YOLOv8m Finetuned on Brackish Underwater

Fine-tuned YOLOv8m 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.

Detection Showcase

Brackish Underwater Detection Demo

Performance

Metric	Score (%)
mAP@50	99.19
mAP@50-95	86.04
Precision	98.69
Recall	98.71
F1 Score	98.7
Parameters	25.9M
FLOPs	78.9B

Evaluation Protocol

Metrics reported in this model card are computed 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.

Model	mAP@50	mAP@50-95	Precision	Recall
YOLOv8s	99.3	85.65	99.36	98.53
YOLOv8m	99.19	86.04	98.69	98.71
YOLOv26s	99.1	85.77	99.45	97.83
YOLOv26n	98.95	83.66	98.17	97.23
YOLOv11x	98.89	86.3	98.84	98.25
YOLOv11n	98.87	83.27	98.76	96.69
YOLOv26m	98.74	85.93	98.74	97.32
YOLOv8n	98.41	83.01	99.51	96.95

Per-Class Performance

Class	mAP@50	mAP@50-95
crab	99.5	95.2
fish	99.49	90.19
jellyfish	97.86	73.83
shrimp	99.5	79.27
small_fish	99.27	78.37
starfish	99.5	99.4

Evaluation Visualizations

Precision-Recall Curve

F1 Curve

Confusion Matrix

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

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-yolov8m",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Training Configuration

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

Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
confusion_matrix.png
val_batch0_pred.jpg
brackish_yolov8m_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 Brackish Underwater dataset (see below)
The original YOLOv8m 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

@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 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}
}

Other architectures compared against on Brackish Underwater in this model card:

YOLOv11

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}
}

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}
}

@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}
}