BatchNormalization's Running Stats are Accumulated in ImageNet Linear Evaluation

Hi,

Thanks for the nice paper and clear code!

I found that the models are set with .train() in clf_linear.py. Thus the running averages (i.e., the states) of BatchNormalization layers will be accumulated when training the ImageNet datasets (via calling the forward function), and the backbone model seems not to be fully frozen. Is it a special design for this fine-tuning task?

Best, Hao

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Thanks for the prompt reply. I am glad that all the experiments are not affected by this XD.

I asked the questions because the (now-fixed) adapted BN reminds an old paper thus I am not sure whether this is a common practice for domain adaption (from COCO --> IN).

(Haha. I surely trust you for all these experiments even without these histories. However, the remaining commits are definitely more convincing to everyone on the earth. )

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Great. I closed this issue. Thanks for all these answers and the awesome codebase/paper.