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---
license: apache-2.0
datasets:
- Shravanig/fire_detection_final
language:
- en
base_model:
- google/siglip2-base-patch16-512
pipeline_tag: image-classification
library_name: transformers
tags:
- Forest-Fire-Detection
- SigLIP2
- climate
- Smoke
- Normal
- Fire
---
# 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.
> [!note]
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features : https://arxiv.org/pdf/2502.14786
```py
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
```
---
## Label Space: 3 Classes
```
Class 0: Fire
Class 1: Normal
Class 2: Smoke
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
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. |