Comments (3)
I would try the following things:
- Distributing on a single GPU
- Trying the text-only variant without pretrained checkpoint, which should already give non-zero performance
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Hi @antoyang,
Thanks for pointing out!
Please let me know if you can get expected results via the non-distribution way. Not too sure if this is due to my environment issue or some bugs in the implementation...
Cheers
from frozenbilm.
I have just verified that the performance of a pretrained checkpoint is the same without distributing. Some notes:
- It looks like you are trying to use a fine-tuned checkpoint and not the one that actually does zero-shot.
- Make sure that the weights of the language model from Hugging Face are loaded. Many of the checkpoints I host do not include these weights for storage issues, which should not be an issue as they are frozen so unchanged through cross-modal training.
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Related Issues (14)
- Errors in MSRVTT-QA test set HOT 1
- Error on zero-shot VQA HOT 7
- About the stage of pre-training HOT 3
- [Import Error] with demo_videoqa.py HOT 4
- Autoregressive Model Checkpoint HOT 1
- Bad zero-shot results on TVQA HOT 2
- Do you L2 Normalize the CLIP Features? HOT 1
- Problems in reproducing the code process HOT 1
- Verification code is need for downloading the data features HOT 9
- Problematic Tokennizer? HOT 3
- webvid_clipvitl14_features HOT 1
- Conda Environment Setting HOT 1
- Few-shot VideoQA training details HOT 1
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