diff --git a/avex/models/utils/load.py b/avex/models/utils/load.py
index 7341278..6bfb899 100644
--- a/avex/models/utils/load.py
+++ b/avex/models/utils/load.py
@@ -245,15 +245,14 @@ def _load_from_modelspec(
                 num_classes = len(label_mapping["label_to_index"])
                 model_kwargs["num_classes"] = num_classes
                 logger.info(f"Extracted num_classes={num_classes} from label mapping")
-        else:
-            # Checkpoint exists but no num_classes found - likely a backbone-only checkpoint
-            # Automatically enable embedding mode for models that support it
-            if supports_return_features_only:
-                return_features_only = True
-                model_kwargs["return_features_only"] = True
-                logger.info(
-                    f"Checkpoint found but no classifier detected; loading {model_type} in embedding extraction mode"
-                )
+        # If we still couldn't determine num_classes, treat this as a backbone-only
+        # checkpoint and fall back to embedding extraction for models that support it.
+        if "num_classes" not in model_kwargs and supports_return_features_only:
+            return_features_only = True
+            model_kwargs["return_features_only"] = True
+            logger.info(
+                f"Checkpoint found but no classifier detected; loading {model_type} in embedding extraction mode"
+            )
 
     # If pretrained=True, pretrained weights are typically backbone-only (no classifier)
     # Automatically enable embedding mode for models that support it
diff --git a/tests/unittests/test_api_load.py b/tests/unittests/test_api_load.py
index 2baac22..da6796c 100644
--- a/tests/unittests/test_api_load.py
+++ b/tests/unittests/test_api_load.py
@@ -812,3 +812,35 @@ def forward(self, x: torch.Tensor, padding_mask: torch.Tensor | None = None) ->
 
         registry._MODEL_CLASSES.clear()
         registry._MODEL_REGISTRY.clear()
+
+    def test_falls_back_to_embedding_mode_when_checkpoint_has_no_classifier(
+        self,
+        tmp_path: Path,
+    ) -> None:
+        """Test fallback to embedding mode for backbone-only checkpoints.
+
+        This matches the failure mode of some official EAT models where a checkpoint
+        exists but does not contain a classifier head and there is no label mapping.
+        In that case, we should automatically enable return_features_only=True for
+        models that support it.
+        """
+        checkpoint_path = tmp_path / "backbone_only.pt"
+        # No classifier/head keys: _extract_num_classes_from_checkpoint should return None.
+        torch.save({"backbone.some_weight": torch.randn(2, 2)}, checkpoint_path)
+
+        model_spec = ModelSpec(
+            name="test_model_type",
+            pretrained=False,
+            device="cpu",
+        )
+
+        model = _load_from_modelspec(
+            model_spec,
+            device="cpu",
+            checkpoint_path=str(checkpoint_path),
+            registry_key="test_model",
+            return_features_only=False,
+        )
+
+        assert isinstance(model, ModelBase)
+        assert getattr(model, "return_features_only", False) is True