diff --git a/intermediate_source/torchvision_tutorial.rst b/intermediate_source/torchvision_tutorial.rst
index d58d88daec..9e3d1b9655 100644
--- a/intermediate_source/torchvision_tutorial.rst
+++ b/intermediate_source/torchvision_tutorial.rst
@@ -221,7 +221,7 @@ way of doing it:
    from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
 
    # load a model pre-trained on COCO
-   model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
+   model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT")
 
    # replace the classifier with a new one, that has
    # num_classes which is user-defined
@@ -242,7 +242,7 @@ way of doing it:
 
    # load a pre-trained model for classification and return
    # only the features
-   backbone = torchvision.models.mobilenet_v2(pretrained=True).features
+   backbone = torchvision.models.mobilenet_v2(weights="DEFAULT").features
    # FasterRCNN needs to know the number of
    # output channels in a backbone. For mobilenet_v2, it's 1280
    # so we need to add it here
@@ -291,7 +291,7 @@ be using Mask R-CNN:
 
    def get_model_instance_segmentation(num_classes):
        # load an instance segmentation model pre-trained on COCO
-       model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
+       model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights="DEFAULT")
 
        # get number of input features for the classifier
        in_features = model.roi_heads.box_predictor.cls_score.in_features
@@ -344,7 +344,7 @@ expects during training and inference time on sample data.
 
 .. code:: python
 
-   model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
+   model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT")
    dataset = PennFudanDataset('PennFudanPed', get_transform(train=True))
    data_loader = torch.utils.data.DataLoader(
     dataset, batch_size=2, shuffle=True, num_workers=4,