akridge/yolo11n-cls-noaa-esd-coral-bleaching

YOLO11n-cls NOAA ESD Coral Bleaching Classifier

Model Overview

This model was trained to classify coral bleaching conditions using the YOLO11n architecture on imagery from NOAA-PIFSC Ecosystem Sciences Division (ESD) Coral Bleaching Classifier dataset. The dataset includes human-annotated points indicating healthy and bleached coral, enabling classification for marine ecosystem monitoring.

• Model Architecture: YOLO11n-cls
• Task: Image Classification
• Classes:
  • CORAL: Healthy coral
  • CORAL_BL: Bleached coral

Model Inference

Ground Truth vs Predictions

Model Weights

• PyTorch Model: Download .pt file
• ONNX Model: Download .onnx file
• NCNN Model: Download
• Torchscript Model: Download .torchscript file

Dataset & Annotations

• Dataset: NOAA ESD Coral Bleaching Classifier Dataset
• Annotation Method:
  • Points annotated by human experts using both randomly generated and targeted sampling methods.

Training Configuration

• Dataset: NOAA ESD Coral Bleaching Classifier Dataset
• Training/Validation Split: 70% training, 15% validation, 15% testing
• Epochs: 100
• Batch Size: 64
• Image Size: 224x224 px
• Optimizer: AdamW
• Augmentations: Minimal augmentations to preserve coral structure integrity

Results and Metrics

The model was evaluated using a withheld test set. The predictions were compared against human-labeled points for validation.

Metric	Value
Top-1 Accuracy	89.8%
Fitness Score	94.9%
Inference Speed	~0.40 ms/image
Preprocessing Speed	~0.17 ms/image
Postprocessing Speed	~0.0003 ms/image

How to Use the Model

from ultralytics import YOLO

# Load the trained model
model = YOLO("yolov11n-cls-noaa-esd-coral-bleaching-classifier.pt")

# Predict on an image
results = model.predict(source="example_coral_image.jpg", imgsz=224)
for result in results:
    predicted_class = result.names[result.probs.top1]
    confidence = result.probs.top1conf.item()
    print(f"Predicted: {predicted_class} (Confidence: {confidence:.2f})")

• more info: https://github.com/MichaelAkridge-NOAA/open-science-ai-toolkit

Intended Use

• Training & Testing new model architectures
• Monitoring coral reef health through automated image classification.
• Scientific research in marine biology and ecosystem science.

Limitations

• The model was trained on the NOAA ESD dataset; it may not generalize to different regions or unrepresented coral species.
• Images with low resolution or poor lighting may lead to incorrect predictions.
• Vertical or flipped images should be processed with appropriate orientation adjustments.

Ethical Considerations

• Predictions should not replace expert human validation in critical conservation decisions.

Metadata / Citation

Citation:
Pacific Islands Fisheries Science Center (2025). Ecosystem Sciences Division (ESD);

Related Metadata:

• Benthic Cover Derived from Analysis of Benthic Images (2019)
• NOAA ESD Coral Bleaching Classifier Annotations Data Dictionary
• Developing a semi-automated CoralNet Bleaching Classifier: annotations and imagery from survey sites across the Hawaiian Archipelago between 2014 and 2019

Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA project content is provided on an ‘as is’ basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.