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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Self-hosting removes the upload entirely
Open-weight vision models make it possible to score locally, which trades convenience and possibly quality for the removal of every server-side question.
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A screenshot is a new file with its own metadata
Screenshotting a photo drops the original EXIF and writes fresh fields about your device and screen, which is less than before but not nothing.
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What it means for a scorer to agree with itself
Reliability is the model agreeing with itself on repeated inputs, and it is a lower bar than accuracy that many tools still do not clear.
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Identifiability does not require a face
A photo can identify a person through marks, setting, metadata or linkage to an account, so 'no face' does not settle the question.
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The phrases that should stop you
Some clauses are boilerplate; a few, in a tool that handles intimate images, are decisive, and they are easy to list.
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The subject is not constant either
Temperature, time of day and state change appearance enough that a model scoring 'the same subject' is often scoring a different one.
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Correlated axes inflate whatever they share
If two rubric axes move together in the training data, the model learns them as one thing and the total counts it twice.