import utils

model = [torchvision.models.detection.fasterrcnn_resnet50_fpn](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.detection.fasterrcnn_resnet50_fpn.html#torchvision.models.detection.fasterrcnn_resnet50_fpn "torchvision.models.detection.fasterrcnn_resnet50_fpn")(weights="DEFAULT")
[dataset](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Subset "torch.utils.data.Subset") = [PennFudanDataset](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Dataset "torch.utils.data.Dataset")('data/PennFudanPed', get_transform(train=True))
[data_loader](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader "torch.utils.data.DataLoader") = [torch.utils.data.DataLoader](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader "torch.utils.data.DataLoader")(
    [dataset](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Subset "torch.utils.data.Subset"),
    batch_size=2,
    shuffle=True,
    collate_fn=utils.collate_fn
)

# For Training
images, targets = next(iter([data_loader](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader "torch.utils.data.DataLoader")))
images = list([image](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") for [image](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") in images)
targets = [{k: v for k, v in t.items()} for t in targets]
output = model(images, targets)  # Returns losses and detections
print(output)

# For inference
[model.eval](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.eval "torch.nn.Module.eval")()
[x](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") = [[torch.rand](https://docs.pytorch.org/docs/stable/generated/torch.rand.html#torch.rand "torch.rand")(3, 300, 400), [torch.rand](https://docs.pytorch.org/docs/stable/generated/torch.rand.html#torch.rand "torch.rand")(3, 500, 400)]
predictions = model([x](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor"))  # Returns predictions
print(predictions[0])