{"id":15642107,"url":"https://github.com/rishit-dagli/cppe-dataset","last_synced_at":"2025-06-15T00:05:47.198Z","repository":{"id":44350356,"uuid":"348677516","full_name":"Rishit-dagli/CPPE-Dataset","owner":"Rishit-dagli","description":"Code for our paper CPPE - 5 (Medical Personal Protective Equipment), a new challenging object detection dataset","archived":false,"fork":false,"pushed_at":"2024-08-22T01:35:49.000Z","size":10171,"stargazers_count":69,"open_issues_count":0,"forks_count":14,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-15T00:05:45.175Z","etag":null,"topics":["artificial-intelligence","computer-vision","cppe5","data","dataset","deep-learning","machine-learning","models","object-detection","pretrained-models","pytorch","tensorflow","vision"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2112.09569","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Rishit-dagli.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.bib","codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-03-17T11:02:53.000Z","updated_at":"2025-06-03T07:15:50.000Z","dependencies_parsed_at":"2022-08-29T20:01:43.161Z","dependency_job_id":"ace180e4-61a7-4b5e-b868-a621244475fb","html_url":"https://github.com/Rishit-dagli/CPPE-Dataset","commit_stats":{"total_commits":88,"total_committers":2,"mean_commits":44.0,"dds":"0.022727272727272707","last_synced_commit":"964693aa4971c902c9cc0dbb6aa4fc0dcb9229c3"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/Rishit-dagli/CPPE-Dataset","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishit-dagli%2FCPPE-Dataset","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishit-dagli%2FCPPE-Dataset/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishit-dagli%2FCPPE-Dataset/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishit-dagli%2FCPPE-Dataset/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Rishit-dagli","download_url":"https://codeload.github.com/Rishit-dagli/CPPE-Dataset/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishit-dagli%2FCPPE-Dataset/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259901381,"owners_count":22929224,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","computer-vision","cppe5","data","dataset","deep-learning","machine-learning","models","object-detection","pretrained-models","pytorch","tensorflow","vision"],"created_at":"2024-10-03T11:54:33.484Z","updated_at":"2025-06-15T00:05:46.838Z","avatar_url":"https://github.com/Rishit-dagli.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CPPE - 5 [![Twitter](https://img.shields.io/twitter/url?style=social\u0026url=https%3A%2F%2Fgithub.com%2FRishit-dagli%2Fhttps://github.com/Rishit-dagli/CPPE-Dataset)](https://twitter.com/intent/tweet?text=Wow:\u0026url=https%3A%2F%2Fgithub.com%2FRishit-dagli%2Fhttps://github.com/Rishit-dagli/CPPE-Dataset)\n\n![GitHub Repo stars](https://img.shields.io/github/stars/Rishit-dagli/CPPE-Dataset?style=social)\n[![arXiv](https://img.shields.io/badge/paper-arXiv:2112.09569-b31b1b.svg?logo=arxiv)](https://arxiv.org/abs/2112.09569)\n[![Models TF Hub](https://img.shields.io/badge/Models-TF%20Hub-orange?style=flat\u0026logo=tensorflow)](https://tfhub.dev/rishit-dagli/collections/cppe5)\n\n![PyPI](https://img.shields.io/pypi/v/cppe5)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\nCPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset\nwith the goal to allow the study of subordinate categorization of medical\npersonal protective equipments, which is not possible with other popular data\nsets that focus on broad level categories.\n\n_**Accompanying paper: [CPPE - 5: Medical Personal Protective Equipment Dataset](https://arxiv.org/abs/2112.09569)**_\n\n_**by Rishit Dagli and Ali Mustufa Shaikh.**_\n\nSome features of this dataset are:\n- high quality images and annotations (~4.6 bounding boxes per image)\n- real-life images unlike any current such dataset\n- majority of non-iconic images (allowing easy deployment to real-world environments)\n- \\\u003e15 pre-trained models in the model zoo availaible to directly use (also for mobile and edge devices)\n\n![](media/annotation_type.png)\n\n## Updates\n\n- 06/01/2022 - Many thanks to [@mariosasko](https://github.com/mariosasko) for creating a [Hugging Face Datasets loader](https://huggingface.co/datasets/cppe-5).\n- 05/01/2022 - This paper got featured on Google Research TRC's [publication section](https://sites.research.google/trc/publications/)\n- 20/12/2021 - First public release of the CPPE - 5 dataset on arXiv\n\n## Get the data\n\nWe strongly recommend you use either the downlaoder script or the Python package\nto download the dataset however you could also download and extract it manually.\n\n| Name | Size | Drive | wget Link | MD5 checksum |\n|:----:|:----:|:----:|:----:|:------------:|\n| `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` |\n\n### Downloader Script\n\nThe easiest way to download the dataset is to use the downloader script:\n\n```bash\ngit clone https://github.com/Rishit-dagli/CPPE-Dataset.git\ncd CPPE-Dataset\nbash tools/download_data.sh\n```\n\n### Python package\n\nYou can also use the Python package to get the dataset:\n\n```bash\npip install cppe5\n```\n\n```python\nimport cppe5\ncppe5.download_data()\n```\n\n## Data Loaders\n\nWe provide PyTorch and TensorFlow data loaders in this repository, the\ndataset can also be loaded from [Hugging Face Datasets](https://github.com/huggingface/datasets). To use the data\nloaders in this repository you would need to install the Python package\nfirst:\n\n```bash\npip install cppe5\n```\n\n### Hugging Face Datasets\n\nInstall the datasets library first:\n\n```bash\npip install datasets\n```\n\n```py\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"cppe-5\")\n```\n\n### PyTorch `DataLoader`\n\nA ready to run Google Colab example can be found at [notebooks/pytorch_loader.ipynb](notebooks/pytorch_loader.ipynb).\n\n```py\nimport cppe5\nfrom cppe5.torch import data_loader\nimport os\n\ncppe5.download_data()\nos.chdir(\"..\")\ndata_loader = cppe5.torch.data_loader() # torch.utils.data.DataLoader\n\n# Fetch all images and annotations\ndevice = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\n# DataLoader is iterable over Dataset\nfor imgs, annotations in data_loader:\n    imgs = list(img.to(device) for img in imgs)\n    annotations = [{k: v.to(device) for k, v in t.items()} for t in annotations]\n```\n\n### TensorFlow Loader\n\nA ready to run Google Colab example can be found at [notebooks/tensorflow_loader.ipynb](notebooks/tensorflow_loader.ipynb).\n\n```py\nimport cppe5\nfrom cppe5.tensorflow import data_loader\n\ncppe5.download_tfrecords()\nos.chdir(\"..\")\n\ndataset = cppe5.tensorflow.data_loader() # tf.data.Dataset\niter(dataset).next()\n```\n\n## Labels\n\nThe dataset contains the following labels:\n\n| Label | Description |\n|:----:|:-------------|\n| 1 | Coverall |\n| 2 | Face_Shield |\n| 3 | Gloves |\n| 4 | Goggles |\n| 5 | Mask |\n\n## Model Zoo\n\nMore information about the pre-trained models (like modlel complexity or FPS benchmark) could be found in [MODEL_ZOO.md](MODEL_ZOO.md)\nand [LITE_MODEL_ZOO.md](LITE_MODEL_ZOO.md) includes models ready for deployment\non mobile and edge devices.\n\n### Baseline Models\n\nThis section contains the baseline models that are trained on the CPPE-5 dataset\n. More information about how these are trained could be found in the original\npaper and the config files.\n\n|   Method    | AP\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003e50\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003e75\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003eS\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003eM\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003eL\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | Configs | TensorBoard.dev | PyTorch model | TensorFlow model |\n|:-----------:|:--------------------------:|:---------------------------------------:|:---------------------------------------:|:----------------------------------------:|:----------------------------------------:|:----------------------------------------:|:-------:|:------:|:-------:|:------:|\n|     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) |\n|    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) |\n| 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) |\n\n### SoTA Models\n\nThis section contains the SoTA models that are trained on the CPPE-5 dataset\n. More information about how these are trained could be found in the original\npaper and the config files.\n\n|           Method           | AP\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003e50\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003e75\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003eS\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003eM\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | AP\u003csub\u003eL\u003c/sub\u003e\u003csup\u003ebox\u003c/sup\u003e | Configs | TensorBoard.dev                                                      | PyTorch model                                                                                                                                  | TensorFlow model                                                                               |\n|:--------------------------:|:----------:|:-----------------:|:-----------------:|:----------------:|:----------------:|:----------------:|:------------:|:----------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------:|\n|         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)                      |                                                -                                               |\n|        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) |                                                -                                               |\n|            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)             |\n|         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)                 |                                                -                                               |\n|      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)                |                                                -                                               |\n|            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)             |\n| 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)                                   |                                                -                                               |\n|        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)                  |                                                -                                               |\n|           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)            |\n|        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)                        |                                                -                                               |\n|            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)                             |                                                -                                               |\n|     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) |                                                -                                               |\n|         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) |\n\n## Tools\n\nWe also include the following tools in this repository to make working with the dataset\na lot easier:\n\n- Download data\n- Download TF Record files\n- Convert PNG images in dataset to JPG Images\n- Converting Pascal VOC to COCO format\n- Update dataset to use relative paths\n\nMore information about each tool can be found in the\n[tools/README.md](tools/README.md) file.\n\n## Tutorials\n\nWe also present some tutorials on how to use the dataset in this repository as\nColab notebooks:\n\n- [pytorch_loader.ipynb](notebooks/pytorch_loader.ipynb) \u003ca href=\"https://colab.research.google.com/github/Rishit-dagli/CPPE-Dataset/blob/main/notebooks/pytorch_loader.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n\nIn  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.\n\n- [tensorflow_loader.ipynb](notebooks/tensorflow_loader.ipynb) \u003ca href=\"https://colab.research.google.com/github/Rishit-dagli/CPPE-Dataset/blob/main/notebooks/tensorflow_loader.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n\nIn this notebook we will load the CPPE - 5 dataset through TF Record files in TensorFlow.\n\n- [visualize.ipynb](notebooks/visualize.ipynb) \u003ca href=\"https://colab.research.google.com/github/Rishit-dagli/CPPE-Dataset/blob/main/notebooks/visualize.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n\nIn this notebook, we will visualize the CPPE-5 dataset, which could be really helpful to see some sample images and annotations from the dataset.\n\n## Citation\n\nIf you use this work, please cite the following paper:\n\n**BibTeX:**\n\n```bibtex\n@misc{dagli2021cppe5,\n      title={CPPE-5: Medical Personal Protective Equipment Dataset}, \n      author={Rishit Dagli and Ali Mustufa Shaikh},\n      year={2021},\n      eprint={2112.09569},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV}\n}\n```\n\n**MLA:**\n\n```\nDagli, 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.\n```\n\n## Acknoweldgements\n\nThe 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.\n\n## Want to Contribute 🙋‍♂️?\n\nAwesome! 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.\n\n## Want to discuss? 💬\n\nHave 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).\n\nHave 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).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishit-dagli%2Fcppe-dataset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frishit-dagli%2Fcppe-dataset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishit-dagli%2Fcppe-dataset/lists"}