docs/eval.md

Evaluation

evaluate.py evaluates a fine-tuned checkpoint against labelled test images. Use inference.py instead for images without ground truth.

Segmentation

python evaluate.py \
  --config configs/aff_base_finetune_512_fpw.yaml \
  --checkpoint AFFMAE_BASE_FT_512

The paper reports mean Intersection over Union (mIoU) and the filtration-slits class IoU. Results are summarized as mean ± standard deviation across four random seeds.

FPW geometry

python evaluate.py \
  --config configs/aff_base_finetune_512_fpw.yaml \
  --checkpoint AFFMAE_BASE_FT_512 \
  --mode fpw --eval-grid-size 1024 \
  --out-json output/fpw.json

The geometry pass recovers connected PGBMI segments from the segmentation, forms a one-pixel-wide ordered centerline for each segment, detects filtration slits, and projects the slit locations onto that centerline. Arc length between successive slit locations represents foot-process width. Predicted and ground-truth segments are paired geometrically before their mean widths are compared. FPW MAE: the mean per-image absolute pixel error |FPW_pred - FPW_GT|, with distances scaled to a 1024×1024 reference grid.

python evaluate.py --config configs/aff_base_finetune_512_fpw.yaml \
  --mode fpw --seeds 42,77,2026,31415 \
  --checkpoint 'output/fpw_seed{seed}/last_model.pth'

Use --vis-dir output/fpw_geometry to render matched PGBMI centerlines and slit locations.