# MODEL ZOO IMPLEMENTATION KNOWLEDGE BASE

## OVERVIEW
This directory is the algorithm zoo. Each file usually contributes one `BaseModel` subclass selected by `model_cfg.model`.

## WHERE TO LOOK
| Task | Location | Notes |
|------|----------|-------|
| Baseline pattern | `baseline.py` | minimal template for silhouette models |
| Scoliosis pipeline | `sconet.py` | label remapping + screening-specific head |
| Large-model fusion | `BiggerGait_DINOv2.py`, `BigGait.py` | external pretrained dependencies |
| Diffusion/noise handling | `denoisinggait.py`, `diffgait_utils/` | high-complexity flow/feature fusion |
| Skeleton variants | `skeletongait++.py`, `gaitgraph1.py`, `gaitgraph2.py` | pose-map/graph assumptions |

## CONVENTIONS
- Most models follow: preprocess input -> backbone -> temporal pooling -> horizontal pooling -> neck/head -> contract dict.
- Input modality assumptions differ by model (silhouette / RGB / pose / multimodal); config and preprocess script must match.
- Many models rely on utilities from `modeling/modules.py`; shared changes there are high blast-radius.

## ANTI-PATTERNS
- Don’t mix modality assumptions silently (e.g., pose tensor layout vs silhouette layout).
- Don’t rename classes without updating `model_cfg.model` references in configs.
- Don’t treat `BigGait_utils`/`diffgait_utils` as generic utilities; they are model-family specific.