Comments (2)
kaethebu
commented on August 15, 2024
Hey, have you solved your binary classification problem? I'm also working with MoViNets and binary classification atm and have a few questions.
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patrick620
commented on August 15, 2024
Hi, using (2+1)D convolution can reduce computation compared with 3D convolution. But I think the stream model should be well designed to decrease the inference time. Also, the stream buffer is returned by the model and sent back to the model in TF version, but that is stored inside the model in this PyTorch implementation. Maybe TF version is a good choice?!
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Related Issues (20)
- Using MoViNet in a dataset with variable-length videos HOT 1
- why don't you use 'T.Normalize' when you train HMDB51? HOT 1
- Neural network arch displayed by Netron is wrong HOT 7
- Tips for Implementing a3 ,a4,a5 movinets streaming version HOT 3
- Test model based on 'evaluate_stream' is ok, but do inference frame by frame is very different? HOT 2
- Validation Loss did not decrease in the HMDB51 notebook? HOT 9
- Kinetics 400 models HOT 1
- Very low validation accuracy with pretrained models! HOT 1
- F.ToFloatTensorInZeroOne not exist HOT 2
- 。
- There seems no implementation of positional_encoding HOT 4
- How can we access the stream buffer? HOT 1
- need to process HMDB51 dataset?
- got wrong results during test
- weight
- The parameters that trained on Charades.
- Kinetics400/600
- Training
- Training on the custom dataset HOT 1
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