数据集获取

git clone https://github.com/lindawangg/COVID-Net.git

curl -L -o repo.zip https://github.com/lindawangg/COVID-Net/archive/refs/heads/master.zip
unzip repo.zip

源站 README 摘录（使用方式）

COVID-Net Open Source Initiative

Note: The COVID-Net models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available. They are currently at a research stage and not yet intended as production-ready models (not meant for direct clinical diagnosis), and we are working continuously to improve them as new data becomes available. Please do not use COVID-Net for self-diagnosis and seek help from your local health authorities.
Recording to webinar on How we built COVID-Net in 7 days with Gensynth
Update 06/02/2022: We released COVIDx CXR-3, a cleaned version of the dataset in which several hundred bad training images have been removed. The new dataset contains 29,986 images from 16,648 patients.
Update 11/28/2021: We released a new training dataset with over 30,000 CXR images from a multinational cohort of over 16,400 patients. The dataset contains 16,490 positive COVID-19 images from over 2,800 patients. The COVIDx V9A dataset is for detection of no pneumonia/non-COVID-19 pneumonia/COVID-19 pneumonia, and COVIDx V9B dataset is for COVID-19 positive/negative detection.
Update 10/19/2021: We released a new COVID-Net CXR-3 model for COVID-19 positive/negative detection which was trained and tested on the COVIDx8B dataset leveraging the new MEDUSA (Multi-scale Encoder-Decoder Self-Attention) architecture.
Update 04/21/2021: We released a new COVIDNet CXR-S model and COVIDxSev dataset for airspace severity grading in COVID-19 positive patient CXR images. For more information on training, testing and inference please refer to severity docs.
Update 03/20/2021: We released a new COVID-Net CXR-2 model for COVID-19 positive/negative detection which was trained on the new COVIDx8B dataset with 16,352 CXR images from a multinational cohort of 15,346 patients from at least 51 countries. The test results are based on the new COVIDx8B test set of 200 COVID-19 positive and 200 negative CXR images.
Update 03/19/2021: We released updated datasets and dataset curation scripts. The COVIDx V8A dataset and create_COVIDx.ipynb are for detection of no pneumonia/non-COVID-19 pneumon

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

完整仓库：github.com/lindawangg/COVID-Net