  This paper introduces a public dataset of 1.4 million procedurally-generated
bicycle designs represented parametrically, as JSON files, and as rasterized
images. The dataset is created through the use of a rendering engine which
harnesses the BikeCAD software to generate vector graphics from parametric
designs. This rendering engine is discussed in the paper and also released
publicly alongside the dataset. Though this dataset has numerous applications,
a principal motivation is the need to train cross-modal predictive models
between parametric and image-based design representations. For example, we
demonstrate that a predictive model can be trained to accurately estimate
Contrastive Language-Image Pretraining (CLIP) embeddings from a parametric
representation directly. This allows similarity relations to be established
between parametric bicycle designs and text strings or reference images.
Trained predictive models are also made public. The dataset joins the BIKED
dataset family which includes thousands of mixed-representation human-designed
bicycle models and several datasets quantifying design performance. The code
and dataset can be found at:
https://github.com/Lyleregenwetter/BIKED_multimodal/tree/main
