README.md

---
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
---

![zdfbdzf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/rrxsJzH4HCNufCCb9duMQ.png)


# **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
```

![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ZFECguZt7jRW7mF5ZjlH1.png)

## **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**