diff --git a/hubconf.py b/hubconf.py
index 33fc87930582..bffe2d588b4f 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -47,7 +47,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
                 model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
                 if autoshape:
                     if model.pt and isinstance(model.model, ClassificationModel):
-                        LOGGER.warning('WARNING: YOLOv5 v6.2 ClassificationModel is not yet AutoShape compatible. '
+                        LOGGER.warning('WARNING: ⚠️ YOLOv5 v6.2 ClassificationModel is not yet AutoShape compatible. '
                                        'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
                     else:
                         model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
diff --git a/utils/autobatch.py b/utils/autobatch.py
index 01152055196d..641b055b9fe3 100644
--- a/utils/autobatch.py
+++ b/utils/autobatch.py
@@ -18,7 +18,7 @@ def check_train_batch_size(model, imgsz=640, amp=True):
         return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
 
 
-def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
+def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
     # Automatically estimate best batch size to use `fraction` of available CUDA memory
     # Usage:
     #     import torch