diff --git a/src/fibsem_tools/metadata/split_annotation.py b/src/fibsem_tools/metadata/split_annotation.py
index af6c9d0..5172f35 100755
--- a/src/fibsem_tools/metadata/split_annotation.py
+++ b/src/fibsem_tools/metadata/split_annotation.py
@@ -1,5 +1,5 @@
 import os
-from typing import Dict
+from typing import Dict, Literal, Tuple, TypeVar, Union
 import click
 from xarray import DataArray
 from fibsem_tools import read_xarray
@@ -20,47 +20,51 @@
     CropGroupAttrs,
     wrap_attributes
 )
+from xarray_multiscale import multiscale, windowed_mode
 
 ome_adapters = get_adapters("0.4")
-out_chunks = (256,) * 3
+out_chunks = (64,) * 3
 
 annotation_type = SemanticSegmentation(encoding={"absent": 0, "present": 1})
 
-
 def create_spec(
-    data: DataArray, crop_name: str, array_name: str, class_encoding: Dict[str, int]
+    data: dict[str, DataArray],
+    crop_name: str, 
+    class_encoding: Dict[str, int]
 ):
 
-    ome_meta = ome_adapters.multiscale_metadata([data], [array_name])
+    ome_meta = ome_adapters.multiscale_metadata(tuple(data.values()), tuple(data.keys()))
 
     annotation_group_specs: dict[str, GroupSpec] = {}
 
     for class_name, value in class_encoding.items():
-        data_unique = (data == value).astype("uint8")
-        num_present = int(data_unique.sum())
-        num_absent = data_unique.size - num_present
-        hist = {"absent": num_absent}
-
-        annotation_group_attrs = AnnotationGroupAttrs(
-            class_name=class_name, description="", annotation_type=annotation_type
-        )
-
-        annotation_array_attrs = AnnotationArrayAttrs(
-            class_name=class_name, histogram=hist, annotation_type=annotation_type
-        )
-        label_array_spec = ArraySpec.from_array(
-            data_unique,
-            chunks=out_chunks,
-            compressor=Blosc(cname="zstd"),
-            attrs={"cellmap": {"annotation": annotation_array_attrs}},
-        )
+        label_array_specs = {}
+        for array_name, array_data in data.items():
+            data_unique = (array_data == value).astype("uint8")
+            num_present = int(data_unique.sum())
+            num_absent = data_unique.size - num_present
+            hist = {"absent": num_absent}
+
+            annotation_group_attrs = AnnotationGroupAttrs(
+                class_name=class_name, description="", annotation_type=annotation_type
+            )
+
+            annotation_array_attrs = AnnotationArrayAttrs(
+                class_name=class_name, histogram=hist, annotation_type=annotation_type
+            )
+            label_array_specs[array_name] = ArraySpec.from_array(
+                data_unique,
+                chunks=out_chunks,
+                compressor=Blosc(cname="zstd"),
+                attrs=wrap_attributes(annotation_array_attrs).dict(),
+            )
 
         annotation_group_specs[class_name] = GroupSpec(
             attrs={
-                "cellmap": {"annotation": annotation_group_attrs},
+                **wrap_attributes(annotation_group_attrs).dict(),
                 "multiscales": [ome_meta.dict()],
             },
-            members={array_name: label_array_spec},
+            members=label_array_specs,
         )
 
     crop_attrs = CropGroupAttrs(
@@ -97,9 +101,18 @@ def guess_format(path: str):
 
 
 def split_annotations(
-    source: str, dest: str, crop_name: str, class_encoding: Dict[str, int]
+    source: str, 
+    dest: str, 
+    crop_name: str, 
+    class_encoding: Dict[str, int],
+    chunks: Union[Literal['auto'], Tuple[Tuple[int, ...],...]] = 'auto'
 ) -> zarr.Group:
 
+    if chunks == 'auto':
+        out_chunks = (64,64,64)
+    else:
+        out_chunks = chunks
+
     pre, post, _ = split_by_suffix(dest, (".zarr",))
     # fail fast if there's already a group there
 
@@ -123,9 +136,8 @@ def split_annotations(
     multi = {m.name: m for m in multiscale(data, windowed_mode, (2,2,2), chunks=out_chunks)}
 
     spec = create_spec(
-        data=data,
+        data=multi,
         crop_name=crop_name,
-        array_name=array_name,
         class_encoding=class_encoding,
     )
 
@@ -134,13 +146,14 @@ def split_annotations(
     )
 
     for class_name, value in class_encoding.items():
-        data_unique = np.array((data == value).astype("uint8"))
-        arr = zarr.Array(
-            store=crop_group.store,
-            path=os.path.join(crop_group.path, class_name, array_name),
-            write_empty_chunks=False,
-        )
-        arr[:] = data_unique
+        for array_name, data in multi.items():
+            data_unique = np.array((data == value).astype("uint8"))
+            arr = zarr.Array(
+                store=crop_group.store,
+                path=os.path.join(crop_group.path, class_name, array_name),
+                write_empty_chunks=False,
+            )
+            arr[:] = data_unique
 
     return crop_group
 
@@ -151,7 +164,7 @@ def split_annotations(
 @click.argument("name", type=click.STRING)
 def cli(source, dest, name):
     class_encoding = class_encoding_from_airtable_by_image(name)
-    split_annotations(source, dest, name, class_encoding)
+    split_annotations(source, dest, name, class_encoding, chunks)
 
 if __name__ == "__main__":
     cli()