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New WoodBR-Cross dataset improves AI wood species identification accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced WoodBR-Cross, a new dataset designed to improve the accuracy of wood species identification in images. This dataset contains over 4,800 images across 38 species, crucially linking images to individual trees to prevent optimistic performance estimates that can arise from training and testing on images from the same tree. Evaluations of models like ResNet-50, ConvNeXt-Tiny, and ViT-B/16 on this benchmark show that performance can decrease when models are trained and tested on data that is disjoint at the tree level, highlighting the dataset's utility for more realistic evaluations. AI

IMPACT This dataset aims to provide more realistic benchmarks for computer vision models in species identification, potentially improving their real-world applicability.

RANK_REASON The cluster contains a new academic paper introducing a dataset and benchmark for computer vision tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New WoodBR-Cross dataset improves AI wood species identification accuracy

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The cluster contains a new academic paper introducing a dataset and benchmark for computer vision tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Daniel Casanova, Alexandre Bahia Gontijo, Arnaldo Candido Junior, Silvana Nisgoski, Pedro Luiz de Paula Filho ·

    WoodBR-Cross: A public wood-image dataset and individual-tree-disjoint benchmark for species identification

    arXiv:2610.12676v1 Announce Type: new Abstract: Wood-image benchmarks can yield optimistic estimates when images from the same tree occur in both training and test sets. WoodBR-Cross contains 4,875 transverse-surface images in 38 operational taxonomic classes, linked to 303 disti…

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