From pixels to perception: A benchmark for human-like symmetry detection.

Symmetry, a fundamental concept in nature, science and art, has challenged computer vision researchers because it occurs in various forms and human symmetry perception can deviate from the mathematical definition. Previous symmetry detection datasets are limited by the number of annotators and by missing the nuances of human perception. We introduce PIX2PER, a novel dataset for reflection symmetry in natural scenes and artworks. We also introduce WF1, a modified version of the widely-used F1 detection performance score, by adding weights to precision and recall to accommodate for the perceived symmetry strength. Created by adding weights to precision and recall to accommodate for the perceived symmetry strength. We perform a comparative analysis of existing models for symmetry detection on this human-centric dataset. Additionally, we present a fully synthetic dataset for pretraining symmetry detection models. When finetuning this pretrained model with human data, performance increases significantly. This research introduces and evaluates ways of improving symmetry detection and contributes to the development of computer vision models that more effectively represent human perception.