24 lines
1.4 KiB
Markdown
24 lines
1.4 KiB
Markdown
# MODEL ZOO IMPLEMENTATION KNOWLEDGE BASE
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## OVERVIEW
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This directory is the algorithm zoo. Each file usually contributes one `BaseModel` subclass selected by `model_cfg.model`.
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## WHERE TO LOOK
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| Task | Location | Notes |
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|------|----------|-------|
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| Baseline pattern | `baseline.py` | minimal template for silhouette models |
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| Scoliosis pipeline | `sconet.py` | label remapping + screening-specific head |
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| Large-model fusion | `BiggerGait_DINOv2.py`, `BigGait.py` | external pretrained dependencies |
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| Diffusion/noise handling | `denoisinggait.py`, `diffgait_utils/` | high-complexity flow/feature fusion |
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| Skeleton variants | `skeletongait++.py`, `gaitgraph1.py`, `gaitgraph2.py` | pose-map/graph assumptions |
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## CONVENTIONS
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- Most models follow: preprocess input -> backbone -> temporal pooling -> horizontal pooling -> neck/head -> contract dict.
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- Input modality assumptions differ by model (silhouette / RGB / pose / multimodal); config and preprocess script must match.
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- Many models rely on utilities from `modeling/modules.py`; shared changes there are high blast-radius.
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## ANTI-PATTERNS
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- Don’t mix modality assumptions silently (e.g., pose tensor layout vs silhouette layout).
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- Don’t rename classes without updating `model_cfg.model` references in configs.
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- Don’t treat `BigGait_utils`/`diffgait_utils` as generic utilities; they are model-family specific.
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