For trustworthy and human-aware artificial intelligence, models should be evaluated beyond accuracy, among others through error predictability and semantic alignment. Similarity is central to these aspects, as it influences which classes a model considers related and confusable. Similarity manifests in multiple forms, including semantic similarity, which can serve as a proxy for human similarity perception. While similarity perception is often imposed in computer vision, little attention has been paid to its natural emergence during supervised training. Existing studies are largely limited to static and qualitative analyzes and lack a systematic, training-time perspective. Therefore, we analyze how similarity perception evolves and aligns with model error patterns and semantics in supervised vision networks. As an enabler, we introduce Deep Similarity Inspector (DSI) - a systematic, training-time framework that unifies complementary views on similarity within a single methodology. Using DSI, we analyzed Convolutional and Transformer-based Networks and showed that both architectures develop rich similarity structures through three phases - initial similarity surge, refinement, stabilization - while exhibiting clear differences. We also identified the mistake refinement phenomenon, in which networks improve mistakes with time.

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