NOAA ESD Coral Bleaching ViT Based Patch Image Classifier
📝 Model Overview
This model was trained to classify coral bleaching conditions using the Vision Transformer (ViT) 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.
Image Size: 224x224 (consistent with ViT input requirements)
📈 Results and Metrics
The model was evaluated using a withheld test set. The predictions were compared against human-labeled points for validation.
📄 Classification Report:
precision
recall
f1-score
CORAL
0.86
0.91
0.88
CORAL_BL
0.84
0.75
0.79
accuracy
0.85
macro avg
0.85
0.83
0.84
weighted avg
0.85
0.85
0.85
results
🚀 How to Use the Model
🔗 Load with Transformers
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("akridge/noaa-esd-coral-bleaching-vit-classifier-v1")
model = AutoModelForImageClassification.from_pretrained("akridge/noaa-esd-coral-bleaching-vit-classifier-v1")
import torch
from transformers import ViTForImageClassification, AutoImageProcessor
from PIL import Image
# ✅ Load the model and processor
model = ViTForImageClassification.from_pretrained("akridge/noaa-esd-coral-bleaching-vit-classifier-v1")
processor = AutoImageProcessor.from_pretrained("akridge/noaa-esd-coral-bleaching-vit-classifier-v1")
# ✅ Load and process image
image = Image.open("your_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
# ✅ Perform inference
with torch.no_grad():
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
id2label = model.config.id2label
print(f"Prediction: {prediction} ({id2label[prediction]})")
🔗 Use TorchScript
import torch
# ✅ Load the TorchScript model
scripted_model = torch.jit.load("noaa-esd-coral-bleaching-vit-classifier-v1.pt")
scripted_model.eval()
# ✅ Inference with TorchScript model
with torch.no_grad():
scripted_output = scripted_model(inputs["pixel_values"])
scripted_prediction = scripted_output.argmax(-1).item()
print(f"TorchScript Prediction: {id2label[scripted_prediction]}")
🔗 Use ONNX
import onnxruntime as ort
# ✅ Load the ONNX model
onnx_model = "noaa-esd-coral-bleaching-vit-classifier-v1.onnx"
ort_session = ort.InferenceSession(onnx_model)
# ✅ Prepare ONNX input
onnx_inputs = {"input": inputs["pixel_values"].numpy()}
# ✅ Run inference with ONNX
onnx_outputs = ort_session.run(None, onnx_inputs)
onnx_prediction = onnx_outputs[0].argmax(axis=1)[0]
print(f"ONNX Prediction: {id2label[onnx_prediction]}")
Intended Use
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.
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.
