diff --git a/README.md b/README.md
index 2d9f68a8c..13711cde9 100644
--- a/README.md
+++ b/README.md
@@ -39,8 +39,9 @@ The following sections give basic examples of what you can do with TorchGeo.
 First we'll import various classes and functions used in the following sections:
 
 ```python
-from lightning import Trainer
+from lightning.pytorch import Trainer
 from torch.utils.data import DataLoader
+
 from torchgeo.datamodules import InriaAerialImageLabelingDataModule
 from torchgeo.datasets import CDL, Landsat7, Landsat8, VHR10, stack_samples
 from torchgeo.samplers import RandomGeoSampler
@@ -56,8 +57,8 @@ Many remote sensing applications involve working with [*geospatial datasets*](ht
 In this example, we show how easy it is to work with geospatial data and to sample small image patches from a combination of [Landsat](https://www.usgs.gov/landsat-missions) and [Cropland Data Layer (CDL)](https://data.nal.usda.gov/dataset/cropscape-cropland-data-layer) data using TorchGeo. First, we assume that the user has Landsat 7 and 8 imagery downloaded. Since Landsat 8 has more spectral bands than Landsat 7, we'll only use the bands that both satellites have in common. We'll create a single dataset including all images from both Landsat 7 and 8 data by taking the union between these two datasets.
 
 ```python
-landsat7 = Landsat7(root="...")
-landsat8 = Landsat8(root="...", bands=Landsat8.all_bands[1:-2])
+landsat7 = Landsat7(root="...", bands=["B1", ..., "B7"])
+landsat8 = Landsat8(root="...", bands=["B2", ..., "B8"])
 landsat = landsat7 | landsat8
 ```
 
@@ -124,8 +125,18 @@ In order to facilitate direct comparisons between results published in the liter
 
 ```python
 datamodule = InriaAerialImageLabelingDataModule(root="...", batch_size=64, num_workers=6)
-task = SemanticSegmentationTask(segmentation_model="unet", encoder_weights="imagenet", learning_rate=0.1)
-trainer = Trainer(gpus=1, default_root_dir="...")
+task = SemanticSegmentationTask(
+    model="unet",
+    backbone="resnet50",
+    weights="imagenet",
+    in_channels=3,
+    num_classes=2,
+    loss="ce",
+    ignore_index=None,
+    learning_rate=0.1,
+    learning_rate_schedule_patience=6,
+)
+trainer = Trainer(default_root_dir="...")
 
 trainer.fit(model=task, datamodule=datamodule)
 ```