Change detail level and representation mode to see what survives when an image becomes numbers. The tool makes recognition thresholds visible — you can watch the moment an image stops being identifiable and ask which cue carried it longest.
Live preview · launch for the interactive version
Before a model can see anything, an image is a grid of numbers. Feature Extraction makes that grid inspectable — you can see exactly what the model is working with at any level of detail.
Reduce detail until the image stops being recognizable. The last surviving cue tells you what the recognition system is relying on — often not what you would expect a human to use.
Color, outline, texture, context, and spatial arrangement each carry different amounts of recognition information. This tool lets you test the hierarchy directly by watching what survives.
Reduce detail one step at a time. Describe what you see at each level — not what you expect to be there.
Write down which cue you think will carry recognition longest: color, outline, texture, or spatial context. Then test your prediction.
Drop one level of detail at a time. At what level does recognition become uncertain? At what level does it fail completely?
Run the same reduction on two different images. Does the same feature type survive longest, or does it depend on the subject?
Name the specific feature that still carries identity after everything else is gone. That feature is what the recognition system is relying on.