  We present a new challenging dataset, CPPE - 5 (Medical Personal Protective
Equipment), with the goal to allow the study of subordinate categorization of
medical personal protective equipments, which is not possible with other
popular data sets that focus on broad-level categories (such as PASCAL VOC,
ImageNet, Microsoft COCO, OpenImages, etc). To make it easy for models trained
on this dataset to be used in practical scenarios in complex scenes, our
dataset mainly contains images that show complex scenes with several objects in
each scene in their natural context. The image collection for this dataset
focuses on: obtaining as many non-iconic images as possible and making sure all
the images are real-life images, unlike other existing datasets in this area.
Our dataset includes 5 object categories (coveralls, face shields, gloves,
masks, and goggles), and each image is annotated with a set of bounding boxes
and positive labels. We present a detailed analysis of the dataset in
comparison to other popular broad category datasets as well as datasets
focusing on personal protective equipments, we also find that at present there
exist no such publicly available datasets. Finally, we also analyze performance
and compare model complexities on baseline and state-of-the-art models for
bounding box results. Our code, data, and trained models are available at
https://git.io/cppe5-dataset.
