ABSTRACTAnomaly detection is a crucial task in computer vision, with applications ranging from quality control to security monitoring, among many others. Recent technological advancements have enabled near‐perfect solutions on benchmark datasets like MVTec, raising the need for novel datasets that pose new challenges for this modelling task. This work presents a novel Wood Anomaly Detection (WoodAD) dataset, which includes defects in wooden pieces that result in challenges for the most advanced techniques applied to other established datasets. This article evaluates such challenges posed by WoodAD with one‐class and few‐shot supervised learning approaches. Our experiments herein reveal that EfficientAD, a state‐of‐the‐art method previously excelling on the MVTec dataset, outperforms all other one‐class learning approaches. Nevertheless, there is room for improvement, as EfficientAD achieves a 0.535 pixel/segmentation average precision (AP) over the complete test set. UNet, a well‐known pixel‐level classification architecture, leveraged few‐shot supervised learning to enhance the pixel AP score, achieving 0.862 pixel/segmentation AP over the entire test set. Our WoodAD dataset represents a valuable contribution to the field of anomaly detection, offering complex image textures and challenging defects. Researchers and practitioners are encouraged to leverage this dataset to push the boundaries of anomaly detection and develop more robust and effective solutions for more complex real‐world applications. The WoodAD dataset has been made publicly available in Kaggle (https://www.kaggle.com/datasets/itiresearch/wood‐anomaly‐detection‐one‐class‐classification).