README.md

metadata

license: apache-2.0
datasets:
  - aneeshd27/Corals-Classification
language:
  - en
base_model:
  - google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
  - Corals
  - Bleach
  - Healthy
  - Classification
  - Siglip2
  - ViT

Coral-Health

Coral-Health is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify coral reef images into two health conditions using the SiglipForImageClassification architecture.

Classification Report:
                 precision    recall  f1-score   support

Bleached Corals     0.8677    0.7561    0.8081      4850
 Healthy Corals     0.7665    0.8742    0.8168      4442

       accuracy                         0.8125      9292
      macro avg     0.8171    0.8151    0.8124      9292
   weighted avg     0.8193    0.8125    0.8122      9292

The model categorizes images into two classes:

• Class 0: Bleached Corals
• Class 1: Healthy Corals

---

Run with Transformers 🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Coral-Health"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Updated labels
labels = {
    "0": "Bleached Corals",
    "1": "Healthy Corals"
}

def coral_health_detection(image):
    """Predicts the health condition of coral reefs in the image."""
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
    
    return predictions

# Create Gradio interface
iface = gr.Interface(
    fn=coral_health_detection,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Prediction Scores"),
    title="Coral Health Detection",
    description="Upload an image of coral reefs to classify their condition as Bleached or Healthy."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()

---

Intended Use:

The Coral-Health model is designed to support marine conservation and environmental monitoring. Potential use cases include:

• Coral Reef Monitoring: Helping scientists and conservationists track coral bleaching events.
• Environmental Impact Assessment: Analyzing reef health in response to climate change and pollution.
• Educational Tools: Raising awareness about coral reef health in classrooms and outreach programs.
• Automated Drone/ROV Analysis: Enhancing automated underwater monitoring workflows.