Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance for selecting pretrained encoders. We study emerald inclusion grading with a custom dataset of labeled images across three classes and ask: under matched backbone capacity, how does pretraining objective affect downstream representation quality? We compare four frozen ViT-B/16 encoders trained with supervised classification, contrastive learning (SigLIP2), masked reconstruction (MAE), and self-distillation (DINOv3), and evaluate them with leave-one-out cross-validation using linear and nonlinear probes. To control statistical noise in the low-N regime, we use permutation testing (N=1000) on macro one-vs-rest AUC. Supervised and contrastive encoders provide the strongest linear separability (logistic AUC: 0.768 and 0.735; SVM AUC: 0.739 and 0.697), while MAE improves under nonlinear probes (XGBoost AUC: 0.713). We find that DINOv3 underperforms across probe families in this domain. These results support a practical recommendation for extreme low-data FGVC: prioritize margin-enforcing pretraining objectives when data scarcity restricts probing to linear decision rules, and consider reconstruction-style encoders when nonlinear classifiers are feasible given dataset constraints.

翻译：极低数据量下的细粒度分类常见于标注成本高昂的专家领域，但从业者仍需要原则性指导来选择预训练编码器。我们利用自定义的三类标注图像数据集研究祖母绿内含物分级，并探究以下问题：在匹配骨干网络容量的前提下，预训练目标如何影响下游表示质量？我们比较了使用监督分类、对比学习（SigLIP2）、掩码重建（MAE）和自蒸馏（DINOv3）训练的四种冻结ViT-B/16编码器，通过留一法交叉验证结合线性和非线性探针进行评估。为控制低样本量下的统计噪声，我们对宏观一对多AUC实施置换检验（N=1000）。监督式和对比式编码器表现出最强的线性可分性（逻辑回归AUC：0.768和0.735；SVM AUC：0.739和0.697），而MAE在非线性探针下表现提升（XGBoost AUC：0.713）。我们发现DINOv3在该领域各探针家族中表现均不佳。这些结果支持针对极低数据量细粒度分类的实用建议：当数据稀缺限制探针只能采用线性决策规则时，优先选择具有边界强化特性的预训练目标；若数据集条件允许使用非线性分类器，则可考虑基于重建范式的编码器。