Annotations for training dataset of images of birds in flight labelled with upstroke and downstroke

Annotations for a training dataset for a model that can classify between images of birds flying in upstroke and birds flying in downstroke. The corresponding image dataset is formed from 8,699 images of birds in flight, manually selected from the North American Birds dataset (NABirds) and the 2019 iNaturalist dataset. We manually annotated each bird within the selected images from these datasets (some images had more than one bird) as being either in upstroke or downstroke to produce a new labelled dataset. There are 5,754 downstrokes and 4,421 upstrokes. As we are unable to distribute the images these annotations correspond to, please find them through the links in Related works. The filenames of the annotation files will correspond to the filenames of the original images. Labelling was achieved using LabelImg and model training was done through the Python detecto module. To use these labels through detecto, follow the README at the linked GitHub.