This project is part of a task for the college where I study, so task-parts contains files that associated with that task, whishing that I would get the full mark ;). In general the base code doesn't have any special parts except that folder.

After cloning the repository, install the required packages in a virtual environment.

Next, download the datasets and checkpoints, as describe below.

1. Download the Chen et al. labels and the chest X-rays in png format for IU X-Ray from:

https://openi.nlm.nih.gov

2. Place the files into dataset folder, such that their paths are dataset/reports and dataset/images.

This approach uses CheXNet, and DenseNet121 as a CNN Encoder model. By default the CheXNet pretrained weights are located in weights folder.

The model configurations for each task can be found in its config.py file.

Use the below command to train the model form a saved checkpoint or without a checkpoint.

python train.py

The model performance measure is based of the BLEU metric.

Feel free to change the performance measure metric in the check_accuracy method that is located in the eval.py file

Run the following command to calculate BLEU score.

python eval.py