TorchVision Detection models have a weights
and a weights_backbone
parameter. Does using pretrained weights
imply that the model uses pretrained weights_backbone
under the hood? I am training a RetinaNet model and um unsure which of the two options I should use and what the differences are.
TorchVision using pretrained weights for entire model vs backbone
664 Views Asked by Harry Stuart At
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The difference is pretty simple: you can either choose to do transfer learning on the backbone only or on the whole network.
RetinaNet from Torchvision has a Resnet50 backbone. You should be able to do both of:
retinanet_resnet50_fpn(weights=RetinaNet_ResNet50_FPN_Weights.COCO_V1)
retinanet_resnet50_fpn(backbone_weights=ResNet50_Weights.IMAGENET1K_V1)
As implied by their names, the backbone weights are different. The former were trained on COCO (object detection) while the later were trained on ImageNet (classification).
To answer your question, pretrained
weights
implies that the whole network, including backbone weights, are initialized. However, I don't think that it callsbackbone_weights
under the hood.