diff --git a/src/deepforest/conf/schema.py b/src/deepforest/conf/schema.py
index 029a4960e..4cd6aba83 100644
--- a/src/deepforest/conf/schema.py
+++ b/src/deepforest/conf/schema.py
@@ -82,7 +82,7 @@ class TrainConfig:
     fast_dev_run: bool = False
     preload_images: bool = False
     validate_coordinates: bool = True
-    augmentations: list[str] | None = field(default_factory=lambda: ["HorizontalFlip"])
+    augmentations: list[Any] | None = field(default_factory=lambda: ["HorizontalFlip"])
 
 
 @dataclass
@@ -104,7 +104,7 @@ class ValidationConfig:
     iou_threshold: float = 0.4
     val_accuracy_interval: int = 20
     lr_plateau_target: str = "val_loss"
-    augmentations: list[str] | None = field(default_factory=lambda: [])
+    augmentations: list[Any] | None = field(default_factory=lambda: [])
 
 
 @dataclass
diff --git a/tests/test_augmentations.py b/tests/test_augmentations.py
index 57e391234..ff9a2d861 100644
--- a/tests/test_augmentations.py
+++ b/tests/test_augmentations.py
@@ -41,6 +41,34 @@ def test_load_dataset_without_augmentations():
     image, target, path = next(iter(train_ds))
     assert len(train_ds.dataset.transform) == 0
 
+def test_augmentation_schema_validation():
+    """
+    Test that the schema accepts a mixed list of dictionaries and strings for augmentations,
+    and that they are correctly applied to the dataset pipeline.
+    """
+    augmentations = [
+        {"RandomResizedCrop": {"size": (800, 800), "scale": (0.5, 1.0), "p": 0.3}},
+        "HorizontalFlip"
+    ]
+
+    m = main.deepforest(config_args={"train": {"augmentations": augmentations}})
+
+    # Verify Schema stored it correctly
+    assert m.config.train.augmentations == augmentations
+
+    csv_file = get_data("example.csv")
+    root_dir = os.path.dirname(csv_file)
+
+    train_ds = m.load_dataset(csv_file, root_dir=root_dir, augmentations=augmentations)
+
+    transforms = train_ds.dataset.transform
+
+    has_resized_crop = any(isinstance(t, K.RandomResizedCrop) for t in transforms)
+    has_hflip = any(isinstance(t, K.RandomHorizontalFlip) for t in transforms)
+
+    assert has_resized_crop
+    assert has_hflip
+
 """
 Augmentation parsing tests:
 """