diff --git a/README.md b/README.md
index aeb58e6f46b..2f0c9dc7266 100644
--- a/README.md
+++ b/README.md
@@ -119,15 +119,10 @@ TorchGeo includes a number of [*benchmark datasets*](https://torchgeo.readthedoc
 If you've used [torchvision](https://pytorch.org/vision) before, these datasets should seem very familiar. In this example, we'll create a dataset for the Northwestern Polytechnical University (NWPU) very-high-resolution ten-class ([VHR-10](https://github.com/chaozhong2010/VHR-10_dataset_coco)) geospatial object detection dataset. This dataset can be automatically downloaded, checksummed, and extracted, just like with torchvision.
 
 ```python
-import kornia.augmentation as K
-import torch
 from torch.utils.data import DataLoader
 
-from torchgeo.datamodules.utils import AugPipe, collate_fn_detection
+from torchgeo.datamodules.utils import collate_fn_detection
 from torchgeo.datasets import VHR10
-from torchgeo.transforms import AugmentationSequential
-
-batch_size = 2
 
 # Initialize the dataset
 dataset = VHR10(root="...", download=True, checksum=True)
@@ -135,29 +130,17 @@ dataset = VHR10(root="...", download=True, checksum=True)
 # Initialize the dataloader with the custom collate function
 dataloader = DataLoader(
     dataset,
-    batch_size=batch_size,
+    batch_size=128,
     shuffle=True,
     num_workers=4,
     collate_fn=collate_fn_detection,
 )
-
-# Initialize augs to normalize and resize images to size (512, 512)
-aug = AugPipe(
-    augs=AugmentationSequential(
-        K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
-        K.Resize((512, 512)),
-        data_keys=["image", "boxes", "masks"],
-    ),
-    batch_size=batch_size,
-)
-
 # Training loop
 for batch in dataloader:
-    batch = aug(batch)
-    images = batch["image"] # List of images
-    boxes = batch["boxes"] # List of boxes
-    labels = batch["labels"] # List of labels
-    masks = batch["masks"] # List of masks
+    images = batch["image"]  # list of images
+    boxes = batch["boxes"]  # list of boxes
+    labels = batch["labels"]  # list of labels
+    masks = batch["masks"]  # list of masks
 
     # train a model, or make predictions using a pre-trained model
 ```