Default --data `imagenette160` training (fastest) (#9033)

classify/train.py

@@ -6,7 +6,7 @@ YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov
 Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
 
 Usage - Single-GPU and Multi-GPU DDP
-    $ python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 128
+    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 128
     $ 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
 """
 
@@ -266,8 +266,8 @@ def train(opt, device):
 def parse_opt(known=False):
     parser = argparse.ArgumentParser()
     parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path')
-    parser.add_argument('--data', type=str, default='mnist', help='cifar10, cifar100, mnist, imagenet, etc.')
-    parser.add_argument('--epochs', type=int, default=10)
+    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('--nosave', action='store_true', help='only save final checkpoint')

train.py

@@ -436,7 +436,7 @@ def parse_opt(known=False):
     parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
     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-low.yaml', help='hyperparameters path')
-    parser.add_argument('--epochs', type=int, default=300)
+    parser.add_argument('--epochs', type=int, default=300, help='total training epochs')
     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')