Flood-Image-Detection

Flood-Image-Detection

Flood-Image-Detection is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-512 for binary image classification. It is trained to detect whether an image contains a flooded scene or non-flooded environment. The model uses the SiglipForImageClassification architecture.

[!note] SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features : https://arxiv.org/pdf/2502.14786

Classification Report:
               precision    recall  f1-score   support

Flooded Scene     0.9172    0.9458    0.9313       609
  Non Flooded     0.9744    0.9603    0.9673      1309

     accuracy                         0.9557      1918
    macro avg     0.9458    0.9530    0.9493      1918
 weighted avg     0.9562    0.9557    0.9559      1918

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Label Space: 2 Classes

Class 0: Flooded Scene  
Class 1: Non Flooded

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Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

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Inference Code

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

# Load model and processor
model_name = "prithivMLmods/flood-image-detection"  # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Updated label mapping
id2label = {
    "0": "Flooded Scene",
    "1": "Non Flooded"
}

def classify_image(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()

    prediction = {
        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
    }

    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="Flood Detection"),
    title="Flood-Image-Detection",
    description="Upload an image to detect whether the scene is flooded or not."
)

if __name__ == "__main__":
    iface.launch()

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Intended Use

Flood-Image-Detection is designed for:

• Disaster Monitoring – Rapid detection of flood-affected areas from imagery.
• Environmental Analysis – Track flooding patterns across regions using image datasets.
• Crisis Response – Assist emergency services in identifying critical zones.
• Surveillance and Safety – Monitor infrastructure or locations for flood exposure.
• Smart Alert Systems – Integrate with IoT or camera feeds for automated flood alerts.