Write up on Computer Vision

[bouteloua]

Some non-verifiable observations are included in the training:

@kueda

The vision system is trained on captive / cultivated records. However, our automated suggestions are based on vision results and nearby records, and the nearby records part of it is currently only using RG records, so if vision ranks Canary Island Pine highly, it might get knocked down by the legit lodgepole records in the Transverse Ranges.

Patrick tells me we haven’t run our accuracy tests without the RG requirement, so we’ll do that and see if it makes things better or worse.

Sort of tangential, but it’s also worth noting that our policy of assuming that cultivated plant obs don’t need more IDs means that our cultivated plant records (both images and occurences) probably have less-accurate identifications and are thus less useful for either purpose (probably why we put that RG requirement in there to begin with). I’m aware there are many who would prefer that we remove that part of our quality grade assessment, and it might help situations like this… or it might just make even more identifiers give up in the face of endless potted plants.

@kueda

Observations in the test set for vision and the test set for the whole system are drawn from Research Grade observations and observations that would be RG if they weren’t captive (RG+Captive for short).