COVID-Efficientnet → a Pytorch Upgrade of COVID-Next and COVID-Net

Disclaimer: A lot of this code is taken from https://github.com/velebit-ai/COVID-Next-Pytorch Inspired by COVID-Next and the efficiency and mobility of Efficientnet, we are now open sourcing the upgraded Pytorch implementation of both called COVID-Efficientnet.

COVID-Efficientnet features an architecture that builds upon Efficientnet b7 architecture, an AutoML architecture for optimizing both accuracy and mobility.

Requirements

To install all requirements, simply run pip3 install -r requirements.txt. Code was tested with Python 3.7.

Dataset

We have created a script that automates the dataset generation from the two sources referenced in the original repo. To generate the dataset, follow these steps:

1. Download the datasets listed below:
   • COVID ChestXray dataset. Be aware this repository is constantly adding new images.
   • Kaggle RSNA pneumonia dataset
2. Run the generate_dataset.py script. Run python3 generate_dataset.py -h to see supported arguments.

The script will create a new folder with train and test subfolders where images are located, along with the two metadata files for both train and test subsets.

Training

Training configuration is currently modified through the config.py module. Check it out before starting training.

python3 train.py command will run model training.

Results

The following results were obtained on the dataset used in the original repo as of May 19 2020.

Model	Accuracy
COVID-Net (Large)	91.90%
COVID-Next	94.76%
COVID-Efficientnet	96.01%

Pretrained Weights

Pretrained weights are available at weights

Minimal prediction example

You can find the minimal prediction example in minimal_prediction.py. The example demonstrates how to load the model and use it to predict the disease type on the image.