roboflow · Borda · Jun 13, 2026 · Jun 13, 2026 · Jun 13, 2026
@@ -179,8 +179,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import rfdetr.datasets.aug_config as aug_config\n",
-    "from rfdetr.datasets.aug_config import AUG_AGGRESSIVE\n",
+    "import rfdetr.datasets.aug_configs as aug_config\n",
+    "from rfdetr.datasets.aug_configs import AUG_AGGRESSIVE\n",
     "\n",
     "for name in (\"AUG_CONSERVATIVE\", \"AUG_AGGRESSIVE\", \"AUG_AERIAL\", \"AUG_INDUSTRIAL\"):\n",
     "    preset = getattr(aug_config, name)\n",

@@ -272,10 +272,10 @@ model.train(
 )
 ```
 
-Use a built-in preset by importing it from `rfdetr.datasets.aug_config`:
+Use a built-in preset by importing it from `rfdetr.datasets.aug_configs`:
 
 ```python
-from rfdetr.datasets.aug_config import AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL
+from rfdetr.datasets.aug_configs import AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL
 
 model.train(dataset_dir="path/to/dataset", aug_config=AUG_AGGRESSIVE)
 ```

@@ -12,7 +12,7 @@ Pass `aug_config` to your training call. Import one of the built-in presets:
 
 ```python
 from rfdetr import RFDETRSmall
-from rfdetr.datasets.aug_config import AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL
+from rfdetr.datasets.aug_configs import AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL
 
 model = RFDETRSmall()
 model.train(dataset_dir="path/to/dataset", epochs=100, aug_config=AUG_CONSERVATIVE)
@@ -46,7 +46,7 @@ To disable augmentations: `aug_config={}`. Omitting it uses the default (horizon
 All presets are plain dicts — inspect or extend them before passing:
 
 ```python
-from rfdetr.datasets.aug_config import AUG_AGGRESSIVE
+from rfdetr.datasets.aug_configs import AUG_AGGRESSIVE
 
 my_config = {**AUG_AGGRESSIVE, "VerticalFlip": {"p": 0.1}}
 model.train(dataset_dir="...", aug_config=my_config)

@@ -179,8 +179,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import rfdetr.datasets.aug_config as aug_config\n",
-    "from rfdetr.datasets.aug_config import AUG_AGGRESSIVE\n",
+    "import rfdetr.datasets.aug_configs as aug_config\n",
+    "from rfdetr.datasets.aug_configs import AUG_AGGRESSIVE\n",
     "\n",
     "for name in (\"AUG_CONSERVATIVE\", \"AUG_AGGRESSIVE\", \"AUG_AERIAL\", \"AUG_INDUSTRIAL\"):\n",
     "    preset = getattr(aug_config, name)\n",

@@ -8,7 +8,7 @@
 Import a preset and pass it as ``aug_config`` to your training call:
 
 ```python
-from rfdetr.datasets.aug_config import AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL
+from rfdetr.datasets.aug_configs import AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL
 
 model.train(dataset_dir="...", aug_config=AUG_CONSERVATIVE) model.train(dataset_dir="...", aug_config=AUG_AGGRESSIVE)
 

@@ -26,7 +26,7 @@
 from PIL import Image
 from torchvision.transforms.v2 import Compose, ToDtype, ToImage
 
-from rfdetr.datasets.aug_config import AUG_CONFIG
+from rfdetr.datasets.aug_configs import AUG_CONFIG
 from rfdetr.datasets.transforms import AlbumentationsWrapper, Normalize
 from rfdetr.utilities.logger import get_logger
 
@@ -542,7 +542,7 @@ def make_coco_transforms(
             :func:`compute_multi_scale_scales` to derive candidate resolutions.
         aug_config: Albumentations augmentation config dict passed to
             :class:`~rfdetr.datasets.transforms.AlbumentationsWrapper`.  Falls back to the default
-            :data:`~rfdetr.datasets.aug_config.AUG_CONFIG` when ``None``.
+            :data:`~rfdetr.datasets.aug_configs.AUG_CONFIG` when ``None``.
         gpu_postprocess: When ``True``, skip Albumentations augmentation wrappers and
             ``Normalize`` from the CPU pipeline.  The ``RFDETRDataModule`` then applies both augmentation and
             normalization on the GPU in ``on_after_batch_transfer``.  Has no effect on val/test splits.
@@ -642,7 +642,7 @@ def make_coco_transforms_square_div_64(
             derive the list of candidate square resolutions.
         aug_config: Augmentation configuration dictionary compatible with
             :class:`~rfdetr.datasets.transforms.AlbumentationsWrapper`. If ``None``, the default
-            :data:`~rfdetr.datasets.aug_config.AUG_CONFIG` is used.
+            :data:`~rfdetr.datasets.aug_configs.AUG_CONFIG` is used.
         gpu_postprocess: When ``True``, skip Albumentations augmentation wrappers and
             ``Normalize`` from the CPU pipeline.  The ``RFDETRDataModule`` then applies both augmentation and
             normalization on the GPU in ``on_after_batch_transfer``.  Has no effect on val/test splits.

@@ -306,7 +306,7 @@ def build_kornia_pipeline(
         ValueError: If *aug_config* contains an unsupported augmentation key.
 
     Examples:
-        >>> from rfdetr.datasets.aug_config import AUG_CONSERVATIVE
+        >>> from rfdetr.datasets.aug_configs import AUG_CONSERVATIVE
         >>> pipeline = build_kornia_pipeline(AUG_CONSERVATIVE, resolution=560)
         >>> pipeline_seg = build_kornia_pipeline(AUG_CONSERVATIVE, resolution=560, with_masks=True)
     """

@@ -662,7 +662,7 @@ def build_roboflow_from_yolo(image_set: str, args: Any, resolution: int) -> Yolo
             ``dataset_dir``, ``square_resize_div_64``, ``aug_config``, ``segmentation_head``, ``multi_scale``,
             ``expanded_scales``, ``do_random_resize_via_padding``, ``patch_size``, ``num_windows``. ``aug_config`` is
             forwarded to the transform builder; when ``None`` the builder falls back to the default
-            :data:`~rfdetr.datasets.aug_config.AUG_CONFIG`.
+            :data:`~rfdetr.datasets.aug_configs.AUG_CONFIG`.
         resolution: Target square resolution in pixels.
 
     Returns:

@@ -16,7 +16,7 @@
 from rfdetr._namespace import _namespace_from_configs
 from rfdetr.config import ModelConfig, TrainConfig
 from rfdetr.datasets import build_dataset
-from rfdetr.datasets.aug_config import AUG_CONFIG
+from rfdetr.datasets.aug_configs import AUG_CONFIG
 from rfdetr.utilities.box_ops import box_xyxy_to_cxcywh
 from rfdetr.utilities.logger import get_logger
 from rfdetr.utilities.tensors import make_collate_fn

@@ -16,7 +16,7 @@
 from torchvision.transforms.v2 import Compose
 
 from rfdetr.datasets._develop import _SimpleDataset
-from rfdetr.datasets.aug_config import AUG_AGGRESSIVE, AUG_CONFIG
+from rfdetr.datasets.aug_configs import AUG_AGGRESSIVE, AUG_CONFIG
 from rfdetr.datasets.coco import make_coco_transforms, make_coco_transforms_square_div_64
 from rfdetr.datasets.transforms import AlbumentationsWrapper, _build_albu_transform
 from rfdetr.utilities import collate_fn

@@ -12,7 +12,7 @@
 import pytest
 import torch
 
-from rfdetr.datasets.aug_config import (
+from rfdetr.datasets.aug_configs import (
     AUG_AERIAL,
     AUG_AGGRESSIVE,
     AUG_CONSERVATIVE,

@@ -1035,7 +1035,7 @@ def test_gpu_path_uses_aug_config_fallback(self, tmp_path):
         import sys
         from unittest.mock import MagicMock, patch
 
-        from rfdetr.datasets.aug_config import AUG_CONFIG
+        from rfdetr.datasets.aug_configs import AUG_CONFIG
 
         dm = self._build_dm_with_backend(tmp_path, "auto")
         assert dm.train_config.aug_config is None, "precondition: aug_config must be None for this test"