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https://github.com/rishit-dagli/cppe-dataset
Code for our paper CPPE - 5 (Medical Personal Protective Equipment), a new challenging object detection dataset
https://github.com/rishit-dagli/cppe-dataset
artificial-intelligence computer-vision cppe5 data dataset deep-learning machine-learning models object-detection pretrained-models pytorch tensorflow vision
Last synced: 13 days ago
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Code for our paper CPPE - 5 (Medical Personal Protective Equipment), a new challenging object detection dataset
- Host: GitHub
- URL: https://github.com/rishit-dagli/cppe-dataset
- Owner: Rishit-dagli
- License: apache-2.0
- Created: 2021-03-17T11:02:53.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-22T01:35:49.000Z (3 months ago)
- Last Synced: 2024-10-23T14:07:50.543Z (21 days ago)
- Topics: artificial-intelligence, computer-vision, cppe5, data, dataset, deep-learning, machine-learning, models, object-detection, pretrained-models, pytorch, tensorflow, vision
- Language: Python
- Homepage: https://arxiv.org/abs/2112.09569
- Size: 9.7 MB
- Stars: 66
- Watchers: 3
- Forks: 15
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.bib
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
# CPPE - 5 [](https://twitter.com/intent/tweet?text=Wow:&url=https%3A%2F%2Fgithub.com%2FRishit-dagli%2Fhttps://github.com/Rishit-dagli/CPPE-Dataset)
[](https://arxiv.org/abs/2112.09569)
[](https://tfhub.dev/rishit-dagli/collections/cppe5)
[](https://github.com/psf/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](https://arxiv.org/abs/2112.09569)**_
_**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](https://github.com/mariosasko) for creating a [Hugging Face Datasets loader](https://huggingface.co/datasets/cppe-5).
- 05/01/2022 - This paper got featured on Google Research TRC's [publication section](https://sites.research.google/trc/publications/)
- 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 | wget Link | MD5 checksum |
|:----:|:----:|:----:|:----:|:------------:|
| `dataset.tar.gz` | ~230 MB | [Download](https://drive.google.com/file/d/1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr/view?usp=sharing) | [Download](https://github.com/Rishit-dagli/CPPE-Dataset/releases/download/v0.1.0/dataset.tar.gz) | `f4e043f983cff94ef82ef7d57a879212` |
### Downloader Script
The easiest way to download the dataset is to use the downloader script:
```bash
git clone https://github.com/Rishit-dagli/CPPE-Dataset.git
cd CPPE-Dataset
bash tools/download_data.sh
```
### Python package
You can also use the Python package to get the dataset:
```bash
pip install cppe5
```
```python
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](https://github.com/huggingface/datasets). To use the data
loaders in this repository you would need to install the Python package
first:
```bash
pip install cppe5
```
### Hugging Face Datasets
Install the datasets library first:
```bash
pip install datasets
```
```py
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](notebooks/pytorch_loader.ipynb).
```py
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](notebooks/tensorflow_loader.ipynb).
```py
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](MODEL_ZOO.md)
and [LITE_MODEL_ZOO.md](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](baselines/ssd.config) | [tb.dev](https://tensorboard.dev/experiment/2EimzQz9Q4GCJjYsyo1MKQ/) | [bucket]() | [bucket](https://storage.googleapis.com/cppe-5/trained_models/ssd/tf_ssd.tar.gz) |
| YOLO | 38.5 | 79.4 | 35.3 | 23.1 | 28.4 | 49.0 | [config](baselines/yolov3_d53_mstrain-608_273e_coco.py) | [tb.dev](https://tensorboard.dev/experiment/5JrpU22hRnOOOXCLKvxFyQ) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/yolo/yolov3_d53_608_273e-2942d1ca.pth) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/yolo/yolo.tar.gz) |
| Faster RCNN | 44.0 | 73.8 | 47.8 | 30.0 | 34.7 | 52.5 | [config](baselines/faster_rcnn_r101_fpn_2x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/20XQ37HgQUyMJuOlbqmVDQ/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/faster_rcnn/faster_rcnn_r101_fpn_2x_coco-77efa99b.pth) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/faster_rcnn/faster_rcnn.tar.gz) |
### 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](configs/reppoints_moment_r50_fpn_gn_2x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/Co6JQVe1RDmxgbMx4gD0Qg/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/reppoints/reppoints_moment_r50_fpn_gn_2x_coco-18beef36.pth) | - |
| Sparse RCNN | 44.0 | 69.6 | 44.6 | 30.0 | 30.6 | 54.7 | [config](configs/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/se3w7zQ7SlyE6T8q59P79w/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.pth) | - |
| FCOS | 44.4 | 79.5 | 45.9 | 36.7 | 39.2 | 51.7 | [config](configs/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/O343s1kRQIKTqs508jESDA/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-031dc428.pth) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/fcos/tf_fcos.tar.gz) |
| Grid RCNN | 47.5 | 77.9 | 50.6 | 43.4 | 37.2 | 54.4 | [config](configs/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/fgGkJ4IBSZmDQj1QEKgXqA/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco-65319c19.pth) | - |
| Deformable DETR | 48.0 | 76.9 | 52.8 | 36.4 | 35.2 | 53.9 | [config](configs/deformable_detr_refine_r50_16x2_50e_coco.py) | [tb.dev](https://tensorboard.dev/experiment/uq80boznQY2iJVhWSXAKTw/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/deformable_detr/deformable_detr_refine_r50_16x2_50e-d36a2db1.pth) | - |
| FSAF | 49.2 | 84.7 | 48.2 | 45.3 | 39.6 | 56.7 | [config](configs/fsaf_x101_64x4d_fpn_1x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/jUa0QjFJQZe68o4vbP194Q/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/fsaf/fsaf_x101_64x4d_fpn_1x_coco-7284d216.pth) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/fsaf/tf_fsaf.tar.gz) |
| Localization Distillation | 50.9 | 76.5 | 58.8 | 45.8 | 43.0 | 59.4 | [config](configs/ld_r50_gflv1_r101_fpn_coco_1x.py) | [tb.dev](https://tensorboard.dev/experiment/UMGK5cbATVSDZM5DKN1QAA/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/ld/ld_r50_gflv1_r101_fpn_coco_1x-e12b2422.pth) | - |
| VarifocalNet | 51.0 | 82.6 | 56.7 | 39.0 | 42.1 | 58.8 | [config](configs/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/bE7LlxNLRU2nGanjxEs2rg/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco-8d841df9.pth) | - |
| RegNet | 51.3 | 85.3 | 51.8 | 35.7 | 41.1 | 60.5 | [config](configs/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/eYyj3lwcR5O3XDbuyFZ81Q/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/regnet/regnet-4GF-987ef260.pth) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/regnet/regnet.tar.gz) |
| Double Heads | 52.0 | 87.3 | 55.2 | 38.6 | 41.0 | 60.8 | [config](configs/dh_faster_rcnn_r50_fpn_1x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/cLMEyMJEQPqWXWeW4XpRkA/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/double_heads/dh_faster_rcnn_r50_fpn_1x_coco-b10cef7a.pth) | - |
| DCN | 51.6 | 87.1 | 55.9 | 36.3 | 41.4 | 61.3 | [config](configs/faster_rcnn_r50_fpn_mdpool_1x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/GWTGBFo5TruxPlazzkIpXQ/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco-1d85638a.pth) | - |
| Empirical Attention | 52.5 | 86.5 | 54.1 | 38.7 | 43.4 | 61.0 | [config](configs/) | [tb.dev](https://tensorboard.dev/experiment/56OgPsWLTWe1jhAV1i00iw/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco-f69549ae.pth) | - |
| TridentNet | 52.9 | 85.1 | 58.3 | 42.6 | 41.3 | 62.6 | [config](configs/tridentnet_r50_caffe_mstrain_3x_coco.py) | [tb.dev](https://tensorboard.dev/experiment/9O0MAFnlRMWWezz1TbLYGQ/) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco-eb569217.pth) | [bucket](https://storage.googleapis.com/cppe-5/trained_models/tridentnet/tf_tridentnet.tar.gz) |
## 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](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](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](notebooks/tensorflow_loader.ipynb)
In this notebook we will load the CPPE - 5 dataset through TF Record files in TensorFlow.
- [visualize.ipynb](notebooks/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:**
```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](CONTRIBUTING.md). You can also take a look at [open issues](https://github.com/Rishit-dagli/CPPE-Dataset/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](hhttps://github.com/Rishit-dagli/CPPE-Dataset/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](https://github.com/Rishit-dagli/CPPE-Dataset/discussions/categories/show-and-tell).