dataloader = DataLoader(dataset, batch_size=128, shuffle=True, num_workers=4)
for batch in dataloader:
    image = batch["image"]
    label = batch["label"]
# train a model, or make predictions using a pre-trained model

```
All TorchGeo datasets are compatible with PyTorch data loaders, making them easy to integrate into existing training workflows. The only difference between a benchmark dataset in TorchGeo and a similar dataset in torchvision is that each dataset returns a dictionary with keys for each PyTorch Tensor.




Example predictions from a Mask R-CNN model trained on the NWPU VHR-10 dataset. The model predicts sharp bounding boxes and masks for all objects with high confidence scores.

Reproducibility with PyTorch Lightning