YOLO26m Finetuned on SeaDronesSee

Fine-tuned YOLO26m 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.

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-yolo26m",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Performance

Evaluated on the SeaDronesSee val split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

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.

Per-Class Performance

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

Training Configuration

Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
BoxP_curve.png
BoxR_curve.png
confusion_matrix.png
confusion_matrix_normalized.png
val_batch0_pred.jpg
seadronessee_yolo26m_showcase.jpg
README.md

Related Resources

• SeaDronesSee 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: 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.

Citation

If you use this model in your research, please consider citing the dataset and the model architecture:

@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}
}

@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}
}