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Description:

cppe5 0.1.1

CPPE - 5

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)

Updates

06/01/2022 - Many thanks to @mariosasko for creating a Hugging Face Datasets loader.
05/01/2022 - This paper got featured on Google Research TRC's publication section
20/12/2021 - First public release of the CPPE - 5 dataset on arXiv

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 https://github.com/Rishit-dagli/CPPE-Dataset.git
cd CPPE-Dataset
bash tools/download.sh

Python package
You can also use the Python package to get the dataset:
pip install cppe5

import cppe5
cppe5.download_data()

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

cppe5.download_data()
os.chdir("..")
data_loader = cppe5.torch.data_loader() # torch.utils.data.DataLoader

# 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(img.to(device) for img in imgs)
annotations = [{k: v.to(device) 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

cppe5.download_tfrecords()
os.chdir("..")

dataset = cppe5.tensorflow.data_loader() # tf.data.Dataset
iter(dataset).next()

Labels
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 MODEL_ZOO.md
and LITE_MODEL_ZOO.md 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
TensorBoard.dev
PyTorch model
TensorFlow model

SSD
29.50
57.0
24.9
32.1
23.1
34.6
config
tb.dev
bucket
bucket

YOLO
38.5
79.4
35.3
23.1
28.4
49.0
config
tb.dev
bucket
bucket

Faster RCNN
44.0
73.8
47.8
30.0
34.7
52.5
config
tb.dev
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
TensorBoard.dev
PyTorch model
TensorFlow model

RepPoints
43.0
75.9
40.1
27.3
36.7
48.0
config
tb.dev
bucket
-

Sparse RCNN
44.0
69.6
44.6
30.0
30.6
54.7
config
tb.dev
bucket
-

FCOS
44.4
79.5
45.9
36.7
39.2
51.7
config
tb.dev
bucket
bucket

Grid RCNN
47.5
77.9
50.6
43.4
37.2
54.4
config
tb.dev
bucket
-

Deformable DETR
48.0
76.9
52.8
36.4
35.2
53.9
config
tb.dev
bucket
-

FSAF
49.2
84.7
48.2
45.3
39.6
56.7
config
tb.dev
bucket
bucket

Localization Distillation
50.9
76.5
58.8
45.8
43.0
59.4
config
tb.dev
bucket
-

VarifocalNet
51.0
82.6
56.7
39.0
42.1
58.8
config
tb.dev
bucket
-

RegNet
51.3
85.3
51.8
35.7
41.1
60.5
config
tb.dev
bucket
bucket

Double Heads
52.0
87.3
55.2
38.6
41.0
60.8
config
tb.dev
bucket
-

DCN
51.6
87.1
55.9
36.3
41.4
61.3
config
tb.dev
bucket
-

Empirical Attention
52.5
86.5
54.1
38.7
43.4
61.0
config
tb.dev
bucket
-

TridentNet
52.9
85.1
58.3
42.6
41.3
62.6
config
tb.dev
bucket
bucket

Tools
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/README.md file.
Tutorials
We also present some tutorials on how to use the dataset in this repository as
Colab notebooks:

pytorch_loader.ipynb

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.

tensorflow_loader.ipynb

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

visualize.ipynb

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.
Citation
If you use this work, please cite the following paper:
BibTeX:
@misc{dagli2021cppe5,
title={CPPE-5: Medical Personal Protective Equipment Dataset},
author={Rishit Dagli and Ali Mustufa Shaikh},
year={2021},
eprint={2112.09569},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

MLA:
Dagli, Rishit, and Ali Mustufa Shaikh. ‘CPPE-5: Medical Personal Protective Equipment Dataset’. ArXiv:2112.09569 [Cs], Dec. 2021. arXiv.org, http://arxiv.org/abs/2112.09569.

Acknoweldgements
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.