About the accuracy of fine-tuning on the target fine-grained datasetes

[Yifan]

Hi, I have read your paper which is a great work. But I have a question, how is the performance of fine-tuning the entire ResNet50 (both encoder and classifier) on these target fine-grained datasetes with supervised learning? Do you have the result?

[Sangmin Bae]

Hi and thanks for your good question!

Yes, in Table 7b, we summarized end-to-end supervised fine-tuning results with SSL pretrained ResNet50 on four fine-grained datasets.
But, we only reported not fully-labeled scenarios as in previous SSL works.

Here, we additionally summarize e2e fine-tuning results on fully-labeled (100%) target datasets:

Pretrain	Aircraft	Cars	Pet	Birds
X	79.89	88.63	78.24	67.46
OS	81.28	87.43	85.01	70.12
SimCore	83.28	89.54	85.95	71.24

cf) Note that we fine-tuned models for 100 epochs with a momentum SGD optimizer and weight decay of 1e-4.
We searched the optimal learning rate among three logarithmically spaced values from 1e-1 to 1e-2 (i.e., {1e-1, 3e-2, 1e-2}), and they are decayed after 60 and 80 epochs by the ratio of 0.1.