BIKED: A Dataset for Computational Bicycle Design with Machine Learning\n Benchmarks
Bibliographic record
Abstract
In this paper, we present "BIKED," a dataset comprised of 4500 individually\ndesigned bicycle models sourced from hundreds of designers. We expect BIKED to\nenable a variety of data-driven design applications for bicycles and support\nthe development of data-driven design methods. The dataset is comprised of a\nvariety of design information including assembly images, component images,\nnumerical design parameters, and class labels. In this paper, we first discuss\nthe processing of the dataset, then highlight some prominent research questions\nthat BIKED can help address. Of these questions, we further explore the\nfollowing in detail: 1) Are there prominent gaps in the current bicycle market\nand design space? We explore the design space using unsupervised dimensionality\nreduction methods. 2) How does one identify the class of a bicycle and what\nfactors play a key role in defining it? We address the bicycle classification\ntask by training a multitude of classifiers using different forms of design\ndata and identifying parameters of particular significance through\npermutation-based interpretability analysis. 3) How does one synthesize new\nbicycles using different representation methods? We consider numerous machine\nlearning methods to generate new bicycle models as well as interpolate between\nand extrapolate from existing models using Variational Autoencoders. The\ndataset and code are available at http://decode.mit.edu/projects/biked/.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".