diff --git a/PytorchWildlife/utils/post_process.py b/PytorchWildlife/utils/post_process.py
index 27a364557..ef6f028b7 100644
--- a/PytorchWildlife/utils/post_process.py
+++ b/PytorchWildlife/utils/post_process.py
@@ -220,7 +220,7 @@ def save_detection_classification_json(
                     "clf_confidence": [float(x) for x in clf_confidence],
                 }
             )
-        json.dump(json_results, f)
+        json.dump(json_results, f, indent=4)
 
 
 def save_detection_classification_timelapse_json(
diff --git a/demo/image_detection_colabdemo.ipynb b/demo/image_detection_colabdemo.ipynb
index 42bdc7a2b..1c2451ea9 100644
--- a/demo/image_detection_colabdemo.ipynb
+++ b/demo/image_detection_colabdemo.ipynb
@@ -1047,6 +1047,26 @@
         "                             exclude_category_ids=[]) # Category IDs can be found in the definition of each model."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "id": "20251521",
+      "metadata": {},
+      "source": [
+        "### 3.1 Timelapse JSON Format:\n",
+        "PytorchWildlife is compatible with Timelapse, a popular tool to visualize and process camera trap data. This code will output the detection results in a JSON format compatible with Timelapse:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0689bc45",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pw_utils.save_detection_timelapse_json(results, os.path.join(\".\",\"batch_output_timelapse.json\"),\n",
+        "                             categories=detection_model.CLASS_NAMES)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "a4ee1d7b",