TITLE: BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks 

ABSTRACT: In this paper, we present "BIKED," a dataset comprised of 4500 individually
designed bicycle models sourced from hundreds of designers. We expect BIKED to
enable a variety of data-driven design applications for bicycles and support
the development of data-driven design methods. The dataset is comprised of a
variety of design information including assembly images, component images,
numerical design parameters, and class labels. In this paper, we first discuss
the processing of the dataset, then highlight some prominent research questions
that BIKED can help address. Of these questions, we further explore the
following in detail: 1) Are there prominent gaps in the current bicycle market
and design space? We explore the design space using unsupervised dimensionality
reduction methods. 2) How does one identify the class of a bicycle and what
factors play a key role in defining it? We address the bicycle classification
task by training a multitude of classifiers using different forms of design
data and identifying parameters of particular significance through
permutation-based interpretability analysis. 3) How does one synthesize new
bicycles using different representation methods? We consider numerous machine
learning methods to generate new bicycle models as well as interpolate between
and extrapolate from existing models using Variational Autoencoders. The
dataset and code are available at http://decode.mit.edu/projects/biked/.
In this paper, we present "BIKED," a dataset comprised of 4500 individually
designed bicycle models sourced from hundreds of designers. We expect BIKED to
enable a variety of data-driven design applications for bicycles and support
the development of data-driven design methods. The dataset is comprised of a
variety of design information including assembly images, component images,
numerical design parameters, and class labels. In this paper, we first discuss
the processing of the dataset, then highlight some prominent research questions
that BIKED can help address. Of these questions, we further explore the
following in detail: 1) Are there prominent gaps in the current bicycle market
and design space? We explore the design space using unsupervised dimensionality
reduction methods. 2) How does one identify the class of a bicycle and what
factors play a key role in defining it? We address the bicycle classification
task by training a multitude of classifiers using different forms of design
data and identifying parameters of particular significance through
permutation-based interpretability analysis. 3) How does one synthesize new
bicycles using different representation methods? We consider numerous machine
learning methods to generate new bicycle models as well as interpolate between
and extrapolate from existing models using Variational Autoencoders. The
dataset and code are available at http://decode.mit.edu/projects/biked/.
BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks
http://arxiv.org/abs/2103.05844v3