| # Fine-tuning and evaluation |
|
|
| The final decoder uses metric local refinement (`metric_local_refinement`), with 128 local channels, |
| two blocks, and at most 16 neighbors. Both schedules run up to 120 epochs, |
| including ten frozen-encoder epochs, followed by cosine fine-tuning. |
| The QL1 and QL2 trainers preserve their respective research protocols. |
|
|
| Copy `configs/ql*_segmentation.yaml`, edit data paths and choose a new |
| output directory. Download the paired encoder artifact before fine-tuning. |
| A complete segmentation checkpoint is sufficient for inference. |
|
|
| ```bash |
| python scripts/finetune_segmentation.py --config configs/ql2_segmentation.yaml |
| python scripts/evaluate_segmentation.py --config configs/ql2_segmentation_eval.yaml |
| ``` |
|
|
| The evaluation configs load released best segmentation checkpoints and use FP32. |
| When evaluating a new run, update `model.segmentation_checkpoint`. |
| QL1 includes the final common-support evaluation seed. mIoU uses five |
| unthresholded semantic classes with unmapped labels ignored. Confidence |
| thresholding belongs to exported products. QL2 evaluation is on the thinned |
| QL1 proxy. Crop visualization LAS files cannot reconstruct an original LAS |
| header or CRS from NumPy input. |
|
|
| Record any change to support sampling, normalization, labels, physical radii, |
| checkpoint, or split as a new experiment. Matching architecture alone does |
| not reproduce scores: original splits and support selection also matter. |
|
|
