AI scoring and image analysis, as a subject in its own right
AI penis analysis, explained properly
What image models can and cannot tell you about anatomy, how the scoring actually works, and where the numbers stop meaning anything.
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Perspective distortion reaches the model unchanged
A wide lens close to a subject exaggerates whatever is nearest, and the model has no way to know the lens, so it scores the distortion.
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A short document that answers most privacy questions
A model card names training data sources, evaluation and intended use; its absence from a rating tool is informative in itself.
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Inter-annotator agreement is the accuracy ceiling
When the humans who labelled the data agreed only loosely, no model trained on that data can be more consistent than the agreement allowed.
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Two ways to build a rubric scorer, and how they fail differently
A shared backbone with several outputs is cheaper and leaks between axes; separate models are independent and expensive.
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Apparent symmetry is a property of the viewpoint
A model scoring symmetry sees the projected outline, and a small change in angle changes that outline more than the subject ever could.
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Anonymisation of images is harder than of tables
A service that says it trains on anonymised images is making a claim that is difficult to make true for photographs, and worth reading closely.
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Face detection as a policy step
A face detector running before scoring is a privacy and policy choice, and it can misfire on things that are not faces.