|
|
--- |
|
|
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** |
