diff --git a/zetta_utils/training/datasets/collection_dataset.py b/zetta_utils/training/datasets/collection_dataset.py
index 71f8b822c..058418ba1 100644
--- a/zetta_utils/training/datasets/collection_dataset.py
+++ b/zetta_utils/training/datasets/collection_dataset.py
@@ -1,5 +1,6 @@
 import json
 import os
+from collections import defaultdict
 from typing import Sequence
 
 import fsspec
@@ -28,8 +29,8 @@ def build_collection_dataset(
     chunk_size: Sequence[int],
     chunk_stride: Sequence[int],
     layer_rename_map: dict[str, str],
-    shared_read_procs: Sequence[DataProcessor],
-    per_layer_read_procs: dict[str, Sequence[DataProcessor]],
+    per_layer_read_procs: dict[str, Sequence[DataProcessor]] | None = None,
+    shared_read_procs: Sequence[DataProcessor] = tuple(),
     tags: list[str] | None = None,
 ) -> JointDataset:
     datasets = {}
@@ -37,6 +38,10 @@ def build_collection_dataset(
         collection_ids=[collection_name], tags=tags, union=False
     )
     layer_group_map: dict[str, dict[str, Layer]] = {}
+    if per_layer_read_procs is None:
+        per_layer_read_procs_dict = defaultdict(tuple)
+    else:
+        per_layer_read_procs_dict = per_layer_read_procs
 
     for i, annotation in enumerate(annotations.values()):
         if annotation.layer_group not in layer_group_map:
@@ -47,17 +52,18 @@ def build_collection_dataset(
                 name = layer.name
                 if name in layer_rename_map:
                     name = layer_rename_map[name]
-                read_procs = per_layer_read_procs[name]
+                read_procs = per_layer_read_procs_dict[name]
                 layers[name] = build_cv_layer(path=layer.source, read_procs=read_procs)
             layer_group_map[annotation.layer_group] = layers
         else:
             layers = layer_group_map[annotation.layer_group]
+
         z_resolution = resolution[-1]
         for layer in layers.values():
             info_path = os.path.join(layer.backend.name.strip("precomputed://"), "info")
             with fsspec.open(info_path) as f:
                 info = json.loads(f.read())
-                z_resolutions = set([e["resolution"][-1] for e in info["scales"]])
+                z_resolutions = {e["resolution"][-1] for e in info["scales"]}
                 if len(z_resolutions) != 1:
                     raise RuntimeError("Only layers with single z resolution are supported")
                 z_resolution = list(z_resolutions)[0]
@@ -68,15 +74,13 @@ def build_collection_dataset(
             point_b_nm = Vec3D(*ng_bbox.pointB).int()
 
             start_coord = [
-                round((min(point_a_nm[i], point_b_nm[i]) / this_resolution[i]))
-                * this_resolution[i]
+                int((min(point_a_nm[i], point_b_nm[i]) / this_resolution[i])) * this_resolution[i]
                 for i in range(3)
             ]
             end_coord = [
-                round(max(point_a_nm[i], point_b_nm[i]) / this_resolution[i]) * this_resolution[i]
+                int(max(point_a_nm[i], point_b_nm[i]) / this_resolution[i]) * this_resolution[i]
                 for i in range(3)
             ]
-
             bbox = BBox3D.from_coords(start_coord=start_coord, end_coord=end_coord)
             datasets[str(i)] = LayerDataset(
                 layer=build_layer_set(layers=layers, read_procs=shared_read_procs),