README.md

metadata

license: apache-2.0
datasets:
  - DamarJati/Face-Mask-Detection
language:
  - en
base_model:
  - google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
  - Face-Mask-Detection
  - SigLIP2

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

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Installation

pip install transformers torch pillow gradio

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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