ArticleData in brief2023
Face mask wearing image dataset: A comprehensive benchmark for image-based face mask detection models.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- FaciaVox: A diverse multimodal biometric dataset of facial images and voice recordings.Data in brief · 2025Article
- HelmetML: A dataset of helmet images for machine learning applications.Data in brief · 2024Article
- Face detection based on K-medoids clustering and associated with convolutional neural networks.Heliyon · 2024Article
- Face mask wearing image dataset: A comprehensive benchmark for image-based face mask detection models.Data in brief · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Face Mask Wearing Image Dataset is a comprehensive collection of images aimed at facilitating research in the domain of face mask detection and classification. This dataset consists of 24,916 images, carefully categorized into two main folders: "Correct" and "Incorrect" representing instances of face masks being worn properly and improperly, respectively. Each folder is further divided into four subfolders, each denoting a specific type of face mask - Bandana, Cotton, N95, and Surgical. In the "Correct" folder, images depict individuals correctly wearing their respective face masks, while the "Incorrect" folder contains images of improper face mask usage. To capture variations in face mask application across different demographics, such as age and gender, each subfolder also includes three additional subfolders - Child, Male, and Female. The dataset's diverse content encompasses different face mask types, covering bandana-style, cloth, N95 respirators, and surgical masks, across various age groups and genders. This design ensures a comprehensive representation of real-world scenarios, enabling the evaluation of machine learning algorithms for face mask detection and classification. Researchers can leverage this dataset to develop and assess models that can accurately identify and distinguish between correct and incorrect face mask usage. By contributing to the advancement of face mask detection technologies, this dataset further supports public health initiatives and encourages proper mask-wearing behavior to mitigate the spread of infectious diseases, particularly during times of heightened health concerns such as the COVID-19 pandemic.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.