Volunteers needed for new turtle detector experiments

Hello IoT users!

We’ve retrained detection (bounding boxes and their rotation) and viewpoint labeling and need your help with the next steps.

There’s two successes/failures we need to test:

  • Are bounding boxes generally better, including new and old perspective
  • If yes to the above, are viewpoint labels as-good-as or better than before?

There are some trade-offs between the top three candidates for our labeler (such as certain viewpoints getting labeled correctly more often than others) and we’d like to hear from our turtle researchers if you want to participate in these experiments to better evaluate its performance, especially if you’re working with less common viewpoints.

Let us know if you’re interested in helping with the the detection experiments by posting here!

I’d be happy to help

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Thanks @GoBeach! I’ll be in touch with next steps soon.

Sounds good - I can delete and re-upload some of my most recent bulk uploads - we haven’t processed some of them yet.

I’ll let you know as soon as I get more info from the devs!

Happy to help if further tests are needed for individual uploads - ORP team has also noticed several issues with IA Class and orientation assignments for automatic annotations.

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Thank you both! We hope to have this set up for you to start testing before the end of this week.

Hi - Checking in on this. I’ve got a couple of bulk imports that need to be re-submitted but will wait until you’re ready for us.

Hi @GoBeach

@ORP-StephanieK asked us to hold off on starting the detection experiments until the bug that prevented users from editing annotations was fixed. We rolled that out yesterday evening, so we’ll pick up that discussion again this week. Thanks for your patience.

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I hoped that the detector would have gotten set up by this weekend, but I’ll need to follow up on it next week. Here’s what I know so far:

The test model will use the old viewpoint labeler with the new detector. In order to test it, you’ll use the taxonomy Chelonia fictus. This allows you to compare the old detector performance (using the correct taxonomy for your turtle) alongside the new one (using the fictional taxonomy).

We’re looking for feedback on annotation accuracy, if the correct viewpoint labels are applied (left, right, up, down, etc.), and matchability.

Tagging @jason in case you happen to set this up before I’m back online.

OK, the test detector is ready! The species will be listed as Chelonia fauxdas:

It uses the new YOLO11 model for annotations and rotation but the old viewpoint labeler.

OK, I just submitted a large import (109 photos) with Chelonia fauxdas and this import has already been through the standard detector (without any validation by our team.

File: ‘Test 20250719 KPlummer Bulk Import’

I’ll hold at this one file, let me know if you need more

It looks like these were imported as Chelonia mydas. Can you double-check your test spreadsheet?

sorry - forgot to save the spreadsheet before uploading - it’s processing now

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Awesome! Just spot checking some encounters on the first page of the new upload compared to the original one, I can already see fewer missed detections. All the encounters I’ve checked so far all have annotations.

This one should be down, or down back. Isn’t ‘up’ used for different species?

Yes! the annotations look much better and are more accurate - although I had to snicker at the duck that got annotated in one of the encounters :rofl:

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Double checking with the ML side of the team now, but this does seem consistent with other Wildbooks where a top down view of the head is labeled as “up”.

We’ve been using ‘down’ or ‘down back’ for this type of view