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YOLOv26l Finetuned on ExDark

Name: exdark-yolo26l
Availability: InStock

Fine-tuned YOLOv26l object detector on the ExDark 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	77.51
mAP@50-95	50.88
Precision	80.71
Recall	70.72
F1 Score	75.39
Parameters	26.3M
FLOPs	93.8B

Evaluation Protocol

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

ExDark Model Zoo

Every model DetectionBench has trained and evaluated on ExDark 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 Small	88.98	61.67	83.07	81.89
RF-DETR Medium	88.64	62.55	86.6	79.46
RF-DETR Nano	85.27	58.01	85.18	74.67
YOLOv26l	77.51	50.88	80.71	70.72
YOLOv26m	76.54	50.02	82.29	68.83
YOLOv8x	75.4	48.39	81.53	65.86
YOLOv8l	75.26	48.48	81.44	67.58
YOLOv8m	74.69	48.05	78.4	69.17
YOLOv11x	74.41	48.98	81.87	67.05
YOLOv9m	74.17	47.38	76.27	67.94
YOLOv26s	74.0	48.32	79.11	65.59
YOLOv11l	73.44	47.56	78.57	67.09
YOLOv11s	73.35	46.8	77.93	66.38
YOLOv11m	73.17	47.16	74.83	67.23
YOLOv8s	73.01	45.85	78.26	65.13
YOLOv26n	72.7	46.27	81.0	62.67
YOLOv8n	71.29	44.78	78.25	62.76
YOLOv11n	70.36	44.72	76.18	61.15

Per-Class Performance

Class	mAP@50	mAP@50-95
Bicycle	79.94	56.11
Boat	82.1	43.85
Bottle	71.27	44.12
Bus	84.9	65.33
Car	81.43	55.8
Cat	83.17	55.61
Chair	66.31	40.7
Cup	73.45	49.15
Dog	85.1	60.44
Motorbike	82.68	52.82
People	78.36	45.48
Table	61.37	41.11

Evaluation Visualizations

Precision-Recall Curve

F1 Curve

Confusion Matrix

Dataset

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

https://huggingface.co/datasets/dronefreak/ExDark

Classes

Bicycle
Boat
Bottle
Bus
Car
Cat
Chair
Cup
Dog
Motorbike
People
Table

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/exdark-yolo26l",
    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	ExDark
Framework	Ultralytics YOLO
Training Toolkit	DetectionBench
Epochs (configured max)	500
Epochs (actually trained)	238
Early Stopping Patience	100
Batch Size	32
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
exdark_yolo26l_showcase.jpg
README.md

Related Resources

ExDark 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

Severe class imbalance: People accounts for roughly 46% of all annotated boxes while Bus is the rarest class, so per-class accuracy on rare classes is measured on very few test examples and should be read with wide uncertainty.
Small dataset overall (7,344 images, 734 in the test split, across 12 classes) -- limited training signal for several classes independent of the imbalance above.
Two-hop provenance: this dataset was converted to YOLO format by a third-party Roboflow export before reaching DetectionBench, not sourced directly from the original per-class-folder release; images are pre-resized to 640x640 by that export.
The original authors separately request non-commercial use of this dataset (beyond the BSD-3-Clause license text itself) -- this applies to any model trained on it, not only the raw images.

Citation

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

The ExDark dataset (see below)
The original YOLOv26l 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{Exdark,
  title = {Getting to Know Low-light Images with The Exclusively Dark Dataset},
  author = {Loh, Yuen Peng and Chan, Chee Seng},
  journal = {Computer Vision and Image Understanding},
  volume = {178},
  pages = {30-42},
  year = {2019},
  doi = {https://doi.org/10.1016/j.cviu.2018.10.010}
}

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 ExDark 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}
}

YOLOv9

bibtex

@article{wang2024yolov9,
  title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information},
  author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2402.13616},
  year={2024}
}

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}
}