On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers

[2608.14705] On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14705 (cs) [Submitted on 10 Aug 2026] Title: On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers Authors: Ljubomir Buturovic (East Palo Alto, United States) View a PDF of the paper titled On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers, by Ljubomir Buturovic (East Palo Alto and 1 other authors View PDF HTML (experimental) Abstract: Hyperparameter optimization (HPO) can materially affect the performance of deep learning (DL) image classifiers, but there is little empirical guidance on how to derive the validation signal that drives it, especially for the small sample sizes common in fields such as medical imaging. We compared three HPO protocols in terms of {\em absolute performance-estimation error} (AEE; the absolute difference between the winning configuration's validation AUROC and its test AUROC): fixed holdout (F), reshuffled holdout (R), and 5-fold cross-validation (C). The search space, sampler, training procedure, architecture, and test set were held identical across protocols. We evaluated the protocols on three public datasets spanning two regimes: binary medical imaging (RSNA pneumonia radiographs and binarized HAM10000 skin lesions) and 200-class natural imaging (Tiny ImageNet), across a range of development set sizes $n$ and two backbones (ResNet-18 on all datasets, Vision Transformer (ViT-S/16) on RSNA). On the medical datasets, every point estimate favored cross-validation over both holdout protocols, with reductions in AEE largest at small sample sizes and diminishing as $n$ increased. This pattern remained robust under conservative family-wise adjustment. On Tiny ImageNet, AEE was negligible under all three protocols. Test AUROC was generally similar among protocols. Fixed holdout had lower mean AEE than reshuffled holdout in 11 of 12 medical conditions, although this secondary finding was less uniformly supported. For small-sample medical image classification, we recommend cross-validation-based HPO when computational resources permit because it trades additional computation for a more reliable development-time estimate of subsequent test performance. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG) Cite as: arXiv:2608.14705 [cs.CV] (or arXiv:2608.14705v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14705 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ljubomir Buturovic [ view email ] [v1] Mon, 10 Aug 2026 22:44:42 UTC (379 KB) Full-text links: Access Paper: View a PDF of the paper titled On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers, by Ljubomir Buturovic (East Palo Alto and 1 other authors View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files ( details ) : README.md bham/dataset_info.json bham/split_manifest.csv bham_cv.sh bham_rf.sh build_bham.py crossed_inference.py cvdl.py cvdl_parser.py dl_tin.py family.py familywise_aee/checkpoints.csv familywise_aee/familywise_aee_inference.csv familywise_aee/summary.md main_logs.sh make_rsnap_splits.py results.csv results/bham1000cv.csv results/bham1000cv_best.csv results/bham1000rf.csv results/bham1000rf_best.csv results/bham100cv.csv results/bham100cv_best.csv results/bham100rf.csv results/bham100rf_best.csv results/bham3000cv.csv results/bham3000cv_best.csv results/bham3000rf.csv results/bham3000rf_best.csv results/bham300cv.csv results/bham300cv_best.csv results/bham300rf.csv results/bham300rf_best.csv results/r1000cv.csv results/r1000cv_best.csv results/r1000rf.csv results/r1000rf_best.csv results/r100cv.csv results/r100cv_best.csv results/r100rf.csv results/r100rf_best.csv results/r3000cv.csv results/r3000cv_best.csv results/r3000rf.csv results/r3000rf_best.csv results/r300cv.csv results/r300cv_best.csv results/r300rf.csv results/r300rf_best.csv results/rvit1000cv.csv results/rvit1000cv_best.csv results/rvit1000rf.csv results/rvit1000rf_best.csv results/rvit100cv.csv results/rvit100cv_best.csv results/rvit100rf.csv results/rvit100rf_best.csv results/rvit3000cv.csv results/rvit3000cv_best.csv results/rvit3000rf.csv results/rvit3000rf_best.csv results/rvit300cv.csv results/rvit300cv_best.csv results/rvit300rf.csv results/rvit300rf_best.csv results/tin10000cv.csv results/tin10000cv_best.csv results/tin10000rf.csv results/tin10000rf_best.csv results/tin2000cv.csv results/tin2000cv_best.csv results/tin2000rf.csv results/tin2000rf_best.csv results/tin5000cv.csv results/tin5000cv_best.csv results/tin5000rf.csv results/tin5000rf_best.csv results_abs.pdf results_best.csv results_full_inference.csv rsnap_cv.sh rsnap_image.py sus.py symmetric_rsnap_rf.sh symmetric_tin_rf.sh tin_cv.sh tin_sus.py tin_test.py tin_top1.csv wds.py (85 additional files not shown) Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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