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Cannot reproduce linear evaluation performance on UCF-101 #4
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Hi, |
Thank you very much for your quick response. I trained from scratch and got 55% acc on UCF101, which seems OK. Would you please tell me the details of random initialization on Conv and FC layers? Random initialization on FC layer is more important? I used Normal distribution with (0, 1/np.sqrt(NUMBER of features)) on FC layer. |
Also, could you please tell me what the accuracy you got when only training the speed prediction? I got ~57% for it. Thanks. |
Hi, 55% with supervised sounds reasonable (although a bit lower than the 60% I got). I used the default initialization of TF (glorot-uniform I believe). Do you mean the training accuracy on the speed prediction task? I believe around 60% with 4 speed classes. |
Hi, thanks for your response. I need to check my code further. I found a strange thing when I trained on your source code. When I was going to pre-train only on speed prediction task, I set --transform 'orig' and delet |
Hi, if you used train_test_C3D.py for this, then the setup is different from Table 1 in the paper. The script does full fine-tuning of all the layers following the setup of Table 2. Otherwise, your steps to train only on speed prediction sound correct. You could specify train_scopes=''.join(['{}/fc_{}'.format(net_scope, i+1) for i in range(3)]) in line 50 to keep the conv layers fixed. |
Hi, Jenni, thanks for your patience. I nearly reproduced the performance on Pytorch platform with different learning rate settings on speed prediction task. Now I am still confused about one thing that why you used |
Dear friend, thank you very much for your work, I really learned a lot from it. It is impressive that after training only on speed prediction and 50 epoch, it got 49.3% acc on UCF-101 with linear evaluation. Nowadays, I have been trying to reproduce this performance on Pytorch following your code. But I just got 15% acc on UCF-101 with linear evaluation. Could you please give me some advice on how to achieve the performance? I have checked a lot of times that I followed your code and I may neglect some important things. Thank you very much.
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