An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers
- Published
- Source
- arXiv
- Paper number
- 313
- Field
- Computer Vision
- arXiv ID
- 2606.05149
Key points
- Evaluated on 3,805 annotated overtaking events collected from bike lanes in Ann Arbor, Michigan, the pipeline achieved 0.94 accuracy, and class-wise F1 scores ranged from 0.91 for minivans to 0.97 for SUVs.
- Three of the four well-represented categories retained F1 scores above 0.90 even under domain shift.
- The complete pipeline, including the inference script, training code, evaluation utilities, and model weights, is released as open-source software to support reproducibility and reuse across roadside video archives and bicycle safety research.
Paper links
External research summaries. These are not HDATF publications or measured product results.