035737_f3ac6323 · completed_with_weak_training
Automatic weak mappings. Defect names follow CSV row numbers. Poor fits and rejected annotations stay visible; only eligible datasets enter experimental training. Mapping evidence and eligibility JSON
| Dataset | Median fit (assumed mm) | Annotations | Weak nodes | Eligible | PNGs |
|---|---|---|---|---|---|
| AC28_DT_FRT_UPR_LHD_20260909_15_15__NG_a22f7d301de4 | 72.07 | 1 accepted / 2 rejected | 0 | False | Overlap · CAD labels |
| AC28_DT_RR_UPR_STD_1_20260827_18_01__NG_7194c0e36c7b | 3.90 | 0 accepted / 1 rejected | 0 | False | Overlap · CAD labels |
| AC28_DT_RR_UPR_STD_2_20260827_18_01__NG_8df93616f7ea | 2.10 | 1 accepted / 0 rejected | 222 | True | Overlap · CAD labels |
| AC28_DT_RR_UPR_STD_20260827_16__NG_008adde420f0 | 4.51 | 1 accepted / 0 rejected | 129 | True | Overlap · CAD labels |
| AC28_DT_RR_UPR_STD_20260828_09_21__NG_f4c68f38e467 | 7.60 | 1 accepted / 0 rejected | 130 | True | Overlap · CAD labels |
| LT2_DT_FRT_AREST2_20260914_15_10__NG_243895937535 | 14.30 | 1 accepted / 1 rejected | 0 | False | Overlap · CAD labels |
| ME1a_DT_FRT_UPR_LHD_20260608_14_06__NG_ffc4fce5d996 | 14.03 | 6 accepted / 0 rejected | 0 | False | Overlap · CAD labels |
| MX5a_PE_DT_RR_AREST_1_20260626_13_08__NG_fe0e1b9b8b53 | 22.12 | 4 accepted / 0 rejected | 0 | False | Overlap · CAD labels |
| MX5a_PE_DT_RR_AREST_2_20260626_13_08__NG_41ec5d7a218e | 17.99 | 3 accepted / 1 rejected | 0 | False | Overlap · CAD labels |
| NX5_DT_RR_UPR-2_CUR_1_20260916_10_40__NG_1f98f526a60c | 7.74 | 3 accepted / 1 rejected | 0 | False | Overlap · CAD labels |
| NX5_DT_RR_UPR-2_CUR_1_20260916_10_53__NG_b3a8f50d89ae | 7.38 | 2 accepted / 1 rejected | 0 | False | Overlap · CAD labels |
| NX5_DT_RR_UPR-2_CUR_1_20260916_11_40__NG_1b598f48d4e1 | 6.40 | 2 accepted / 0 rejected | 93 | True | Overlap · CAD labels |
| NX5_DT_RR_UPR-2_CUR_2_20260916_10_40__NG_4debd31ebf8a | 6.25 | 2 accepted / 2 rejected | 0 | False | Overlap · CAD labels |
| NX5_DT_RR_UPR-2_CUR_2_20260916_10_53__NG_a9e70207f7eb | 6.95 | 1 accepted / 1 rejected | 0 | False | Overlap · CAD labels |
# Training diagnosis: 20261020 / 035737_f3ac6323 State: **completed_with_weak_training**. Trained predictions: 140; scored records: 0; folds with epoch losses: 9. Reviewed dataset labels: 0. - gaussian_process: requires_training_labels (14 records): GP requires a fitted training posterior; no random/synthetic prior score emitted 1. Review CAD-node risk labels against the exact geometry_sha256 and node order; use each dataset's review_template.json. 2. CSV X/Y/W/H are image pixels. Supply a reviewed pixel-to-CAD mapping before treating them as CAD labels. 3. Run main.py --prepared-manifest <saved prepared_manifest.json> --labels-dir <reviewed-label-folder> to retain the exact labeled geometry. 4. sparse_unet requires MinkowskiEngine. Choose available models with --models if that optional backend is absent.






















































