This model is a trained model for scoring creativity - specifically figural (drawing-based) originality scoring. It is a fine-tuned version of
beit-large-patch16-224.
It achieves the following results on the evaluation set:
Acar, S.^, Organisciak, P.^, & Dumas, D. (2023). Automated Scoring of Figural Tests of Creativity with Computer Vision.
http://dx.doi.org/10.13140/RG.2.2.26865.25444This model judges the originality of figural drawings. There are some limitations.
First, there is a confound with elaboration - drawing more leads - partially - to higher originality.
Secondly, the training is specific to one test, and mileage may vary on other images.
This is trained on the Multi-Trial Creative Ideation task (MTCI;
Barbot 2018), with the
data from Patterson et al. (
2023).
The train/test splits aligned with the ones from Patterson et al. 2023.
