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Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

License: MIT License

Python 100.00%
artificial-intelligence deep-learning attention-mechanism transformers video-classification

stam-pytorch's Issues

The weights

Can you provide your models weight ? I would like to reproduce the experience of the paper.
Thanks,

Best regards,

regression

Beautiful work as usual, thanks for this implementation.

I'm curious if you tried using this for a regression task? I have tried using TimeSFormer without success yet, I know the signal is there because I can learn it with a small 3dcnn trained from scratch so I suspect my understanding of how and where to modify the transformer is the culprit. The output is a 1D vector with len == num_frames. Any suggestions very appreciated!

train loss curve is realy weird

Hi Thanks for the great work. I am trying to train your transformer with my video data but the train loss curve looks really weird and its increasing. I tried with normal video data of shape [3,256,256] in range 0-255 and in range 0-1. Could you please tell what I am doing wrong. My data contains a person doing something and I want to recognize the action.
loss(4)

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