👁 2.png

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.

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

👁 download.png

Label Space: 2 Classes

Class 0: Flooded Scene 
Class 1: Non Flooded

Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

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()

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.

Base model