Ref this tutorial: TorchVision Object Detection Finetuning Tutorial — PyTorch Tutorials 1.8.1+cu102 documentation 7, where “Section 2 - Modifying the model to add a different backbone” reads:
# load a pre-trained model for classification and return
# only the features
backbone = torchvision.models.mobilenet_v2(pretrained=True).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
backbone.out_channels = 1280
Is it possible to use a pretrained Resnet101 (not fpn) as a backbone without rewriting a lot of code?
I found no obvious feature variable to use in Resnet101.
Sorry if this is a silly question.