5-Fold Cross-Validation Results — v2 Models

All 9 models were re-trained with the full v2 protocol (CLAHE, RandAugment, WeightedRandomSampler, MixUp+CutMix, warmup-cosine LR, AMP fp16) on 5 stratified-group folds (pHash-grouped pool, fixed held-out test set of 3,208 images). Results confirm the published headline metrics are robust.

Summary (sorted by test accuracy)

Model	Family	Test Acc	95% CI	Test F1	ROC-AUC	ECE	Folds
Inception-v3	CNN	90.18%	[89.24, 91.24]	92.54%	0.9930	0.0194	5
CLIP ViT-B/16	CLIP	90.15%	[89.18, 91.24]	92.83%	0.9944	0.0217	5
VGG-19	CNN	90.12%	[89.09, 91.12]	92.59%	0.9933	0.0228	5
ResNet-101	CNN	90.09%	[89.15, 91.12]	92.63%	0.9941	0.0243	5
DenseNet-121	CNN	89.65%	[88.62, 90.71]	92.29%	0.9937	0.0272	5
DINOv2-L	Foundation	89.61% ± 0.20	—	92.27% ± 0.17	—	—	5
ResNet-50	CNN	89.50%	[88.40, 90.59]	92.20%	0.9945	0.0339	5
Swin-B	Foundation	87.00% ± 0.26	—	90.26% ± 0.18	—	—	5
RETFound	Foundation	83.35% ± 0.23	—	87.27% ± 0.20	—	—	5

Files

kfold_v2_summary.csv — unified summary of all 9 models
cnn_clip/summary.json — test metrics for all 6 CNN/CLIP models
cnn_clip/<model>_kfold.json — per-fold val metrics (6 models × 5 folds)
cnn_clip/mcnemar.json — pairwise McNemar tests (all p > 0.05 → no significant difference between CNN/CLIP architectures)
foundation_kfold.json — aggregated mean ± std for 3 foundation models
foundation_fold{0-4}_summary.json — per-fold summaries

McNemar Pairwise Tests (CNN/CLIP)

All 15 pairwise McNemar p-values > 0.05. The models perform at statistical parity on this test set — architecture choice does not significantly alter prediction errors.

Protocol

CNN/CLIP: run_v2_experiments.py --folds 5 --epochs 100 --patience 12
Foundation: run_foundation_kfold.py --folds 5 --lp-epochs 20 --ft-epochs 15 --patience 8
Split: splits/holdout_split_augmented.json (StratifiedGroupKFold, seed=42, pHash threshold=8)
Test set: 3,208 images, fixed, never used during training or fold selection