← 研踪 PaperTrailarXiv:2610.12676 · v2
WoodBR-Cross: A public wood-image dataset and individual-tree-disjoint benchmark for species identification
首次提交 2026-10-09 03:57
版本更新 2026-11-03 22:07
雷达观测 2026-11-04 23:39
摘要
Wood identification supports timber inspection and species conservation. However, benchmarks can overestimate generalization when photographs of the same tree occur in training and test sets. We present WoodBR-Cross: 4,875 transverse-surface microscope images, 38 operational taxonomic classes and 303 catalogue-linked trees. A species subset contains 3,406 images, 20 classes and 214 trees. Task A evaluates all classes using duplicate-aware image partitions. B1 partitions images of the species subset, allowing different photographs of one tree across partitions. B2 keeps each tree's photographs together, testing recognition of unseen trees on the same subset. We benchmark three backbones and baselines across three folds and five training seeds. Validation-selected ConvNeXt-Tiny achieves B2 tree-level macro-F1 of 0.8621 +/- 0.0317; frozen DINOv2 features yield 0.9000 +/- 0.0722. Values are means +/- SD across three folds; fine-tuned scores are seed-averaged within folds. This resource supports reproducible, group-aware evaluation.
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v2 · 2026-11-03 22:07 · 最新观测版本
WoodBR-Cross: A public wood-image dataset and individual-tree-disjoint benchmark for species identification
Wood identification supports timber inspection and species conservation. However, benchmarks can overestimate generalization when photographs of the same tree occur in training and test sets. We present WoodBR-Cross: 4,875 transverse-surface microscope images, 38 operational taxonomic classes and 303 catalogue-linked trees. A species subset contains 3,406 images, 20 classes and 214 trees. Task A evaluates all classes using duplicate-aware image partitions. B1 partitions images of the species subset, allowing different photographs of one tree across partitions. B2 keeps each tree's photographs together, testing recognition of unseen trees on the same subset. We benchmark three backbones and baselines across three folds and five training seeds. Validation-selected ConvNeXt-Tiny achieves B2 tree-level macro-F1 of 0.8621 +/- 0.0317; frozen DINOv2 features yield 0.9000 +/- 0.0722. Values are means +/- SD across three folds; fine-tuned scores are seed-averaged within folds. This resource supports reproducible, group-aware evaluation.
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