SigLIP2 Content Filters 052025 Patch 1
Collection
Moderation, Balance, Classifiers
•
7 items
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Updated
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.
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
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
pip install transformers torch pillow gradio
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()
Base model
google/siglip2-base-patch16-224