dronefreak/lisa-rfdetr-nano
Object Detection • Updated • 79 • 2
Datasets:
Tasks:
Object Detection
Languages:
English
Size:
10K - 100K
Tags:
object-detection
traffic-lights
autonomous-driving
self-driving-cars
advanced-driver-assistance-systems
computer-vision
License:
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End of preview. Expand in Data Studio
LISA Traffic Light Dataset (YOLO Box Export)
Unofficial redistribution of the LISA Traffic Light Dataset's "box" annotation style, exported in YOLO format, under the original CC BY-NC-SA 4.0 license.
Disclaimer
This repository is not an official release of the LISA Traffic Light Dataset.
The dataset was created by Morten Bornø Jensen, Mark Philip Philipsen, Andreas Møgelmose, Thomas B. Moeslund, and Mohan M. Trivedi at the Laboratory for Intelligent and Safe Automobiles (LISA), University of California, San Diego. The original authors retain all rights to the dataset. This repository does not claim ownership of any images, videos, or annotations, and takes no credit for the collection or annotation effort.
Three-hop provenance. This redistribution is narrower than a direct mirror of the original release. It is built from the box (traffic-light-housing) YOLO export inside dronefreak/LISA-Traffic-Lights, a companion project by the same author as this one, which itself converted the LISA lab's original CSV annotations into YOLO/COCO training formats with clip-level train/val/test splitting. This repository redistributes only the box-variant YOLO subset of that work, for use with DetectionBench, plus one configuration-file fix. If you want the bulb-annotation variant, the COCO/RF-DETR export, or the untouched original archive, see dronefreak/LISA-Traffic-Lights directly.
If you use this dataset, please respect the license terms below and cite the original papers, not this repository.
Dataset Description
Reliable detection and recognition of traffic lights is a key capability for autonomous vehicles and Advanced Driver Assistance Systems (ADAS). The LISA Traffic Light Dataset was collected to provide a common, public benchmark for this task: continuous training and test video sequences recorded in San Diego, California (Pacific Beach and La Jolla), captured with a Point Grey Bumblebee XB3 stereo camera (only the left view is used) at 1280x960 resolution, under both day and night conditions with varying light and weather.
Two annotation styles exist upstream: BOX (bounding box around the entire traffic-light housing) and BULB (bounding box around only the lit bulb area). This repository redistributes only the BOX variant, already exported to YOLO format.
Changes from the Official Release
This is a narrower re-export of an already-derived work, not a direct mirror of the LISA lab's original release:
1. Original LISA release → dronefreak/LISA-Traffic-Lights (not performed by this repository)
The companion project converted the LISA lab's original per-frame CSV annotations (frameAnnotationsBOX.csv / frameAnnotationsBULB.csv) into Ultralytics YOLO and COCO formats, with explicit clip-level train/val/test splits (not per-frame random splits) to avoid near-duplicate consecutive video frames leaking across splits, and de-duplicated the bundled sample-dayClip6 / sample-nightClip1 example subsets against the full clips already present elsewhere in the data. See that repository for full details of this conversion.
2. dronefreak/LISA-Traffic-Lights (box/YOLO subset) → this repository
- Fixed a broken
data.yaml: the source file'spath:value is both relative and inconsistently cased, which does not resolve on a case-sensitive filesystem once relocated. A correcteddata.yamlwith an explicit, correctly-cased root path is provided here. - Only the
box-variant YOLO export was carried over; thebulbvariant and COCO/RF-DETR export were not (see the companion repository for those). - No images were added, removed, or modified. No labels were changed. No splits were changed.
A count discrepancy worth flagging. The companion repository's own card reports 44,657 / 7,169 / 57,649 boxes for train/val/test in its BOX YOLO export. Counting directly from the label files in the specific box export this repository redistributes, we measure 29,655 / 4,160 / 39,398 boxes for the same splits — noticeably fewer. We have not yet root-caused this difference (possible causes include a stricter box-validity filter, a regeneration between the two exports, or a partial write) and are flagging it here rather than silently using either number as authoritative. Treat the box counts below as accurate for the specific files in this repository, not as a confirmed match to the upstream dataset's true annotation count, until this is resolved.
Dataset Structure
dataset/
├── README.md
├── data.yaml
├── train/
│ ├── images/
│ └── labels/
├── val/
│ ├── images/
│ └── labels/
└── test/
├── images/
└── labels/
where:
images/contains 1280x960 JPEG frames for each split.labels/contains one YOLO-format.txtannotation file per image (class x_center y_center width height, normalized).data.yamlis the Ultralytics dataset configuration file (class names, split paths).- Splits (as measured in this repository): train 17,082 images / 29,655 boxes · val 3,454 images / 4,160 boxes · test 22,481 images / 39,398 boxes (43,017 images total). See the count-discrepancy note above.
Classes (7)
| id | class name |
|---|---|
| 0 | go |
| 1 | goForward |
| 2 | goLeft |
| 3 | stop |
| 4 | stopLeft |
| 5 | warning |
| 6 | warningLeft |
Dataset Sources
Original Papers
Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives Morten Bornø Jensen, Mark Philip Philipsen, Andreas Møgelmose, Thomas B. Moeslund, Mohan M. Trivedi IEEE Transactions on Intelligent Transportation Systems, 17(7), 1800-1815, 2016. DOI: 10.1109/TITS.2015.2509509
Traffic Light Detection: A Learning Algorithm and Evaluations on Challenging Dataset Mark Philip Philipsen, Morten Bornø Jensen, Andreas Møgelmose, Thomas B. Moeslund, Mohan M. Trivedi IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), 2341-2345, 2015. DOI: 10.1109/ITSC.2015.7313470
Official Resources
- Original Kaggle listing: https://www.kaggle.com/datasets/mbornoe/lisa-traffic-light-dataset
- LISA Lab (UC San Diego): https://cvrr.ucsd.edu/
Companion Project (direct source of this export)
- dronefreak/LISA-Traffic-Lights on Hugging Face — the full original archive plus YOLO (box + bulb) and COCO/RF-DETR exports: https://huggingface.co/datasets/dronefreak/LISA-Traffic-Lights
Attribution
All credit for collecting, recording, and annotating this dataset belongs entirely to the original LISA lab authors — Morten Bornø Jensen, Mark Philip Philipsen, Andreas Møgelmose, Thomas B. Moeslund, and Mohan M. Trivedi.
The YOLO/COCO conversion, clip-level splitting, and deduplication this repository builds on was done in the dronefreak/LISA-Traffic-Lights companion project.
This repository only redistributes the box-variant YOLO subset of that work, with one configuration-file fix, for use with DetectionBench. It does not modify, reinterpret, or take credit for the underlying data or annotations.
If you use this dataset in your research, please cite the original publications below.
License
The original LISA Traffic Light Dataset is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, as specified on the official Kaggle listing.
Accordingly:
- Attribution to the original authors is required.
- Commercial use is prohibited.
- Any derivative work (including this YOLO export, and the companion project it is built from) must be distributed under the same license.
This repository is distributed under the same CC BY-NC-SA 4.0 license.
Citation
If you use this dataset, please cite:
@article{jensen2016vision,
title={Vision for looking at traffic lights: Issues, survey, and perspectives},
author={Jensen, Morten Born{\o} and Philipsen, Mark Philip and M{\o}gelmose, Andreas and Moeslund, Thomas Baltzer and Trivedi, Mohan Manubhai},
journal={IEEE Transactions on Intelligent Transportation Systems},
volume={17},
number={7},
pages={1800--1815},
year={2016},
doi={10.1109/TITS.2015.2509509},
publisher={IEEE}
}
@inproceedings{philipsen2015traffic,
title={Traffic light detection: A learning algorithm and evaluations on challenging dataset},
author={Philipsen, Mark Philip and Jensen, Morten Born{\o} and M{\o}gelmose, Andreas and Moeslund, Thomas B and Trivedi, Mohan M},
booktitle={Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on},
pages={2341--2345},
year={2015},
organization={IEEE}
}
Acknowledgements
We sincerely thank the original LISA lab authors for collecting, annotating, and publicly releasing this valuable benchmark, and credit the dronefreak/LISA-Traffic-Lights companion project for the YOLO/COCO conversion work this repository builds on.
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