OracleNet: a few-shot multi-scale deep learning framework for calibrated authentication of oracle bone artefacts

Oracle bone inscriptions, the earliest mature writing system in ancient China, require reliable artefact authentication for archaeological scholarship and museum curation. Traditional expert-based methods are subjective and difficult to reproduce at scale. We propose OracleNet, a few-shot multi-scale deep learning framework integrating ResNet-18, a Feature Pyramid Network, a Convolutional Block Attention Module, data augmentation, Focal Loss and Temperature Scaling calibration. Fivefold artefact-level cross-validation on 272 images yields a mean accuracy of 91.50% ± 4.20% and an ROC–AUC of 99.12% ± 0.80%. A positivity-constrained Temperature Scaling step reduces Expected Calibration Error from 0.269 to 0.054 (79.8% reduction) and Brier score by 52.6%. A symmetric calibrated comparison establishes post-hoc Temperature Scaling as a generalisable calibration tool under few-shot conditions. Sensitivity reaches 98.53%. The backbone-only variant outperforms the full model in raw accuracy on single-fold evaluation (McNemar p = 0.041), confirming that calibrated confidence is the primary contribution.