MinneApple is a benchmark dataset for apple detection and segmentation. The dataset annotates each object instance using polygonal masks to aid accurate object detection, localization, and segmentation. In addition, the dataset provides patch-based clustering of fruit counts. The dataset contains over 410,000 annotated object instances in 1,000 images.

Citation

@misc{hani2019minneapple,
title={MinneApple: A Benchmark Dataset for Apple Detection and Segmentation},
author={Nicolai Häni and Pravakar Roy and Volkan Isler}
year={2019},
eprint={1909.06441},
archivePrefix={arXiv},
primaryClass={cs.CV}
}