This project is part of a task for the college where I study, so
task-partscontains 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.
- Download the Chen et al. labels and the chest X-rays in png format for IU X-Ray from:
https://openi.nlm.nih.gov
- Place the files into
datasetfolder, such that their paths aredataset/reportsanddataset/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.pyThe model performance measure is based of the BLEU metric.
Feel free to change the performance measure metric in the
check_accuracymethod that is located in theeval.pyfile
Run the following command to calculate BLEU score.
python eval.py