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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.
```py
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 🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
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. |