| # Evaluation protocol | |
| Dataset 1 contains 340 held-out test images. Dataset 2 contains 1,000 held-out test images. The datasets are trained and evaluated separately. | |
| COCO box AP is averaged over IoU thresholds 0.50–0.95 in increments of 0.05, with maxDets = 100. Predictions from successive refinement calls are pooled before classwise NMS. Each configuration retains its tested resize and padding transforms. | |
| The default KaryoFlow configuration uses 500 proposals, four refinement calls and six cascade stages. H3 uses three stages per call. LQCR combines class probability p and predicted quality q as p × q². Its parent detector is frozen during quality-branch training. | |
| The model list records individual runs rather than representing a three-run mean with a single weight file. RF, RF+LQCR and H3 results use training seeds 42, 123 and 789. Dataset 1 comparators also use three independent training runs. Dataset 2 comparator repeats vary inference seeds 42, 123 and 789 while keeping the checkpoint fixed. | |
| The ranking intervention in Fig. 4 compares p and p × q² within the same quality-augmented checkpoints. These paired values should not be substituted with a separately evaluated parent checkpoint, even when their means round to the same value. | |
| Latency was measured on an RTX A6000 with batch size 1, 20 warm-up iterations and 500 timed iterations per repeat, repeated three times. Timing includes model preprocessing and postprocessing for a preloaded test batch. It excludes disk image loading. Reported standard deviations describe timing repeats, not training runs. | |
| `paper_tables.csv` retains the manuscript's table cells, including LaTeX notation for uncertainties. The other CSV files provide numeric columns suitable for analysis. Figure-specific measurements are stored in `figures/data/`; they include validation sweeps, paired AP changes, scale analysis and recorded candidate boxes. | |
| ## Solver comparison | |
| Euler, Heun and the RF multistep solver are evaluated at one to four steps with three independent training seeds on each dataset. All solvers use the shifted reverse-time grid. At four steps, the maximum difference between solver mean mAP values is below 0.0001 on both datasets. Euler and the multistep rule use four detector calls; Heun uses seven. | |
| LQCR is evaluated with ranking disabled and enabled in each quality-augmented checkpoint. The quality exponent is selected on validation images. The published accuracy, timing and figure records use the corresponding configurations and checkpoints listed in this repository. | |
| Run the solver checks with `python -m pytest tests/test_rf_solver_grid.py`. | |