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Really interesting piece, the bit about tight vs loose bounding boxes got me thinking. Small inaccuracies can add up fast, especially in edge cases or when training on limited data.

Has anyone here found good ways to handle bounding box quality in noisy datasets? Do you rely more on human annotation or clever augmentation?

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Thank you! Better training data is often the key to solving these issues, though it can be a costly solution.

In some cases, running a model like SAM 2 on a loose bounding box can help refine the results. I usually add about 10% padding in each direction to the bounding box, just in case the original was too tight. Then if you don't actually need to mask you just convert it back to a bounding box.

I actually did this in my paper: https://scholar.google.com/scholar?cluster=14980420937479044...

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