diff --git a/kauldron/data/py/base.py b/kauldron/data/py/base.py
index 6bd30a99..db817237 100644
--- a/kauldron/data/py/base.py
+++ b/kauldron/data/py/base.py
@@ -40,8 +40,11 @@ class PyGrainPipeline(pipelines.Pipeline):
   See doc:
 
   Attributes:
-    transforms: A list of transformations to apply to the dataset. Each
-      transformation should be either a `grain.MapTransform` or a
+    transforms: A list of transformations to apply to the dataset before
+      batching. Each transformation should be either a `grain.MapTransform` or a
+      `grain.RandomMapTransform`.
+    post_batch_transforms: A list of transformations to apply after batching.
+      Each transformation should be either a `grain.MapTransform` or a
       `grain.RandomMapTransform`.
     num_epochs: Number of epoch. If missing, iterate indefinitely (number of
       iteration is given by `cfg.num_training_steps`)
@@ -62,6 +65,9 @@ class PyGrainPipeline(pipelines.Pipeline):
   transforms: tr_normalize.Transformations = dataclasses.field(
       default_factory=tuple
   )
+  post_batch_transforms: tr_normalize.Transformations = dataclasses.field(
+      default_factory=tuple
+  )
 
   # Params only relevant for the root top-level dataset (when dataset mixture)
   num_epochs: Optional[int] = None
@@ -113,6 +119,7 @@ def _root_ds(self) -> grain.IterDataset:
     # batching.
     if self.batch_size:
       ds = ds.batch(self.batch_size, drop_remainder=self.batch_drop_remainder)
+      ds = transform_utils.apply_transforms(ds, self.post_batch_transforms)
 
     # Distribute the execution across multiple worker processes.
     num_workers = _get_num_workers(self.num_workers)
diff --git a/kauldron/data/py/transform_utils.py b/kauldron/data/py/transform_utils.py
index 19548d3e..9712a535 100644
--- a/kauldron/data/py/transform_utils.py
+++ b/kauldron/data/py/transform_utils.py
@@ -14,12 +14,14 @@
 
 """Utils for using Kauldron transforms with PyGrain."""
 
-from typing import Any, Callable, Mapping
+from typing import Any, Callable, Mapping, TypeVar
 
 import grain.python as grain
 from kauldron.data.transforms import abc as tr_abc
 from kauldron.data.transforms import normalize as tr_normalize
 
+_T = TypeVar("_T", grain.MapDataset, grain.IterDataset)
+
 
 class PyGrainMapAdapter(tr_normalize.TransformAdapter, grain.MapTransform):
   """Adapter from `kd.data.MapTransform` to pygrain."""
@@ -60,8 +62,8 @@ def _adapt_for_pygrain(
 
 
 def apply_transforms(
-    ds: grain.MapDataset, transforms: tr_normalize.Transformations
-) -> grain.MapDataset:
+    ds: _T, transforms: tr_normalize.Transformations
+) -> _T:
   """Apply the transformations to the dataset."""
   if isinstance(transforms, Mapping):
     transforms = transforms.values()
@@ -72,8 +74,8 @@ def apply_transforms(
 
 
 def _apply_transform(
-    ds: grain.MapDataset, tr: grain.Transformation
-) -> grain.MapDataset:
+    ds: _T, tr: grain.Transformation
+) -> _T:
   """Apply a list of single transformation."""
   match tr:
     case grain.MapTransform():
diff --git a/kauldron/data/tf/base.py b/kauldron/data/tf/base.py
index dee9f017..731f7c11 100644
--- a/kauldron/data/tf/base.py
+++ b/kauldron/data/tf/base.py
@@ -58,9 +58,12 @@ class TFDataPipeline(pipelines.Pipeline, abc.ABC):
   returns the `tf.data.Dataset` for the current process.
 
   Attributes:
-    transforms: A list of `grain.Transformation` to apply to the dataset. Can be
-      a dict to allow easier CLI / sweep access (
+    transforms: A list of `grain.Transformation` to apply to the dataset before
+      batching. Can be a dict to allow easier CLI / sweep access (
       `--cfg.train_ds.transforms.img_scale.in_vrange=(-1,1)`)
+    post_batch_transforms: A list of `grain.Transformation` to apply to the
+      dataset after batching. Can be a dict to allow easier CLI / sweep access
+      (`--cfg.train_ds.post_batch_transforms.img_scale.in_vrange=(-1,1)`)
     tf_data_options: An optional tf.data.Options instance to be applied to the
       dataset.
     prefetch_size: Number of batches to prefetch for this dataset. Defaults to
@@ -79,6 +82,7 @@ class TFDataPipeline(pipelines.Pipeline, abc.ABC):
   # TODO(epot): Users should also be able to specify drop_reminder or mask
   batch_drop_remainder: bool = True
   transforms: _Transforms = dataclasses.field(default_factory=tuple)
+  post_batch_transforms: _Transforms = dataclasses.field(default_factory=tuple)
 
   # Those fields are only applied once at the top level
   tf_data_options: Optional[tf.data.Options] = None
@@ -203,6 +207,13 @@ def _apply_transforms(self, ds: tf.data.Dataset) -> tf.data.Dataset:
       transforms.extend(self.transforms.values())
     else:
       transforms.extend(self.transforms)
+
+    post_batch_transforms = []
+    if isinstance(self.post_batch_transforms, Mapping):
+      post_batch_transforms.extend(self.post_batch_transforms.values())
+    else:
+      post_batch_transforms.extend(self.post_batch_transforms)
+
     if self.batch_size:
       transforms.append(
           grain.TfBatch(
@@ -210,6 +221,7 @@ def _apply_transforms(self, ds: tf.data.Dataset) -> tf.data.Dataset:
               drop_remainder=self.batch_drop_remainder,
           )
       )
+      transforms.extend(post_batch_transforms)
     ds = tr_utils.apply_transformations(ds, transforms)
     return ds