Add new regression and ensemble experiment configurations for V2-M and OrthoBridge

- Introduced multiple regression experiment configurations targeting vf_md, including:
  - cd_solo_reg_set.json: CD tower only regression setup.
  - img_solo_reg_set.json: Image tower only regression setup.
  - reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100).
  - reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations.
  - single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis.

- Added ensemble configurations for OrthoBridge with different inner bridges:
  - ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge.
  - ortho_alts_tritower.json: Tritower tests with the same inner bridges.

- Created V2-M specific configurations:
  - baseline_reg_nt50.json: Regression baseline with V2-M backbone.
  - geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M.
  - single_l1_bridges.json: Single-eye ensemble experiments with various bridge types.
  - tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks.

- Promoted existing experiments to higher repetitions for robustness.

v4/classes/accessory/transforms.py

@@ -6,7 +6,7 @@ from typing import Tuple
 
 from torchvision import transforms
 
-from v4.classes.accessory.backbones import BACKBONES
+from v4.classes.accessory.backbones import BACKBONES, _is_timm_backbone
 
 IMAGENET_MEAN: Tuple[float, float, float] = (0.485, 0.456, 0.406)
 IMAGENET_STD:  Tuple[float, float, float] = (0.229, 0.224, 0.225)
@@ -79,6 +79,11 @@ class ImageTransformConfig:
 def backbone_transform_config(backbone_name: str, augment: bool = True) -> ImageTransformConfig:
     """Build an ImageTransformConfig using the backbone's default normalisation stats."""
     key = (backbone_name or "").lower()
+    if _is_timm_backbone(key):
+        # ConvNeXt-V2 and other timm models we currently expose are all
+        # pretrained with standard ImageNet stats at 224×224.
+        return ImageTransformConfig(crop_size=224, mean=IMAGENET_MEAN,
+                                    std=IMAGENET_STD, augment=augment)
     if key not in BACKBONES:
         raise ValueError(f"Unknown backbone '{backbone_name}'.")
     spec = BACKBONES[key]