CPPE-5: Medical Personal Protective Equipment Dataset

• URL: http://arxiv.org/abs/2112.09569v1
• Date: Wed, 15 Dec 2021 18:45:55 GMT
• Title: CPPE-5: Medical Personal Protective Equipment Dataset
• Authors: Rishit Dagli and Ali Mustufa Shaikh
• Abstract summary: This dataset includes 5 object categories (coveralls, face shield, gloves, mask, and goggles) and each image is annotated with a set of bounding boxes and positive labels. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 focusing 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 shield, gloves, mask, 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 .

Related papers

• Kvasir-VQA: A Text-Image Pair GI Tract Dataset [4.250633109741797]
  This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments. The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification.
  arXiv  Detail & Related papers  (2024-09-02T19:41:59Z)
• Diffusion Models as Data Mining Tools [87.77999285241219]
  This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. We show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure. This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease.
  arXiv  Detail & Related papers  (2024-07-20T17:14:31Z)
• 360 in the Wild: Dataset for Depth Prediction and View Synthesis [66.58513725342125]
  We introduce a large scale 360$circ$ videos dataset in the wild. This dataset has been carefully scraped from the Internet and has been captured from various locations worldwide. Each of the 25K images constituting our dataset is provided with its respective camera's pose and depth map.
  arXiv  Detail & Related papers  (2024-06-27T05:26:38Z)
• DEArt: Dataset of European Art [0.17188280334580194]
  We propose DEArt, an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning.
  arXiv  Detail & Related papers  (2022-11-02T16:05:35Z)
• State-of-the-art Models for Object Detection in Various Fields of Application [0.0]
  COCO minival, COCO test, Pascal VOC 2007, ADE20K, and ImageNet are reviewed. The datasets are handpicked after closely comparing them with the rest in terms of diversity, quality of data, minimal bias, labeling quality etc. It lists the top models and their optimal use cases for each of the respective datasets.
  arXiv  Detail & Related papers  (2022-11-01T20:25:32Z)
• A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and Ground Truth [61.90504318229845]
  This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene. A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
  arXiv  Detail & Related papers  (2022-06-13T19:14:06Z)
• Free Lunch for Co-Saliency Detection: Context Adjustment [14.688461235328306]
  We propose a "cost-free" group-cut-paste (GCP) procedure to leverage images from off-the-shelf saliency detection datasets and synthesize new samples. We collect a novel dataset called Context Adjustment Training. The two variants of our dataset, i.e., CAT and CAT+, consist of 16,750 and 33,500 images, respectively.
  arXiv  Detail & Related papers  (2021-08-04T14:51:37Z)
• Personalized Image Semantic Segmentation [58.980245748434]
  We generate more accurate segmentation results on unlabeled personalized images by investigating the data's personalized traits. We propose a baseline method that incorporates the inter-image context when segmenting certain images. The code and the PIS dataset will be made publicly available.
  arXiv  Detail & Related papers  (2021-07-24T04:03:11Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort [117.41383937100751]
  Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. These generated datasets can then be used for training any computer vision architecture just as real datasets are.
  arXiv  Detail & Related papers  (2021-04-13T20:08:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.