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A wildlife conservation unmanned aerial vehicle (UAV) dataset derived from Mao et al.'s dataset WAID: Wildlife Aerial Images from Drone as introduced via their publication: "WAID: A Large-Scale Dataset for Wildlife Detection with Drones".
This subset was trimmed down to 14,000 images while the size of the test set was enhanced to accomodate for 2,000 images instead of the original 1,437 and the training and validation sets were combined to be able to generate k splits of the dataset for validation purposes. The dataset also includes 3 pre-done splits. These splits were created using a k-fold cross-validation approach with a focus on class balance, ensuring that each fold maintains a representative distribution of classes. It creates training and validation splits while calculating and saving label distributions to preserve class balance across all folds.
Note that the labels are in YOLO format, meaning the label fiels can be read as: class index, x_center, y_center, width, height.
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