diff --git a/plugins/kitti_export_object_detection/pyproject.toml b/plugins/kitti_export_object_detection/pyproject.toml
index 67cf4e8..ba2caaa 100644
--- a/plugins/kitti_export_object_detection/pyproject.toml
+++ b/plugins/kitti_export_object_detection/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "KITTI object detection export plugin for Lightly Studio"
 requires-python = ">=3.9"
 dependencies = [
-    "lightly_studio>=1.0.0",
+    "lightly_studio>=1.0.4",
     "labelformat",
     "sqlmodel",
 ]
diff --git a/plugins/kitti_export_object_detection/src/lightly_plugins_kitti_export_object_detection/operator.py b/plugins/kitti_export_object_detection/src/lightly_plugins_kitti_export_object_detection/operator.py
index d01c2ea..77deb11 100644
--- a/plugins/kitti_export_object_detection/src/lightly_plugins_kitti_export_object_detection/operator.py
+++ b/plugins/kitti_export_object_detection/src/lightly_plugins_kitti_export_object_detection/operator.py
@@ -15,6 +15,7 @@
 from sqlmodel import Session
 
 from lightly_studio.core.image.image_sample import ImageSample
+from lightly_studio.export.image_dataset_export import image_sample_to_image
 from lightly_studio.export.lightly_studio_label_input import (
     LightlyStudioObjectDetectionInput,
 )
@@ -51,7 +52,13 @@ def __init__(
             images_root: Common root path used to preserve nested image folders.
         """
         self._images_root = images_root
-        super().__init__(session=session, dataset_id=dataset_id, samples=samples)
+        super().__init__(
+            session=session,
+            dataset_id=dataset_id,
+            samples=samples,
+            annotation_collection_id=None,
+            sample_to_image=image_sample_to_image,
+        )
 
     def get_images(self) -> list[Image]:
         """Return images with filenames relative to the KITTI output folder."""
diff --git a/plugins/yolo_object_detection/src/lightly_plugins_yolo_object_detection/operator.py b/plugins/yolo_object_detection/src/lightly_plugins_yolo_object_detection/operator.py
index e2a532f..8c8bb91 100644
--- a/plugins/yolo_object_detection/src/lightly_plugins_yolo_object_detection/operator.py
+++ b/plugins/yolo_object_detection/src/lightly_plugins_yolo_object_detection/operator.py
@@ -9,6 +9,7 @@
 
 from sqlmodel import Session
 from ultralytics import YOLO  # type: ignore[attr-defined]
+from ultralytics.engine.results import Results
 
 from lightly_studio.models.annotation.annotation_base import (
     AnnotationCreate,
@@ -140,8 +141,8 @@ def execute(
         total_annotations_created = 0
         for i, image_entry in enumerate(samples, start=1):
             try:
-                results = model(
-                    image_entry.file_path_abs, conf=confidence, verbose=False
+                result = list(
+                    model(image_entry.file_path_abs, conf=confidence, verbose=False)
                 )[0]
             except Exception as e:
                 logger.error(
@@ -153,12 +154,22 @@ def execute(
                     success=False,
                     message=f"Failed to run inference on '{image_entry.file_path_abs}': {e}",
                 )
-            for box in results.boxes:
-                category_id = int(box.cls)
+            # A single image always yields one `Results`; `embed=` is never passed.
+            assert isinstance(result, Results)
+            boxes = result.boxes
+            if boxes is None:
+                logger.warning(
+                    "No boxes returned for '%s'; is '%s' a detection model?",
+                    image_entry.file_path_abs,
+                    model_path,
+                )
+                continue
+            for box_index in range(len(boxes)):
+                category_id = int(boxes.cls[box_index])
                 label_id = label_map.get(category_id)
                 if label_id is None:
                     continue
-                x_center, y_center, w, h = box.xywh[0].tolist()
+                x_center, y_center, w, h = boxes.xywh[box_index].tolist()
                 annotations_to_create.append(
                     AnnotationCreate(
                         annotation_label_id=label_id,
@@ -168,7 +179,7 @@ def execute(
                         y=round(y_center - h / 2),
                         width=max(1, round(w)),
                         height=max(1, round(h)),
-                        confidence=float(box.conf),
+                        confidence=float(boxes.conf[box_index]),
                     )
                 )