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End of preview. Expand in Data Studio
Vineyard Grape Trunk Detection
Images of vineyards in the Douro region of Portugal, taken by several ground-level cameras in varied illumination, with bounding boxes for vine trunks and for grape bunches at two growth stages (tiny, after bloom, and medium). The dataset accompanies a study on detecting grape bunches with quantized deep-learning models on low-power hardware; the current Zenodo version also adds trunk annotations. The raw config holds the 2,846 unaugmented RGB images; the augmented config holds 18,515 augmented copies (rotation, flipping, hue/saturation shifts, noise, scaling and translation), with the augmentation named per image. Each image records its capture set, and the record's thermal images are left out. The dataset contains raw and augmented versions. The raw split contains 2,846 images with 13,218 bounding box annotations across 3 categories.
The augmented split contains 18,515 images with 85,722 bounding box annotations across 3 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{aguiar2021grape,
title={Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models},
author={Aguiar, André Silva and Magalhães, Sandro Augusto and dos Santos, Filipe Neves and Castro, Luis and Pinho, Tatiana and Valente, João and Martins, Rui and Boaventura-Cunha, José},
journal={Agronomy},
volume={11},
pages={1890},
year={2021},
publisher={MDPI}
}
The dataset itself can be cited as:
Aguiar, A. S., & Magalhães, S. (2021). Grape bunch and vine trunk dataset for Deep Learning object detection. [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.5139598
This dataset was reformatted from its original format to match HuggingFace standards.
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