Support for "geo-fencing" approach to improve prediction accuracy for regional species list

[mihow]

Summary

As an alternative to training regional models, we should be able to use the Taxa List feature in a post-processing step to provide much more accurate predictions.

Detailed Description

TBD

Implementation Details

Filter the global model predictions (30k) to a regional list and re-weight the softmax scores

Related Issues

#777

Additional Context

Needed for CFIA moth monitoring project (ports of entry)

[jbowden18]

if there was a user friendly approach to filtering species to create ones own 'regional' pipeline would be great --also species come and go

[mihow]

Notes from call about implementation:

• Receive all raw classifications from the processing backend (e.g the global species classifier)

• All occurrences have a determination, which is a Taxon from the global classifier

• Import the regional list of species that exist for a given deployment

• Run post-processing step in Antenna

• Each "Classification" has a prediction for all 20k species in the global list (every item in the model's category map). Each entry is associated with a Taxon instance in the Antenna DB

• The regional taxa list has a subset of species names that were matched to Taxon entries in the Antenna DB when imported

• Create a new CategoryMap with the subset of labels

• Create a new Algorithm entry (called "World Moths (filtered for Pennsylvania)")

• A,5,0.9; B,2,0.1; C;1,0.1;

• A,5,0.9; C,1,0.2;

• The masking function will remove all items from the labels, scores & logits fields that have the same index - which is mapped to a species which is not in the regional list

• The scores will be recalculated so they add up to 1 (softmax)

• Some occurrences will have a new determination

• "Zeroing" entries instead of deleting them is another approach to consider. (set scores of unwanted species to zero, rather than deleting them) - Needs to be a very low negative number since these are logits (zero could be center).

[mihow]

Usage in our test branch feat/logistic-binning

docker compose run --rm  django python manage.py test_class_masking --project 18 --taxa-list 4 --collection 11 --algorithm 12

• Process some images with global classifier
• Run test command
• Update code to create a new Algorithm & CategoryMap on the fly
• Update code to add a new Classification when the masking is run, rather than replace values in the existing classification

[mihow]

This is now being pursued as #1364 / #1367 (build a project taxa list from a region, and track which species a model can predict), building on class masking (#999) and the post-processing framework (#1289). Leaving open as the original geo-fencing proposal, since #1364/#1367 cover the 'generate the list' half rather than every idea here, but linking so the two threads don't diverge.