Trained YOLOv8 model and traning data accompanying the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline"
(2025)
- Author
- Jan Van den Bulcke (UGent) , Louis Verschuren (UGent) and Francis wyffels (UGent)
- Organization
- Project
-
- SmartWoodID: Smart classification of Congolese timbers: deep learning techniques for enforcing forest conservation
- ACcurate Temperature REconstructions and climate change mapping in tree rings of Ancient bristlecone pines, the Longest-living trees in the world [ACTREAL]
- An X-ray view on the intra-seasonal dynamics of carbon storage in trees [XINCAST]
- A game-changing perspective on intra-seasonal wood formation dynamics using high-resolution X-ray Computed Tomography to elucidate leaf senescence and autumn dynamics of temperate deciduous trees in Europe
- Abstract
- Trained YOLOv8 model for vessel and ray segmentation on (gigapixel) RGB TIFF images of beech. Training and annotation data in COCO format are included. This model is needed for running the source code of which releases can be found on https://doi.org/10.5281/zenodo.14637855. The full images of the increment cores can be found on https://doi.org/10.5281/zenodo.14627909. The full images of the disks (see paper for more details) can be found on https://doi.org/10.6019/S-BIAD1574. This is part of an entire sample preparation, imaging and analysis pipeline available in the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline" by Van den Bulcke and co-authors: https://doi.org/10.1186/s13007-025-01330-7. Cite our paper (when accepted) when using these data and/or model.
- Keywords
- wood anatomy, deep learning, optical imaging
- License
- CC-BY-4.0
- Access
- open access
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JKAHGNWVS0KB5S38QDCJW2DH
@misc{01JKAHGNWVS0KB5S38QDCJW2DH,
abstract = {{Trained YOLOv8 model for vessel and ray segmentation on (gigapixel) RGB TIFF images of beech. Training and annotation data in COCO format are included.
This model is needed for running the source code of which releases can be found on https://doi.org/10.5281/zenodo.14637855.
The full images of the increment cores can be found on https://doi.org/10.5281/zenodo.14627909.
The full images of the disks (see paper for more details) can be found on https://doi.org/10.6019/S-BIAD1574.
This is part of an entire sample preparation, imaging and analysis pipeline available in the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline" by Van den Bulcke and co-authors: https://doi.org/10.1186/s13007-025-01330-7.
Cite our paper (when accepted) when using these data and/or model.}},
author = {{Van den Bulcke, Jan and Verschuren, Louis and wyffels, Francis}},
keywords = {{wood anatomy,deep learning,optical imaging}},
publisher = {{Zenodo}},
title = {{Trained YOLOv8 model and traning data accompanying the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline"}},
url = {{http://doi.org/10.5281/ZENODO.14604995}},
year = {{2025}},
}
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