Reproduction of paper results (pretraining on UCF101)

[mertesdorfj]

Hello!

I´m trying to reproduce the results of your paper as a baseline for my thesis. However, I´m not able to reach the same results for pretraining on UCF101 as indicated in tables 1 & 3 (81.2% top-1 accuracy on UCF). Did you use the same hyperparameter setup for UCF and kinetics-pretraining? (i.e. 200 epochs, I3D architecture, crop size 224, batch size 64, learning rate 0.01, weight decay 0.0001)

Also, I´m a bit confused about 2 things:

1. Which lr-scheduler did you use for pretraining? (The Repo readme says cos-scheduler, your paper supplementary materials however mention you used a step-scheduler with lr-decay at epochs 120 and 180)
2. Are you using MLP projection heads to achieve the paper results? According to the paper, you use 2-layer MLP heads, but for I3D, the MLP-heads are disabled. Is there a specific reason to disable the MLP heads only for the I3D architecture?

Thanks in advance for your help!

[hzjian123]

Hi, I experienced the same issue here. May I ask what accuracy you can obtain with UCF pretrain+finetune?

[mertesdorfj]

Sure! With the hyperparameters as mentioned in the paper and the step-scheduler (lr-reduction at epochs 120 & 180) I reached 78.55% Top1-accuracy after pretraining + finetuning on UCF101. However, I was able to achieve 80.56% Top1-accuracy after changing the step-scheduler to reduce the learning rate at epoch 160 instead of 120.

[Mark12Ding]

Sorry for the lateness. If I remember correctly, the step-scheduler is adopted for UCF-101 pertaining. For I3D, there is an embedded MLP layer if you turn off the with_classifier flag here.

For the difficulty of the reproduction, I recommend tuning the hyper-parameter at fine-tuning stage instead of the pretrain stage, e.g., learning rate, dropout rate, weight decay, etc.