# Scoliosis ScoNet-MT-ske Artifacts

This directory archives the two retained best checkpoints from the practical
`1:1:2` skeleton-map training path:

- best by macro-F1
- best by accuracy

Model recipe:

- split: `Scoliosis1K_112.json`
- representation: `body-only` skeleton heatmap
- losses: plain CE + triplet
- finetune optimizer: `AdamW`
- scheduler: cosine annealing

Files:

- `*_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:

```bash
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:

```bash
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%`