dronefreak/seadronessee-yolo11n

YOLOv11n Finetuned on SeaDronesSee

Fine-tuned YOLOv11n object detector on the SeaDronesSee 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.

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Detection Showcase

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Performance

Metric	Score (%)
mAP@50	69.93
mAP@50-95	40.41
Precision	82.87
Recall	69.04
F1 Score	75.33
Parameters	2.6M
FLOPs	6.6B

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Evaluation Protocol

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

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SeaDronesSee Model Zoo

Every model DetectionBench has trained and evaluated on SeaDronesSee 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	83.47	47.49	87.01	83.33
YOLOv26m	82.38	49.57	90.01	81.18
RF-DETR Small	80.97	45.31	85.68	80.16
YOLOv26s	80.14	47.35	88.5	77.51
YOLOv11x	74.82	45.56	87.37	72.46
YOLOv8s	72.94	43.05	84.52	71.25
RF-DETR Nano	72.38	39.83	81.37	74.08
YOLOv11n	69.93	40.41	82.87	69.04
YOLOv8n	69.22	40.35	82.46	68.36
YOLOv8m	62.08	34.41	77.3	61.01

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Per-Class Performance

Class	mAP@50	mAP@50-95
swimmer	71.68	27.07
boat	94.77	69.08
jetski	86.88	54.07
life_saving_appliances	32.3	13.45
buoy	64.04	38.39

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Evaluation Visualizations

Precision-Recall Curve

F1 Curve

Confusion Matrix

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Dataset

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

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

Classes

• swimmer
• boat
• jetski
• life_saving_appliances
• buoy

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Usage

Install Dependencies

pip install ultralytics huggingface_hub

Load Model from Hugging Face

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="dronefreak/seadronessee-yolo11n",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

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Training Configuration

Setting	Value
Dataset	SeaDronesSee
Framework	Ultralytics YOLO
Training Toolkit	DetectionBench
Epochs (configured max)	500
Epochs (actually trained)	283
Early Stopping Patience	100
Batch Size	32
Image Size	640
Optimizer	auto
Initial Learning Rate	0.001
Seed	0

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Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
confusion_matrix.png
val_batch0_pred.jpg
seadronessee_yolo11n_showcase.jpg
README.md

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Related Resources

• SeaDronesSee dataset card on Hugging Face
• DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets

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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.

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Known Limitations

• Severe class imbalance: swimmer (64.22%) and boat (22.55%) account for roughly 87% of all annotated boxes in the training set, while life_saving_appliances (1.60%) is rare -- per-class accuracy on the minority classes is measured on comparatively few examples.
• Small-object heavy: objects are captured from altitude over open water, so roughly 79% of boxes cover under 0.1% of the image area -- swimmers and buoys in particular are small, low-contrast targets against water.
• No public test-split labels: the official images/test/ split is a held-out competition set with no released ground truth, so these models are evaluated on the valid split instead of test -- the number reported here is not directly comparable to official SeaDronesSee leaderboard submissions, which score against the held-out test set via the benchmark's own server.
• A maritime search-and-rescue benchmark specifically: generalization to non-maritime aerial scenes, different water/lighting conditions, or altitudes outside this dataset's capture range is untested.

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Citation

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

1. The SeaDronesSee dataset (see below)
2. The original YOLOv11n architecture (see below)
3. The other model architectures shown in the Model Zoo/External Comparison tables above, if you reference their results
4. DetectionBench, the training/evaluation framework used to produce this checkpoint

@inproceedings{varga2022seadronessee,
  title={SeaDronesSee: A maritime benchmark for detecting humans in open water},
  author={Varga, Leon Amadeus and Kiefer, Benjamin and Messmer, Martin and Zell, Andreas},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2260--2270},
  year={2022}
}

@misc{varga2021seadronesseemaritimebenchmarkdetecting,
      title={SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water},
      author={Leon Amadeus Varga and Benjamin Kiefer and Martin Messmer and Andreas Zell},
      year={2021},
      eprint={2105.01922},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2105.01922}
}

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}
}

Other architectures compared against on SeaDronesSee in this model card:

RF-DETR

@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}
}

YOLOv26

@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}
}

YOLOv8

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}
}

@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}
}