Mask detection is a task in where it is wanted to detect whether a person is wearing a mask or not. It seems like a simple problem, but some facets, such as the fact that people wear such masks and respirators in a multitude of ways, are often ignored and trivialized to the worn/not worn case.
The database is ready-to-be-used and, due to its size, it was divided into three files:
- WWMR-DB - part 1.zip and WWMR-DB - part 2.zip files contain the database images;
- WWMR-DB - Labels.zip file contains the labels in PascalVOC and the YOLO format for each database image.
Due to the limitations imposed by the coronavirus, the database was created by asking volunteers for selfies through Google Forms. For this reason:
- number of images per class
- image quality
- intra-class differences
- rotation of the face
could also have great variations.
Google Forms are still open: if you want to contribute to the database, you can easily submit your images through the following links:
- Front photos (at 0 degrees): https://forms.gle/qLBjfCVhGoaJhnSo9
- Side photos (at 45 degrees): https://forms.gle/D3BuUQgjBLd6dqPj6
- Profile photos (at 90 degrees): https://forms.gle/yHgCAgcGJrfC7X4YA
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This dataset has been developed based on the work of the GeoCOV19Tweets Dataset. The original work by Lamsal, R. runs network analysis on a similar dataset to understand the underlying relationship between countries and hashtags. The work did an analysis on roughly 300k number of [country, hashtag] relations from 190 countries and territories, and 5055 unique hashtags.
This dataset provides [place, hashtag] relationships in a Comma-separated values (CSV) file. Each line represents a relationship. You can simply use the CSV file as per your research needs.
However, if you need to change the place entity from city (currently the dataset uses ["place"]["name"] object) to country, you'll have to consider the ["place"]["country"] object instead. The sample script is provided with this dataset. The script takes in a list of tweet IDs present in a CSV file and hydrates the IDs to extract places and hashtags relationships. The script is written for twarc.
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BIMCV-COVID19- dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of no COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
Once all the compressed files have been downloaded, use 00_extract_data.sh for their correct decompression. For more information, you could see the links on this page.
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The MCData was designed and produced for mouth cavity detection and segmentation. This dataset can be utilized for training and testing of mouth cavity instance segmentation networks. This dataset is the first available dataset for detecting and segmentation of mouth cavity main components to the best of the authors’ knowledge.
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BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
Once all the compressed files have been downloaded, use 00_extract_data.sh for their correct decompression. For more information, you could see the links on this page
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Automatic detection of COVID-19 and community-acquired pneumonia on CT images with artificial intelligence
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This data resource is an outcome of the NSF RAPID project titled "Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab" awarded to University of Missouri-Columbia.
The resource contains the output of variant analysis (along with CADD scores) on human genome sequences obtained from the COVID-19 Data Portal. The variants include single nucleotide polymorphisms (SNPs) and short insert and deletes (indels).
1. Download a .zip file.
2. Unzip the file and extract it into a folder.
3. There will be two folders, namely, VCF and CADD_Scores. These folders contain the compressed .vcf and .tsv files. The .vcf files are filtered VCF files produced by the GATK best practice workflow for RNA-seq data. The reference genome hg19 was used. There is also a .xlsx file containing the run accession IDs (e.g., SRR12095153) and URLs (e.g., https://www.ebi.ac.uk/ena/browser/view/SRR12095153) from where the paired end sequences were downloaded. Complete description of the sequences can be found via these URLs.
4. Check for new .zip files.
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The 3DLSC-COVID datset includes a total of 1,805 3D chest CT scans with more than 570,000 CT slices were collected from 2 standard CT scanners of Liyuan Hospital, i.e., UIH uCT 510 and GE Optima CT600. Among all CT scans, there were 794 positive cases of COVID-19, which were further confirmed by clinical symptoms and RT-PCR from January 16 to April 16, 2020.
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This dataset is consist news articles related to COVID-19 from UK, India, Japan and South Korea newspapers.
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The dataset contains the data on ICU-transferred (N=100) and Stable (N=131) patients with COVID-19 (N=156) and Non-COVID-19 viral pneumonia (N=75). Among COVID-19 patients of this study, 82 patients developed Refractory Respiratory Failure (RRF) or Severe Acute Respiratory Distress Syndrome (SARDS) and were transferred to Intensive Care Unit (ICU), 74 patients had a Stable course of disease and were not transferred to ICU.
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399 Views