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Thank you! Is the detection accurate enough, or simply the observation conclusions are not that sensitive to minor errors?

That makes me want to revisit my previous idea: boiling soup spillage detector. I once had a google meeting with a cooking soup to keep an eye on it and thought, heck, that seems like a nice exercise for a visual detector finetune

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The detection was accurate enough for me to complete one prototype experiment under controlled conditions- a single tardigrade in an otherwise empty field, and even then, it did lose the tardigrade once or twice. Different lighting conditions, and other things in the field like tardigrade eggs, algae and dirt all make it more challenging.

To make it truly ready for production science, I'd need to put more work into making the model robust. I'd also like better object tracking, so I could track multiple unique tardigrades.

If you want to see even better examples, take a look at DeepLabCut, https://www.mackenziemathislab.org/deeplabcut especially the video examples.

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