[Submitted on 8 Oct 2026]
Title:WoodBR-Cross: A public wood-image dataset and individual-tree-disjoint benchmark for species identification
View a PDF of the paper titled WoodBR-Cross: A public wood-image dataset and individual-tree-disjoint benchmark for species identification, by Daniel Casanova and 4 other authors
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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 distinct trees identified by numeric catalogue witness codes (codigos-testemunho) in the Brazilian Forest Service collection. A species-level subset comprises 3,406 images, 20 classes and 214 trees. Three tasks compare duplicate-aware image partitions for all classes (A), image partitions for the species subset (B1), and individual-tree-disjoint partitions for that subset (B2). In B2, all images from each tree are kept together, so no tree is shared across training, validation or test within a fold. ResNet-50, ConvNeXt-Tiny and ViT-B/16 were evaluated with three outer folds, internal validation and five training seeds. Handcrafted and frozen-feature baselines complement border-only and randomized-label controls. Validation-selected ConvNeXt-Tiny achieved B2 tree-level macro-F1 of 0.8621 +/- 0.0317; frozen DINOv2 features yielded 0.9000 +/- 0.0722. Values are means and standard deviations across three seed-averaged fold means. Results estimate performance on previously unseen trees from this collection.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.12676 [cs.CV]
(or arXiv:2610.12676v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.12676
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Daniel Casanova [view email] [v1] Thu, 8 Oct 2026 19:57:10 UTC (3,834 KB)
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