---
language:
- en
base_model:
- Ultralytics/YOLO11
tags:
- yolo
- yolo11
- yolo11n
- coral-bleaching
- image-classification
- NOAA
- marine-ecosystem
pipeline_tag: image-classification
library_name: ultralytics
license: agpl-3.0
datasets:
- akridge/NOAA-ESD-CORAL-Bleaching-Dataset
---
# 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
![results](./03_predictions_vs_ground_truth.png)

## Ground Truth vs Predictions
![results](./01_predictions_vs_ground_truth.png)

## Model Weights
- **PyTorch Model**: [Download `.pt` file](./yolov11n-cls-noaa-esd-coral-bleaching-classifier.pt)  
- **ONNX Model**: [Download `.onnx` file](./yolov11n-cls-noaa-esd-coral-bleaching-classifier.onnx)
- **NCNN Model**: [Download](https://huggingface.co/akridge/yolo11n-cls-noaa-esd-coral-bleaching/tree/main/yolov11n-cls-noaa-esd-coral-bleaching-classifier_ncnn_model)  
- **Torchscript Model**: [Download `.torchscript` file](./yolov11n-cls-noaa-esd-coral-bleaching-classifier.torchscript)  

## Dataset & Annotations
- **Dataset**: [NOAA ESD Coral Bleaching Classifier Dataset](https://huggingface.co/datasets/akridge/NOAA-ESD-CORAL-Bleaching-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)](https://www.fisheries.noaa.gov/inport/item/59195)
- [NOAA ESD Coral Bleaching Classifier Annotations Data Dictionary](https://www.fisheries.noaa.gov/inport/item/68138)
- [Developing a semi-automated CoralNet Bleaching Classifier: annotations and imagery from survey sites across the Hawaiian Archipelago between 2014 and 2019](https://www.fisheries.noaa.gov/inport/item/67962)


#### 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.