A Result-Auditable Protocol for Low-Data Classification of Blue-and-White Porcelain Botanical Motifs Using Frozen Chinese-CLIP Features

Low-data visual classification is common in cultural heritage documentation, where expert labels are expensive, and image sources are heterogeneous. This protocol provides result-level audit support for classifying five botanical motifs-peony, lotus, chrysanthemum, plum blossom, and bamboo-on blue-and-white porcelain. It combines operational label definitions, source documentation, perceptual hash screening, and fixed, stratified, image-level splits with a frozen Chinese Contrastive Language-Image Pre-training (Chinese-CLIP) Vision Transformer (ViT-L/14-336) visual encoder. A training-only StandardScaler and an L2-regularized multinomial logistic regression classifier form the primary model. In a 259-image demonstration, five overlapping image-level splits yielded 91.28% ± 2.47% accuracy and 91.22% ± 2.44% macro-F1. These are image-level demonstration estimates and may be optimistic because complete artifact identifiers are unavailable; they should not be interpreted as artifact-level, population-level, or confirmatory performance. A retrospective sensitivity analysis that excluded training images linked to test images at a perceptual hash distance ≤15 yielded 91.03% ± 2.03% accuracy. The protocol also specifies few-shot evaluation, metadata controls, feature baselines, prompt sensitivity, and checksum-based archiving. An exploratory assessment of all 60 external images yielded 85.00% accuracy and 85.89% macro-F1 against provisional labels, and 83.33% accuracy and 84.31% macro-F1 against labels from one blinded professor. Six label disagreements remain unadjudicated. A 54-image agreement subset is secondary only. Because the external v2 collection included replacements after model errors were inspected, none of these external results constitutes independent confirmatory validation. The workflow supports expert-reviewed candidate labeling and retrieval, not autonomous attribution, dating, or authentication.