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Will there be code release for fine tuning or training? #3

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sanmeow opened this issue Jan 6, 2021 · 3 comments
Closed

Will there be code release for fine tuning or training? #3

sanmeow opened this issue Jan 6, 2021 · 3 comments

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@sanmeow
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sanmeow commented Jan 6, 2021

Hi, this is a super great work!
Are there going to be anyway to fine tune this model for personal datasets?
@jongwook
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jongwook commented Jan 7, 2021

Thanks!

Fine-tuning should be possible with the released model. If you do the forward pass without the torch.no_grad() block, the resulting logits will have the gradients required for fine-tuning. You'll want to use the softmax cross entropy with the correct pairing (i.e. the identity matrix) as the target.

@sanmeow
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sanmeow commented Jan 7, 2021

Thank!I will try it out!

@sanmeow sanmeow closed this as completed Jan 7, 2021
@launchauto
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Thank!I will try it out!

Do you success?

@nishanthcgit nishanthcgit mentioned this issue Feb 1, 2021
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or-toledano added a commit to or-toledano/CLIP that referenced this issue Aug 9, 2021
@or-toledano
Reduce size of diff openai#3
c981368
jongwook pushed a commit to or-toledano/CLIP that referenced this issue Aug 9, 2021
@or-toledano @jongwook
Reduce size of diff openai#3
a2c8ae2
jongwook added a commit that referenced this issue Aug 9, 2021
@or-toledano @kcosta42 @jongwook
Update Prompt_Engineering_for_ImageNet.ipynb (#137)
fcab8b6 
* Can specify root directory when loading model

* specifying download_root instead

* Update Prompt_Engineering_for_ImageNet.ipynb

Fix bug caused by changing default to jit=False with handling the case jit=True as well

* Reduce size of diff

* Reduce size of diff #2

* Reduce size of diff #3

* updated Interacting_with_CLIP.ipynb

* update Prompt_Engineering_for_ImageNet.ipynb

Co-authored-by: kcosta42 <kcosta@student.42.fr>
Co-authored-by: Jong Wook Kim <jongwook@openai.com>
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@jongwook @sanmeow @launchauto