diff --git a/README.md b/README.md
index 11e1341..350fac9 100644
--- a/README.md
+++ b/README.md
@@ -155,16 +155,16 @@ You can specify which model file to use with `--model MODEL.pth`.
 The training progress can be visualized in real-time using [Weights & Biases](https://wandb.ai/).  Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform.
 
 When launching a training, a link will be printed in the console. Click on it to go to your dashboard. If you have an existing W&B account, you can link it
- by setting the `WANDB_API_KEY` environment variable.
+ by setting the `WANDB_API_KEY` environment variable. If not, it will create an anonymous run which is automatically deleted after 7 days.
 
 
 ## Pretrained model
-A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v2.0) is available for the Carvana dataset. It can also be loaded from torch.hub:
+A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v3.0) is available for the Carvana dataset. It can also be loaded from torch.hub:
 
 ```python
-net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True)
+net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True, scale=0.5)
 ```
-The training was done with a 50% scale and bilinear upsampling.
+Available scales are 0.5 and 1.0.
 
 ## Data
 The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data).
diff --git a/hubconf.py b/hubconf.py
index fa6f7a0..d9c39d9 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -1,14 +1,20 @@
 import torch
 from unet import UNet as _UNet
 
-def unet_carvana(pretrained=False):
+def unet_carvana(pretrained=False, scale=0.5):
     """
     UNet model trained on the Carvana dataset ( https://www.kaggle.com/c/carvana-image-masking-challenge/data ).
     Set the scale to 0.5 (50%) when predicting.
     """
-    net = _UNet(n_channels=3, n_classes=2, bilinear=True)
+    net = _UNet(n_channels=3, n_classes=2, bilinear=False)
     if pretrained:
-        checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v2.0/unet_carvana_scale0.5_epoch1.pth'
+        if scale == 0.5:
+            checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale0.5_epoch2.pth'
+        elif scale == 1.0:
+            checkpoint = 'https://github.com/milesial/Pytorch-UNet/releases/download/v3.0/unet_carvana_scale1.0_epoch2.pth'
+        else:
+            raise RuntimeError('Only 0.5 and 1.0 scales are available')
+
         net.load_state_dict(torch.hub.load_state_dict_from_url(checkpoint, progress=True))
 
     return net
diff --git a/predict.py b/predict.py
index fdb95ea..9cfe187 100755
--- a/predict.py
+++ b/predict.py
@@ -57,6 +57,7 @@ def get_args():
                         help='Minimum probability value to consider a mask pixel white')
     parser.add_argument('--scale', '-s', type=float, default=0.5,
                         help='Scale factor for the input images')
+    parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling')
 
     return parser.parse_args()
 
@@ -81,7 +82,7 @@ if __name__ == '__main__':
     in_files = args.input
     out_files = get_output_filenames(args)
 
-    net = UNet(n_channels=3, n_classes=2)
+    net = UNet(n_channels=3, n_classes=2, bilinear=args.bilinear)
 
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     logging.info(f'Loading model {args.model}')
diff --git a/train.py b/train.py
index cae9a86..bffe179 100644
--- a/train.py
+++ b/train.py
@@ -25,7 +25,7 @@ def train_net(net,
               device,
               epochs: int = 5,
               batch_size: int = 1,
-              learning_rate: float = 0.001,
+              learning_rate: float = 1e-5,
               val_percent: float = 0.1,
               save_checkpoint: bool = True,
               img_scale: float = 0.5,
@@ -147,13 +147,14 @@ def get_args():
     parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
     parser.add_argument('--epochs', '-e', metavar='E', type=int, default=5, help='Number of epochs')
     parser.add_argument('--batch-size', '-b', dest='batch_size', metavar='B', type=int, default=1, help='Batch size')
-    parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=0.00001,
+    parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=1e-5,
                         help='Learning rate', dest='lr')
     parser.add_argument('--load', '-f', type=str, default=False, help='Load model from a .pth file')
     parser.add_argument('--scale', '-s', type=float, default=0.5, help='Downscaling factor of the images')
     parser.add_argument('--validation', '-v', dest='val', type=float, default=10.0,
                         help='Percent of the data that is used as validation (0-100)')
     parser.add_argument('--amp', action='store_true', default=False, help='Use mixed precision')
+    parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling')
 
     return parser.parse_args()
 
@@ -168,7 +169,7 @@ if __name__ == '__main__':
     # Change here to adapt to your data
     # n_channels=3 for RGB images
     # n_classes is the number of probabilities you want to get per pixel
-    net = UNet(n_channels=3, n_classes=2, bilinear=True)
+    net = UNet(n_channels=3, n_classes=2, bilinear=args.bilinear)
 
     logging.info(f'Network:\n'
                  f'\t{net.n_channels} input channels\n'
diff --git a/unet/unet_model.py b/unet/unet_model.py
index efa7108..20c35b5 100644
--- a/unet/unet_model.py
+++ b/unet/unet_model.py
@@ -4,7 +4,7 @@ from .unet_parts import *
 
 
 class UNet(nn.Module):
-    def __init__(self, n_channels, n_classes, bilinear=True):
+    def __init__(self, n_channels, n_classes, bilinear=False):
         super(UNet, self).__init__()
         self.n_channels = n_channels
         self.n_classes = n_classes