@@ -95,6 +95,14 @@ keywords: Ultralytics, YOLO, Comet, callbacks, logging, machine learning, monito
 
 <br><br><hr><br>
 
+## ::: ultralytics.utils.callbacks.comet._log_asset
+
+<br><br><hr><br>
+
+## ::: ultralytics.utils.callbacks.comet._log_table
+
+<br><br><hr><br>
+
 ## ::: ultralytics.utils.callbacks.comet.on_pretrain_routine_start
 
 <br><br><hr><br>

@@ -91,6 +91,9 @@
 53246858+hasanghaffari93@users.noreply.github.com:
   avatar: https://avatars.githubusercontent.com/u/53246858?v=4
   username: hasanghaffari93
+57564335+CoderUni@users.noreply.github.com:
+  avatar: https://avatars.githubusercontent.com/u/57564335?v=4
+  username: CoderUni
 60036186+mfloto@users.noreply.github.com:
   avatar: https://avatars.githubusercontent.com/u/60036186?v=4
   username: mfloto

@@ -215,6 +215,22 @@ def test_export_mnn():
     YOLO(file)(SOURCE, imgsz=32)  # exported model inference
 
 
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "task, int8, half, batch",
+    [  # generate all combinations except for exclusion cases
+        (task, int8, half, batch)
+        for task, int8, half, batch in product(TASKS, [True, False], [True, False], [1, 2])
+        if not (int8 and half)
+    ],
+)
+def test_export_mnn_matrix(task, int8, half, batch):
+    """Test YOLO export to MNN format considering various export configurations."""
+    file = YOLO(TASK2MODEL[task]).export(format="mnn", imgsz=32, int8=int8, half=half, batch=batch)
+    YOLO(file)([SOURCE] * batch, imgsz=32)  # exported model inference
+    Path(file).unlink()  # cleanup
+
+
 @pytest.mark.slow
 def test_export_ncnn():
     """Test YOLO export to NCNN format."""