diff --git a/train.py b/train.py
index dfe997b1bde2..d83f3cd1863c 100644
--- a/train.py
+++ b/train.py
@@ -36,7 +36,7 @@
 from models.experimental import attempt_load
 from models.yolo import Model
 from utils.autoanchor import check_anchors
-from utils.autobatch import check_batch_size
+from utils.autobatch import check_train_batch_size
 from utils.datasets import create_dataloader
 from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
     strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
@@ -137,9 +137,8 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple
 
     # Batch size
-    if cuda and RANK == -1:  # single-GPU only
-        with amp.autocast():
-            batch_size = check_batch_size(deepcopy(model).train(), imgsz, batch_size)
+    if RANK == -1 and batch_size == -1:  # single-GPU only, estimate best batch size
+        batch_size = check_train_batch_size(model, imgsz)
 
     # Optimizer
     nbs = 64  # nominal batch size
@@ -446,7 +445,7 @@ def parse_opt(known=False):
     parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
     parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')
     parser.add_argument('--epochs', type=int, default=300)
-    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
+    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
     parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
     parser.add_argument('--rect', action='store_true', help='rectangular training')
     parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
diff --git a/utils/autobatch.py b/utils/autobatch.py
index 3f97140c1a29..22a8c59040c8 100644
--- a/utils/autobatch.py
+++ b/utils/autobatch.py
@@ -7,20 +7,20 @@
 
 import numpy as np
 import torch
+from torch.cuda import amp
 
 from utils.general import colorstr
-from utils.torch_utils import de_parallel, profile
+from utils.torch_utils import profile
 
 
-def check_batch_size(model, imgsz=640, b=16):
-    # Check YOLOv5 batch size
-    if b < 1 or b == 'auto':
-        b = autobatch(model, imgsz)  # compute optimal batch size
-    return b
+def check_train_batch_size(model, imgsz=640):
+    # Check YOLOv5 training batch size
+    with amp.autocast():
+        return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
 
 
-def autobatch(model, imgsz=640, fraction=0.9):
-    # Automatically compute optimal batch size to use `fraction` of available CUDA memory
+def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
+    # Automatically estimate best batch size to use `fraction` of available CUDA memory
     # Usage:
     #     import torch
     #     from utils.autobatch import autobatch
@@ -30,6 +30,10 @@ def autobatch(model, imgsz=640, fraction=0.9):
     prefix = colorstr('autobatch: ')
     print(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
     device = next(model.parameters()).device  # get model device
+    if device.type == 'cpu':
+        print(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
+        return batch_size
+
     t = torch.cuda.get_device_properties(device).total_memory / 1024 ** 3  # (GB)
     r = torch.cuda.memory_reserved(device) / 1024 ** 3  # (GB)
     a = torch.cuda.memory_allocated(device) / 1024 ** 3  # (GB)