{ "schema_version": 1, "study": "lr_capacity_search", "method": "adaptformer", "protocol": "frame_block_cv_search", "model_seed": 42, "split_seed": 42, "init_from_sha256": "3094f103c17bd13558f960c22b91ed3316679cadeab88ba269d862019c4dd58a", "fold_subset": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ], "smoke": false, "search_space": { "lr_candidates": [ 0.0001, 0.0003, 0.001 ], "capacity_axis": "bottleneck", "reference_capacity": { "bottleneck": 64 }, "capacity_candidates": [ 1, 16, 32, 64 ] }, "lr_selection": { "axis": "lr", "candidates": [ 0.0001, 0.0003, 0.001 ], "held_at": { "bottleneck": 64, "reduction_factor": 12 }, "fold_subset": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ], "mean_accuracy_by_candidate": [ { "lr": 0.0001, "config_id": "adaptformer__lr1.0e-04__bottleneck0064", "mean_accuracy": 0.958181818181818 }, { "lr": 0.0003, "config_id": "adaptformer__lr3.0e-04__bottleneck0064", "mean_accuracy": 0.9727272727272727 }, { "lr": 0.001, "config_id": "adaptformer__lr1.0e-03__bottleneck0064", "mean_accuracy": 0.9845454545454546 } ], "highest_mean_accuracy": 0.9845454545454546, "tied_highest": [ 0.001 ], "tie_rule": "highest ten-fold mean held-out accuracy, then the numerically lowest learning rate on an exact tie. Accuracy alone: no UAR, no weighted F1, no loss and no tolerance band enters it", "selected": 0.001 }, "capacity_selection": { "axis": "bottleneck", "candidates": [ 1, 16, 32, 64 ], "at_lr": 0.001, "fold_subset": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ], "mean_accuracy_by_candidate": [ { "bottleneck": 1, "config_id": "adaptformer__lr1.0e-03__bottleneck0001", "mean_accuracy": 0.9654545454545455, "trainable_params": 32274, "reduction_factor": 768 }, { "bottleneck": 16, "config_id": "adaptformer__lr1.0e-03__bottleneck0016", "mean_accuracy": 0.9818181818181818, "trainable_params": 308934, "reduction_factor": 48 }, { "bottleneck": 32, "config_id": "adaptformer__lr1.0e-03__bottleneck0032", "mean_accuracy": 0.9736363636363636, "trainable_params": 604038, "reduction_factor": 24 }, { "bottleneck": 64, "config_id": "adaptformer__lr1.0e-03__bottleneck0064", "mean_accuracy": 0.9845454545454546, "trainable_params": 1194246, "reduction_factor": 12 } ], "highest_mean_accuracy": 0.9845454545454546, "tied_highest": [ 64 ], "tie_rule": "highest ten-fold mean held-out accuracy, then fewer trainable parameters, then the lexicographically smallest configuration id. Accuracy alone decides first: no UAR, no weighted F1, no loss and no tolerance band enters it", "selected": 64 }, "selected": { "config_id": "adaptformer__lr1.0e-03__bottleneck0064", "values": { "bottleneck": 64, "lr": 0.001, "reduction_factor": 12 }, "trainable_params": 1194246, "mean_accuracy": 0.9845454545454546, "cell_dir": "outputs/search__adaptformer__src-ferplus__seed42/cells/adaptformer__lr1.0e-03__bottleneck0064" }, "optimism": "the configuration is selected on the same ten held-out folds whose mean accuracy is then reported, so the comparison is optimistically biased by hyperparameter selection on top of the best-epoch-on-the-held-out-block optimism every cell already carries. It is not nested cross-validation, not independent validation, not an unbiased estimate and not a like-for-like comparison with the published figures", "design": "a sequential hyperparameter study: learning-rate selection, then capacity selection at the selected rate. The best-observed configuration of each strategy is the configuration that enters the five-method comparison", "comparison_eligible": true, "comparison_eligibility": "selected over all ten folds" }