Face-Mask-Detection
Face-Mask-Detection is a binary image classification model based on
google/siglip2-base-patch16-224, trained to detect whether a person is wearing a face mask or not. This model can be used in public health monitoring, access control systems, and workplace compliance enforcement.
Classification Report:
precision recall f1-score support
Face_Mask Found 0.9662 0.9561 0.9611 5883
Face_Mask Not_Found 0.9568 0.9667 0.9617 5909
accuracy 0.9614 11792
macro avg 0.9615 0.9614 0.9614 11792
weighted avg 0.9615 0.9614 0.9614 11792
Label Classes
The model distinguishes between the following face mask statuses:
0: Face_Mask Found
1: Face_Mask Not_Found
Installation
pip install transformers torch pillow gradio
Example Inference Code
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Face-Mask-Detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# ID to label mapping
id2label = {
"0": "Face_Mask Found",
"1": "Face_Mask Not_Found"
}
def detect_face_mask(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_face_mask,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Mask Status"),
title="Face-Mask-Detection",
description="Upload an image to check if a person is wearing a face mask or not."
)
if __name__ == "__main__":
iface.launch()
Applications
- COVID-19 Compliance Monitoring
- Security and Access Control
- Automated Surveillance Systems
- Health Safety Enforcement in Public Spaces