Train the classification model without setting DinoV2's param.requires_grad = False

[Hao-Ping]

Hello,

Thank you for sharing your great article on Medium and GitHub. I was inspired a lot.
In 3.DinoV2_VS_ResnetClassification.ipynb, you load the dinov2_vits14 model, and I don't see anywhere you freeze the parameters in the dinov2.

Does it mean that during the training process, you tune all the parameters in the DinoVisionTransformerClassifier (including transformer and linear layer)?
Then does it mean that all the benefits come from the pretrained model and curated data is discarded?