Update ClassificationModel default training `imgsz=224` (#9469)

classify/train.py

@@ -3,7 +3,7 @@
 Train a YOLOv5 classifier model on a classification dataset
 
 Usage - Single-GPU training:
-    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 128
+    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
 
 Usage - Multi-GPU DDP training:
     $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
@@ -272,7 +272,7 @@ def parse_opt(known=False):
     parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...')
     parser.add_argument('--epochs', type=int, default=10, help='total training epochs')
     parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs')
-    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=128, help='train, val image size (pixels)')
+    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)')
     parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
     parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')