YOLO11x Finetuned on Brackish Underwater

Fine-tuned YOLO11x object detector on the Brackish Underwater 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/brackish-yolo11x",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Performance

Evaluated on the Brackish Underwater test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

Brackish Underwater Model Zoo

Every model DetectionBench has trained and evaluated on Brackish Underwater 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 Brackish Underwater. For the full dataset description, provenance, license, and citation, see the dataset card:

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

Classes

• crab
• fish
• jellyfish
• shrimp
• small_fish
• starfish

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
brackish_yolo11x_showcase.jpg
README.md

Related Resources

• Brackish Underwater 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: crab (34.6%) and small_fish (30.4%) account for roughly two-thirds of all annotated boxes in the training set, while shrimp (1.46%) and jellyfish (1.82%) are rare -- per-class accuracy on the minority classes is measured on comparatively few examples.
• Roughly 15% of images have no annotated objects at all (background-only frames, by design -- part of the dataset's varying-visibility setup, not a data-quality issue).
• Single-location, single-camera capture: all footage comes from one fixed camera 9 meters below the surface on the Limfjords bridge, Denmark -- generalization to other underwater cameras, locations, or water types (brackish vs. marine/freshwater) is untested.
• Two-hop provenance: this dataset was converted to YOLO format via a third-party Roboflow export, not sourced directly from the original annotated-video release; images are pre-resized to 1920x1080 by that export.

Citation

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

@InProceedings{pedersen2019brackish,
  title = {Detection of Marine Animals in a New Underwater Dataset with Varying Visibility},
  author = {Pedersen, Malte and Haurum, Joakim Bruslund and Gade, Rikke and Moeslund, Thomas B. and Madsen, Niels},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2019}
}

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}
}