diff --git a/flash/data/batch.py b/flash/data/batch.py
index f08be37d02..38929f6079 100644
--- a/flash/data/batch.py
+++ b/flash/data/batch.py
@@ -243,6 +243,8 @@ def default_uncollate(batch: Any):
     batch_type = type(batch)
 
     if isinstance(batch, Tensor):
+        if len(batch.shape) == 0:  # 0 shape tensors
+            return batch
         return list(torch.unbind(batch, 0))
 
     elif isinstance(batch, Mapping):
diff --git a/flash_examples/predict/object_detection.py b/flash_examples/predict/object_detection.py
new file mode 100644
index 0000000000..dce35f0c3c
--- /dev/null
+++ b/flash_examples/predict/object_detection.py
@@ -0,0 +1,31 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from flash import Trainer
+from flash.data.utils import download_data
+from flash.vision import ObjectDetector
+
+# 1. Download the data
+# Dataset Credit: https://www.kaggle.com/ultralytics/coco128
+download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/")
+
+# 2. Load the model from a checkpoint
+model = ObjectDetector.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/object_detection_model.pt")
+
+# 3. Detect the object on the images
+predictions = model.predict([
+    "data/coco128/images/train2017/000000000025.jpg",
+    "data/coco128/images/train2017/000000000520.jpg",
+    "data/coco128/images/train2017/000000000532.jpg",
+])
+print(predictions)