*_scalar_test_f1.pt: retained best checkpoint by macro-F1
*_scalar_test_accuracy.pt: retained best checkpoint by accuracy
train_config.yaml: training config for the finetune run
eval_best_f1_27000.yaml: standalone eval config for the retained best-F1 checkpoint
eval_best_accuracy_64000.yaml: standalone eval config for the retained best-accuracy checkpoint

Reproduce eval

Run from the repo root.

Best-F1 checkpoint:

CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  opengait/main.py \
  --cfgs artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_f1_27000.yaml \
  --phase test

Best-accuracy checkpoint:

CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  opengait/main.py \
  --cfgs artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_accuracy_64000.yaml \
  --phase test

Paths to change on another machine:

data_cfg.dataset_root
- point this to your local body-only Scoliosis1K skeleton dataset root
data_cfg.dataset_partition
- change this only if your repo or partition file lives elsewhere
evaluator_cfg.restore_hint
- change this only if you move the artifact/ folder or rename the checkpoint files
evaluator_cfg.output_root
- change this if you want eval outputs somewhere else

Verified results

These commands were rerun from the copied artifact/ configs on 2026-03-11.

Best-F1 checkpoint (27000):

Accuracy: 92.38%
Macro Precision: 90.30%
Macro Recall: 87.39%
Macro F1: 88.70%

Best-accuracy checkpoint (64000):

Accuracy: 94.25%
Macro Precision: 83.24%
Macro Recall: 95.76%
Macro F1: 87.63%