Platform(s), such as mobile, website, API, other: Computer Vision
URLs (aka web addresses) of any pages, if relevant: Some of the most recent discussion of this conundrum can be found here:
https://forum.inaturalist.org/t/are-genus-level-rg-observations-used-for-cv-training/63859
Description of need:
This applies to any genus (of anything…moths, beetles, flies, spiders) which has many taxa which cannot reasonably be identified (yet) in photos, i.e. they require dissection or other examination which cannot typically be captured in photos. Presently, in such genera, if even one species is identifiable and is included in a CV run, apparently CV is prone to spit out that one ID or something unrelated. It lacks the capability of moving to genus-level ID suggestions when one species is well-documented but many/most are not.
Feature request details:
For selected genera, I would like to see CV trained on RG genus-level observations in order for the genus-level ID to become available as a suggested ID. This would probably require some type of nomination/flag process to identify candidate genera. Candidate genera should meet some criterion of “speciosity but unidentifiability”, i.e. they should be populated by a set of taxa that recognized experts agree cannot typically be identified in photos.
Such a training set for a genus won’t preclude species-level taxa in the same genus which might also being included in a training set (e.g. about 100 images from at least 60 observations).
A discussion is needed to figure out what criteria or algorithm staff might use to select candidate genera.