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Forest-Fire-Detection

Forest-Fire-Detection is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-512 for multi-class image classification. It is trained to detect whether an image contains fire, smoke, or a normal (non-fire) scene. 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

 Fire 0.9960 0.9896 0.9928 2020
 Normal 0.9902 0.9960 0.9931 2020
 Smoke 0.9995 1.0000 0.9998 2020

 accuracy 0.9952 6060
 macro avg 0.9952 0.9952 0.9952 6060
weighted avg 0.9952 0.9952 0.9952 6060

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Label Space: 3 Classes

Class 0: Fire 
Class 1: Normal 
Class 2: Smoke

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/Forest-Fire-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": "Fire",
 "1": "Normal",
 "2": "Smoke"
}

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=3, label="Forest Fire Detection"),
 title="Forest-Fire-Detection",
 description="Upload an image to detect whether the scene contains fire, smoke, or is normal."
)

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

Intended Use

Forest-Fire-Detection is designed for:

• Wildfire Monitoring – Rapid identification of forest fire and smoke zones.
• Environmental Protection – Surveillance of forest areas for early fire warning.
• Disaster Management – Support in emergency response and evacuation decisions.
• Smart Surveillance – Integrate with drones or camera feeds for automated fire detection.
• Research and Analysis – Analyze visual datasets for fire-prone region identification.

Base model