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Resnet50 Mask Classification
R
Resnet50 Mask Classification
Developed by AllanOuii
A binary classification model based on ResNet50 architecture for detecting whether people in images are wearing masks, with an accuracy rate of 97.7%
Downloads 23
Release Time : 2/10/2023
Model Overview
This model is trained using AutoTrain and is specifically designed to identify whether people in images are wearing masks, suitable for epidemic prevention monitoring in public places
Model Features
High-precision recognition
Achieves 97.7% accuracy and 100% recall rate on the test set
Fast inference
Based on the optimized ResNet50 architecture, enabling real-time image classification
Eco-friendly training
The training process produces only 1.55 grams of CO2 emissions
Model Capabilities
Image classification
Mask detection
Real-time monitoring
Use Cases
Public health
Public place epidemic prevention monitoring
Automatically detects mask-wearing status of individuals in public places such as shopping malls and stations
Real-time statistics on mask-wearing rates to assist in epidemic prevention management
Smart access control system
Integrated into access control systems to ensure entrants wear masks
Automatically blocks individuals not wearing masks
Education sector
Campus epidemic prevention management
Monitors mask-wearing status in areas like classrooms and cafeterias
Generates daily compliance reports
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