@albanD @aauker
Thanks for all your reply, my pre-trained model is resnet50 which I think there’s no bias.
So is your problem that your model is very severely overfiting to the training set?

Actually ,I just wanted to check what model’s prediction looks like in testing set .However ,the result was just terrible.As I saw my training loss decrease beautifully ,so I decided to predict the image in the training set instead of test set. And I found two possible issues to see if you guys agree:
1.Like I mentioned above ,Since I have bn layers going on while training,the estimation of running mean&var with momentum =0.01 is not accurate, and during the test time,the running mean &var would be used.When I set it to 0.15 or even 0.2,the result of testing is actually better as the table 1 in this paper: Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates (look at the BN MAF).In my case 10 epochs for testing has less than 2000 iterations.
2.As I printed out the cls_loss and loc_loss for object detection ,and I realized that my cls_loss is not decreasing …So I think there’re some others issues in my code.
I guess I would wrap this up here,haha…the repo that I tried to dig into turns out leaving some problems there…Maybe next time make sure the code works(having good result) before I really dig into it.