Computer Vision should tell us how sure it is of its suggestions

Hopefully without straying too much off topic, what about the ability to add an (optional) bounding box to photos? In theory, this could be used to restrict AI training to a certain part of the image, and could be visible to help identifiers work out what to identify.

I’ve often used MS Paint to circle an organism in an image - being able to do this natively would be useful.




And remember placeholder text disappears when an ID is added. Better to leave the text in a comment.


One simple and unobtrusive way you could make the confidence data available would be to add it as a title attribute to the “Visually similar” span in the suggestion interface. That way you could see the confidence score by hovering over the text. It would be a subtle addition, but at least it would make it available for folks that were interested.


It’s the words “pretty sure” that adds false confidence to the Algorithm ID.

I particularly like this “fig”



Some relevant comments from Ken-ichi:


even if we don’t call it confidence or probability, i think it would still be useful to see the “score”.


Presumably one could make a relative scale, where the first choice would be one (or zero), and the following ones would be fractions of one (or negative numbers). This could illustrate the differences between the 10 options. Does one stand out, or are they basically all equally likely?

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I would be concerned that many users, especially new users, would misinterpret any displayed numeric value as some level of relative confidence. I concur with @kueda that some would overly rely on the *cryptic opinion of this black box" and not their own judgement. Student users would be one group of new users who might overly rely on displayed values.


Just to argue with myself, maybe including such numbers would allow people to make better choices among the black box outputs. Counter counter argument: the problem is over-confidence in a numerical rating when the right answer isn’t even on the list. Counter counter counter argument: that’s already a problem. Counter counter counter counter argument: we’re all doomed and should be spending our time learning post-apocalyptic survival skills.


On it:


i think the scores are basically a measure of visual match and possibly some other factors like presence of observations of a taxon nearby. i think that’s why iNat staff don’t want people thinking of it as a probability or confidence.

so, for example, suppose you have 3 brothers A, B, and C. A & B are identical twins. suppose you take a picture of A and run it through a computer vision algorithm similar to iNaturalist’s. i would expect that CV to return scores that might be like this:
F: 0.97 – the family of A, B, & C
A: 0.95
B: 0.93
C: 0.65
D: 0.35 – D is the boy who lives next door.

so obviously, the CV couldn’t be both 95% sure the photo was of A and also 93% sure that it was of B, nor would it make sense to assign 95% probability of A and at the same time assign 93% probability of B, but by seeing the relative scores, you could see that the CV was saying that A and B were way better potential matches than C or D.


32 posts were split to a new topic: Chrome extension showing Computer Vision confidence

I like what @psium suggested. Perhaps it can be a % similarity [to other photos of which are RG]

98% similar to [photos of] genus A
92% similar to [photos of] species X
91% similar to [photos of] species Y
75% similar to [photos of] species z

With an option to view the top ~10 ranking IDs in the Identotron on a separate tab, this will also show where the nearest observations have been to try and eliminate Australian species suggestions for African observations.


I agree, people would tend to read the higher “match” percentage the wrong way and jump on the identification without really looking closely at it.

This is already a bit of an issue (sometimes I even have to stop myself from doing it).

I think it’s best if the AI suggestions are left general. I like the “pretty sure” wording as well, that reminds people that it’s not a certain ID.


If you need some book references, hit me up…


I’m new to this community, so I won’t weight much here, but I think it might be a bad idea.
We have some Formicidae screenshot, they are a good example of why this is a bad idea.

Currently the CV is very bad at suggesting and identifying ants. I don’t recall the algorithm ever suggesting the right species as its favorite choice. I even had “we are pretty sure it is a [genus]” on an insect from a totally different order. Also almost every black ant are suggested as “Camponotus” Because they are all black, 6 leg 2 antennae 1 head one thorax one “wasp waist” and a big gaster. Most don’t have striking pattern like butterflies or birds can have for example.

Such a feature would give a false sentiment of confidence, as stated by other members. But it is already the case with “We are pretty sure it is”.

But worse, a red highlight would give the impression that it is unlikely the correct ID is actually among these “uncertain” suggestions.

Simply put : it’s a confusing system, especially for beginners.


yes. i think that’s exactly why showing the underlying scores would be enlightening – because the #1 choice in a list of bad choices is not the same as the #1 choice in a list of good choices. so if you can see the actual scores, you have better insight into whether you’re being presented good choices or if you’re being presented bad choices.

just for example, here are 3 of my own ant observations, along with the actual computer vision scores:

  • (needs ID as Myrmicine Ants, possibly Bicolored Pennant Ant):

    we’re pretty sure it’s in this family:

    • (99.9) Ants (Formicidae)

    our top suggestions (combined score, vision score):

    • (40.2, 53.4) Dolicohoderus genus
    • (10.2, 4.5) Bicolored Pennant Ant (Tetramorium bicarinatum)
    • (5.6, 7.4) Acorn Ants (Temnothorax genus)
    • (4.6, 6.1) African Big-headed Ant (Pheidole megacephala)
    • (4.4, 1.9) Graceful Twig Ant (Pseudomymex gracilis)
    • (4.2, 1.8) Florida Carpenter Ant (Camponotus floridanus)
    • (3.2, 4.3) Arboreal Bicolored Slender Ant (Tetraponera rufonigra)
    • (3.2, 1.4) Crematogaster laeviuscula
  • (research grade Red Imported Fire Ant / Solenopsis invicta)

    we’re pretty sure it’s in this family:

    • (100) Ants (Formicidae)

    our top suggestions (combined score, vision score):

    • (50.0, 30.5) Forelius genus
    • (8.3, 15.4) Asian Weaver Ant (Oecophylla smaragdina)
    • (6.0, 11.1) Mediterranean Acrobat Ant (Crematogaster scutellaris)
    • (5.7, 3.5) Argentine Ant (Linepithema humile)
    • (5.5, 10.1) Azteca genus
    • (3.5, 2.1) Crematogaster laeviuscula
    • (2.6, 4.9) Tropical Fire Ant (Solenopsis geminata)
    • (2.6, 4.8) Yellow Crazy Ant (Anopolepis gracilipes)
  • (research grade as Eastern Black Carpenter Ant / Componotus pennsylvanicus):

    we’re pretty sure it’s in this genus:

    • (77.9) Carpenter Ants (Camponotus)

    our top suggestions (combined score, vision score):

    • (64.3, 61.3) Shimmering Golden Sugar Ant (C. sericeiventris)
    • (7.0, 1.8) Eastern Black Carpenter Ant (C. pennsylvanicus)
    • (6.9, 7.3) Giant Turtle Ant (Cephalotes atratus)
    • (4.0, 4.3) Eciton genus
    • (2.6, 2.8) Bullet Ant (Paraponera clavata)
    • (2.0, 2.2) Diacamma genus
    • (1.9, 2.0) Giant Forest Ant (Dinomyrmex gigas)
    • (1.8,1.9) Hairy Panther Ant (Neoponera villosa)

if using sessilefielder’s red-to-green gradient, remember that:

so most of the “top suggestions” above would be red to yellow, whereas the “we’re pretty sure” suggestion would be more green. hopefully in such cases, that would push most folks to select the green rather than the yellow or orange, if they were simply choosing blindly based on the system’s suggestions.

i also think if people could see that, say, bird suggestions tend to be very green, while, say, spider suggestions tend to be very red, then they would also be much more careful about relying on the computer vision for spiders.

or if they see two equally green birds suggestions, they might pause for a moment to consider why both are equally green before just blindly selecting the first choice.

of course, computer vision suggestions will never be perfect. there will always be mistakes, but i think showing the computer vision scores will help reduce (rather than increase) the likelihood that the community will adopt those mistakes.


I would have assumed that the scores are the classification scores in the final “softmax” layer of the neural network. In that case, every taxon would be given a score from 0.0 to 1.0, and all scores would sum to 1.0, like probabilities.

i think that’s hard in this case because of the hierarchical nature of the taxa. suppose your had an observation of a blue jay, and the existing algorithm assigned a score of .90 for blue jay, .95 for bird, and 1.0 for animal, what kinds of scores would your assumed implementation assign those taxa?

EDIT: nevermind – alex’s response below changes the way i have to look at things…