EyePACS-light (v1) (EyePACS-AIROGS-light-v1)

This is a machine-learning-ready glaucoma dataset using a balanced subset of standardized fundus images from the Rotterdam EyePACS AIROGS train set. This dataset is split into training, validation, and test folders which contain 2500, 270, and 500 fundus images in each class respectively. Each training set has a folder for each class: referable glaucoma (RG) and non-referable glaucoma (NRG).

Three versions of the same dataset are available with different standardization strategies:

RAW - Resizing the source image to 256x256 pixels PAD - Padding the source image to a square image and then resizing it to 256x256 pixels. This method preserves the aspect ratio but the resultant image contains less usable information. CROP - Cropping black background in the fundus image, pad the resultant image to create a square image, and then resize to 256x256 pixels. This method preserves the aspect ratio and the resultant image contains the most usable information.