  Fine-tuning from pre-trained ImageNet models has become the de-facto standard
for various computer vision tasks. Current practices for fine-tuning typically
involve selecting an ad-hoc choice of hyperparameters and keeping them fixed to
values normally used for training from scratch. This paper re-examines several
common practices of setting hyperparameters for fine-tuning. Our findings are
based on extensive empirical evaluation for fine-tuning on various transfer
learning benchmarks. (1) While prior works have thoroughly investigated
learning rate and batch size, momentum for fine-tuning is a relatively
unexplored parameter. We find that the value of momentum also affects
fine-tuning performance and connect it with previous theoretical findings. (2)
Optimal hyperparameters for fine-tuning, in particular, the effective learning
rate, are not only dataset dependent but also sensitive to the similarity
between the source domain and target domain. This is in contrast to
hyperparameters for training from scratch. (3) Reference-based regularization
that keeps models close to the initial model does not necessarily apply for
"dissimilar" datasets. Our findings challenge common practices of fine-tuning
and encourages deep learning practitioners to rethink the hyperparameters for
fine-tuning.
