too. In June, we released a system trained on iNat photos added up until May 2017. It could identify 17,246 species, and it had the right species in the top ten results ~78% of the time. The system we just released yesterday trained on data through August 2017, and while it's only slightly more accurate (right answer in the top ten ~81% of the time), it knows about 20,217 species, so that's a 2,971 species improvement!

To give you and idea of what's changed, here are some of the most-observed new species the system can recognize:

We were very happy to see a bunch of species from outside our core areas of the North America and New Zealand in there, and we were particularly impressed with Sphenomorphus indicus, a lizard that has experienced a surge in observations this year thanks for iNat folks in Taiwan. A lot of species have years of observations, but only just passed our threshold of having Research Grade observations by ten different people, but that lizard really just became super popular this year. Go lizard.

We also made a slight change to how we use nearby observation data to add suggestions and sort them: we reduced the radius of the search, so hopefully it will make better "nearby" suggestions. It is not excluding suggestions that have not been observed nearby, but that's certainly something we're considering since so many of you have asked for this.

Anyway, a big thanks to all of you for making all of this possible. We couldn't provide this kind of service without all of your hard work making observations and adding identifications.

Posted on August 20, 2020 10:09 AM by hannahsun99