prithivMLmods/Face-Mask-Detection

🤗 Hugging Face 来源image-classificationapache-2.093M 参数372 MBsafetensors✓ 12 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo prithivMLmods/Face-Mask-Detection ./model-folder
需要做种者 →

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