![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/E4Cd-Kbj9wUkI9t_UOqE8.png)

# 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
```

![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/xbB-O5F_pT10R9rLah_R3.png)

---

## 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.
fire normal smoke