Fine-tuned YOLOv26x object detector on the LISA Traffic Lights benchmark dataset, trained and evaluated as part of DetectionBench -- a framework for reproducibly benchmarking modern object detectors with identical training recipes and evaluation metrics across multiple real-world datasets.

Detection Showcase

Performance

Metric	Score (%)
mAP@50	28.92
mAP@50-95	14.09
Precision	43.59
Recall	26.96
F1 Score	33.32
Parameters	59.0M
FLOPs	209.5B

Evaluation Protocol

Metrics reported in this model card are computed on the LISA Traffic Lights test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

LISA Traffic Lights Model Zoo

Every model DetectionBench has trained and evaluated on LISA Traffic Lights so far, for full transparency -- see DetectionBench for the smaller, curated comparison set used on the project README.

Model	mAP@50	mAP@50-95	Precision	Recall
RF-DETR Medium	33.01	14.12	69.37	58.28
RF-DETR Small	32.7	15.11	69.7	53.9
YOLOv26m	29.08	13.68	43.79	29.71
YOLOv26x	28.92	14.09	43.59	26.96
RF-DETR Nano	27.47	12.16	74.96	52.57
YOLOv26l	27.28	13.54	41.42	27.99
YOLOv26s	26.91	12.82	42.47	26.43
YOLOv11x	26.4	13.09	53.98	24.38
YOLOv8m	25.07	11.91	38.16	25.15
YOLOv26n	23.74	10.57	37.23	25.7

Per-Class Performance

Class	mAP@50	mAP@50-95
go	68.89	35.09
goForward	0.0	0.0
goLeft	13.75	5.4
stop	62.37	28.96
stopLeft	12.34	6.22
warning	44.55	22.72
warningLeft	0.52	0.27

Evaluation Visualizations

Precision-Recall Curve

F1 Curve

Confusion Matrix

Dataset

This model was trained on LISA Traffic Lights. For the full dataset description, provenance, license, and citation, see the dataset card:

https://huggingface.co/datasets/dronefreak/LISA-Traffic-Lights

Classes

go
goForward
goLeft
stop
stopLeft
warning
warningLeft

Usage

Install Dependencies

pip install ultralytics huggingface_hub

Load Model from Hugging Face

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="dronefreak/lisa-yolo26x",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

python

results = model.predict(
    source="image.jpg",
    conf=0.25
)

results[0].show()

Training Configuration

Setting	Value
Dataset	LISA Traffic Lights
Framework	Ultralytics YOLO
Training Toolkit	DetectionBench
Epochs (configured max)	500
Epochs (actually trained)	124
Early Stopping Patience	100
Batch Size	16
Image Size	640
Optimizer	auto
Initial Learning Rate	0.001
Seed	0

Repository Contents

text

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
confusion_matrix.png
val_batch0_pred.jpg
lisa_yolo26x_showcase.jpg
README.md

Related Resources

LISA Traffic Lights dataset card on Hugging Face
DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets

Training Framework

This model was trained using DetectionBench, an open-source framework for benchmarking object detectors across multiple real-world datasets with a common pipeline.

Features include:

A dataset-adapter registry for converting real-world datasets into a canonical format
Identical training/evaluation recipes across model families (Ultralytics YOLO/RT-DETR, RF-DETR)
Hardware profiling (latency, FPS, VRAM, parameters, FLOPs)
One-command reproducibility via versioned Hydra configs

If you find this model useful, please consider starring the repository.

Known Limitations

Rare/underrepresented arrow classes (goForward, goLeft, stopLeft, warningLeft) have far fewer training examples than the base go/stop/warning classes and correspondingly lower detection accuracy across every model in this zoo.
Sequential dashcam video frames mean visually similar consecutive frames can appear within the same split; performance on genuinely novel scenes may differ from the reported test-split numbers.
Trained and evaluated only on San Diego daytime/nighttime driving sequences (Pacific Beach, La Jolla); generalization to different traffic-light hardware, road layouts, or camera setups is untested.
Small, distant traffic lights are harder to detect reliably, consistent with general small-object detection challenges.

Citation

If you use this model in your research, please consider citing:

The LISA Traffic Lights dataset (see below)
The original YOLOv26x architecture (see below)
The other model architectures shown in the Model Zoo/External Comparison tables above, if you reference their results
DetectionBench, the training/evaluation framework used to produce this checkpoint

@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}
}

bibtex

@article{jocher2026yolo26,
  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
  journal={arXiv preprint arXiv:2606.03748},
  year={2026}
}

Other architectures compared against on LISA Traffic Lights in this model card:

RF-DETR

bibtex

@inproceedings{robinson2026rfdetr,
  title     = {RF-DETR: Real-Time Detection Transformer},
  author    = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei and Ramanan, Deva and Peri, Neehar},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2511.09554}
}

@article{oquab2023dinov2,
  title={DINOv2: Learning Robust Visual Features without Supervision},
  author={Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others},
  journal={arXiv preprint arXiv:2304.07193},
  year={2023}
}

YOLOv11

bibtex

No official YOLO11 research paper has been published by Ultralytics; the most commonly cited independent architectural analysis is used instead:

@article{khanam2024yolov11,
  title={YOLOv11: An Overview of the Key Architectural Enhancements},
  author={Khanam, Rahima and Hussain, Muhammad},
  journal={arXiv preprint arXiv:2410.17725},
  year={2024}
}

YOLOv8

bibtex

No official YOLOv8 research paper has been published by Ultralytics; this is their own recommended software citation instead:

@software{jocher2023yolov8,
  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
  title = {Ultralytics YOLOv8},
  version = {8.0.0},
  year = {2023},
  url = {https://github.com/ultralytics/ultralytics},
  license = {AGPL-3.0}
}

bibtex

@software{Saksena_DetectionBench_2026,
  author = {Saksena, Saumya Kumaar},
  title = {DetectionBench: Reproducible Benchmarks for Modern Object Detectors on Real-World Datasets},
  url = {https://github.com/dronefreak/DetectionBench},
  year = {2026}
}