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UCF101 action classification result only at 0.68 #2
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Hi, @wuchlei. Thank you for your interest. Actually, when I prepared this repo, I trained the finetuning part for about 2 times, and the results are 67.4% and 69.8%. With different random seeds, the performances vary from one to another. Therefore, I think 0.685 and 0.68 are acceptable. By the way, in our old code version, we directly used To further improve the performance, you can try many strategies such as
Those are usable and effective tricks while we do not include them here because this is not our target in this paper. |
Thanks for the reply. I'll give it a try. |
@BestJuly hi Li,
However I'm still only getting an accuracy around 0.684. I've even tried to use the strong augmentations in SimCLR to train the backbone and I'm only getting a 2% percent improvement (0.702 accuracy, still not close to the 0.72 accuracy reported in the paper). Could you please share ur code or training scripts for the fine-tuning phase? It would be very helpful. |
Hi, @wuchlei . May I ask a question, which SSL pretrained model do you use? I rerun the code twice using the provided model and the current code (ft_classify.py, *Please note that for testing dataset, I use The reported result on UCF101 split 1 in the paper is 71.8% @ top1 (Table. 5, settings: frame repeating, res, R3D). I think it is reasonable and here I do not use any strong data augmentations. Again, I want to say that achiving the exact the same results as that in the paper is impossible because the training procedure includes SSL pretraining and finetuning, and the final recognition results may be affected by each step. |
Thanks for the reply. With these fixes, I've reproduced the results (around 0.72 for Res+Repeat). With SimCLR strong augmentation, I'm even getting a 0.8% improvement. |
Oh, that is good news. SimCLR strong augmentations can have improvements and other augmentations can also help if you want to have better performances. I remembered there is one ECCV'20 paper about the augmentation methods for videos. Also, good luch with your experiments~ |
Hi, thanks for the good work.
I'm currently trying to reproduce the action classification results on UCF101. Using the training parameters that you provided, I've trained my own backbone network and the linear classifier. However, I'm only getting a 0.68 accuracy with the RGB+Res+Repeat settup. I've also trained a linear classifier with the backbone network that you provided, I'm also only getting a accuracy around 0.685.
Could you please help me with this problem? Is there anything you think that could go wrong? Can you share the weights of your linear classifier network?
Thanks very much.
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