Comments (10)
And I try to retrain the model, but the results can not reach the result of your paper, where is the problem?
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How do you RE-SCALE the feature to 100?
I reproduced the results on more than 2 machines and made the screenshot of the performance available.
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When I used the feature, there were not so many choices.
There were only initial I3D features, and I had to re-scale them.
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I use this function to re-scale to 100.
Is this correct?
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Is it related to batch size? I can't set 128 batch size on one gpu, I can only set 32 or 64 batch
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Maybe you can try 128 batch size with DP,
but I do not think batch size will affect the evaluation performance.
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I find in your code, the temporal_scale is not used?
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https://github.com/MCG-NJU/RTD-Action/blob/ANet/datasets/anet.py#L68-L71
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I will upload my re-scaled features.
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Thanks for your reply !
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Related Issues (20)
- mAP evaluation HOT 3
- RTD code for ActivityNet-1.3 HOT 4
- from datasets import build_dataset ;from models import build_model HOT 1
- I have a problem HOT 1
- A question about reproducibility~
- a question for training HOT 1
- TEM_scores HOT 5
- labe issue HOT 4
- about activitynet feature HOT 4
- about anet code HOT 4
- a problem HOT 2
- about thumos14 annotation HOT 3
- Results HOT 3
- Hello, may I ask, in the paper, the training is carried out in two stages, but in the code at that time, the training is carried out in three stages, why? How do they correspond to each other HOT 2
- ActivityNet HOT 8
- about TEM HOT 1
- about RTD in Anet HOT 1
- temporal action detection HOT 1
- how to change the stride of THUMOS features
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