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CPPE - 5 Twitter

GitHub Repo stars arXiv Models TF Hub

PyPI Code style: black

CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset 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.

Accompanying paper: CPPE - 5: Medical Personal Protective Equipment Dataset

by Rishit Dagli and Ali Mustufa Shaikh.

Some features of this dataset are:

  • high quality images and annotations (~4.6 bounding boxes per image)
  • real-life images unlike any current such dataset
  • majority of non-iconic images (allowing easy deployment to real-world environments)
  • >15 pre-trained models in the model zoo availaible to directly use (also for mobile and edge devices)


Get the data

We strongly recommend you use either the downlaoder script or the Python package to download the dataset however you could also download and extract it manually.

Name Size Drive Bucket MD5 checksum
dataset.tar.gz ~230 MB Download Download f4e043f983cff94ef82ef7d57a879212

Downloader Script

The easiest way to download the dataset is to use the downloader script:

git clone
cd CPPE-Dataset
bash tools/

Python package

You can also use the Python package to get the dataset:

pip install cppe5
import cppe5

Data Loaders

We provide PyTorch and TensorFlow data loaders in this repository, the dataset can also be loaded from Hugging Face Datasets. To use the data loaders in this repository you would need to install the Python package first:

pip install cppe5

Hugging Face Datasets

Install the datasets library first:

pip install datasets
from datasets import load_dataset

dataset = load_dataset("cppe-5")

PyTorch DataLoader

A ready to run Google Colab example can be found at notebooks/pytorch_loader.ipynb.

import cppe5
from cppe5.torch import data_loader
import os

data_loader = cppe5.torch.data_loader() #

# Fetch all images and annotations
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

# DataLoader is iterable over Dataset
for imgs, annotations in data_loader:
    imgs = list( for img in imgs)
    annotations = [{k: for k, v in t.items()} for t in annotations]

TensorFlow Loader

A ready to run Google Colab example can be found at notebooks/tensorflow_loader.ipynb.

import cppe5
from cppe5.tensorflow import data_loader


dataset = cppe5.tensorflow.data_loader() #


The dataset contains the following labels:

Label Description
1 Coverall
2 Face_Shield
3 Gloves
4 Goggles
5 Mask

Model Zoo

More information about the pre-trained models (like modlel complexity or FPS benchmark) could be found in and includes models ready for deployment on mobile and edge devices.

Baseline Models

This section contains the baseline models that are trained on the CPPE-5 dataset . More information about how these are trained could be found in the original paper and the config files.

Method APbox AP50box AP75box APSbox APMbox APLbox Configs PyTorch model TensorFlow model
SSD 29.50 57.0 24.9 32.1 23.1 34.6 config bucket bucket
YOLO 38.5 79.4 35.3 23.1 28.4 49.0 config bucket bucket
Faster RCNN 44.0 73.8 47.8 30.0 34.7 52.5 config bucket bucket

SoTA Models

This section contains the SoTA models that are trained on the CPPE-5 dataset . More information about how these are trained could be found in the original paper and the config files.

Method APbox AP50box AP75box APSbox APMbox APLbox Configs PyTorch model TensorFlow model
RepPoints 43.0 75.9 40.1 27.3 36.7 48.0 config bucket -
Sparse RCNN 44.0 69.6 44.6 30.0 30.6 54.7 config bucket -
FCOS 44.4 79.5 45.9 36.7 39.2 51.7 config bucket bucket
Grid RCNN 47.5 77.9 50.6 43.4 37.2 54.4 config bucket -
Deformable DETR 48.0 76.9 52.8 36.4 35.2 53.9 config bucket -
FSAF 49.2 84.7 48.2 45.3 39.6 56.7 config bucket bucket
Localization Distillation 50.9 76.5 58.8 45.8 43.0 59.4 config bucket -
VarifocalNet 51.0 82.6 56.7 39.0 42.1 58.8 config bucket -
RegNet 51.3 85.3 51.8 35.7 41.1 60.5 config bucket bucket
Double Heads 52.0 87.3 55.2 38.6 41.0 60.8 config bucket -
DCN 51.6 87.1 55.9 36.3 41.4 61.3 config bucket -
Empirical Attention 52.5 86.5 54.1 38.7 43.4 61.0 config bucket -
TridentNet 52.9 85.1 58.3 42.6 41.3 62.6 config bucket bucket


We also include the following tools in this repository to make working with the dataset a lot easier:

  • Download data
  • Download TF Record files
  • Convert PNG images in dataset to JPG Images
  • Converting Pascal VOC to COCO format
  • Update dataset to use relative paths

More information about each tool can be found in the tools/ file.


We also present some tutorials on how to use the dataset in this repository as Colab notebooks:

In this notebook we will load the CPPE - 5 dataset in PyTorch and also see a quick example of fine-tuning the Faster RCNN model with torchvision on this dataset.

In this notebook we will load the CPPE - 5 dataset through TF Record files in TensorFlow.

In this notebook, we will visualize the CPPE-5 dataset, which could be really helpful to see some sample images and annotations from the dataset.


If you use this work, please cite the following paper:


      title={CPPE-5: Medical Personal Protective Equipment Dataset}, 
      author={Rishit Dagli and Ali Mustufa Shaikh},


Dagli, Rishit, and Ali Mustufa Shaikh. ‘CPPE-5: Medical Personal Protective Equipment Dataset’. ArXiv:2112.09569 [Cs], Dec. 2021.,


The authors would like to thank Google for supporting this work by providing Google Cloud credits. The authors would also like to thank Google TPU Research Cloud (TRC) program for providing access to TPUs. The authors are also grateful to Omkar Agrawal for help with verifying the difficult annotations.

Want to Contribute 🙋‍♂️?

Awesome! If you want to contribute to this project, you're always welcome! See Contributing Guidelines. You can also take a look at open issues for getting more information about current or upcoming tasks.

Want to discuss? 💬

Have any questions, doubts or want to present your opinions, views? You're always welcome. You can start discussions.

Have you used this work in your paper, blog, experiments, or more please share it with us by making a discussion under the Show and Tell category.