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milk10k_effb2_metadata/cli.py

@@ -53,6 +53,11 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument("--weight-decay", type=float, default=1e-4)
     parser.add_argument("--val-size", type=float, default=0.20)
+    parser.add_argument(
+        "--synthetic-train-only",
+        action="store_true",
+        help="Keep synthetic lesion IDs containing __sdpair_ in train only; validation is split from real lesions.",
+    )
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--branch-dim", type=int, default=512)
     parser.add_argument("--metadata-dim", type=int, default=64)

milk10k_effb2_metadata/training.py

@@ -483,7 +483,18 @@ def train_single_run(
     clinical_backbone_backend: str,
     dermoscopic_backbone_backend: str,
 ) -> dict[str, Any]:
-    train_df, val_df = lesion_split(df, args.val_size, args.seed)
+    if args.synthetic_train_only:
+        synthetic_mask = df["lesion_id"].astype(str).str.contains("__sdpair_", regex=False)
+        real_df = df[~synthetic_mask].copy()
+        synthetic_df = df[synthetic_mask].copy()
+        train_df, val_df = lesion_split(real_df, args.val_size, args.seed)
+        train_df = pd.concat([train_df, synthetic_df], ignore_index=True, sort=False)
+        print(
+            f"Synthetic train-only split: real_train={len(train_df) - len(synthetic_df)}, "
+            f"synthetic_train={len(synthetic_df)}, val_real={len(val_df)}"
+        )
+    else:
+        train_df, val_df = lesion_split(df, args.val_size, args.seed)
     return run_training_split(
         df,
         train_df,