Record 1:1:2 skeleton bridge findings

2026-03-10 14:43:08 +08:00
parent 37483fa628
commit 560f8e7aae
3 changed files with 27 additions and 2 deletions
@@ -66,12 +66,16 @@ The main findings so far are:
 - The `1:1:8` skeleton-map control trained with healthy optimization metrics but evaluated very poorly.
 - A recent `ScoNet-MT-ske`-style control on `Scoliosis1K_sigma_8.0/pkl` finished with `36.45%` accuracy and `32.78%` macro-F1.
 - That result is far below the paper's `1:1:8` ScoNet-MT range and far below the silhouette baseline behavior.
+- On the easier `1:1:2` split, the skeleton branch is clearly learnable:
+  - `body-only + weighted CE` reached `81.82%` accuracy and `65.96%` macro-F1 on the full test set at `7000`
+  - `body-only + plain CE` improved that to `83.16%` accuracy and `68.47%` macro-F1 at `7000`

 The current working conclusion is:

 - the core ScoNet trainer is not the problem
 - the strong silhouette checkpoint is not evidence that the skeleton-map path works
 - the main remaining suspect is the skeleton-map representation and preprocessing path
+- for practical model development, `1:1:2` is currently the better working split than `1:1:8`

 For readability in this repo's docs, `ScoNet-MT-ske` refers to the skeleton-map variant that the DRF paper writes as `ScoNet-MT^{ske}`.