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---
license: apache-2.0
datasets:
- blanchon/FireRisk
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- fire-risk
- detection
- siglip2
---
# **Fire-Risk-Detection**
> **Fire-Risk-Detection** is a multi-class image classification model based on `google/siglip2-base-patch16-224`, trained to detect **fire risk levels** in geographical or environmental imagery. This model can be used for **wildfire monitoring**, **forest management**, and **environmental safety**.
---
```py
Classification Report:
precision recall f1-score support
high 0.4430 0.3382 0.3835 6296
low 0.3666 0.2296 0.2824 10705
moderate 0.3807 0.3757 0.3782 8617
non-burnable 0.8429 0.8385 0.8407 17959
very_high 0.3920 0.3400 0.3641 3268
very_low 0.6068 0.7856 0.6847 21757
water 0.9241 0.7744 0.8427 1729
accuracy 0.6032 70331
macro avg 0.5652 0.5260 0.5395 70331
weighted avg 0.5860 0.6032 0.5878 70331
```
## **Label Classes**
The model distinguishes between the following fire risk levels:
```
0: high
1: low
2: moderate
3: non-burnable
4: very_high
5: very_low
6: water
```
---
## **Installation**
```bash
pip install transformers torch pillow gradio
```
---
## **Example 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/Fire-Risk-Detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# ID to label mapping
id2label = {
"0": "high",
"1": "low",
"2": "moderate",
"3": "non-burnable",
"4": "very_high",
"5": "very_low",
"6": "water"
}
def detect_fire_risk(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=detect_fire_risk,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=7, label="Fire Risk Level"),
title="Fire-Risk-Detection",
description="Upload an image to classify the fire risk level: very_low, low, moderate, high, very_high, non-burnable, or water."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Applications**
* **Wildfire Early Warning Systems**
* **Environmental Monitoring**
* **Land Use Assessment**
* **Disaster Preparedness and Mitigation** |