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[GH-ISSUE #400] Linear Probing ImageNet #342

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opened 2026-10-31 21:14:06 +00:00 by bsakaguchi · 1 comment
bsakaguchi commented 2026-10-31 21:14:06 +00:00
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Originally created by @tudorcebere on GitHub (Nov 12, 2023).
Original GitHub issue: https://github.com/openai/CLIP/issues/400

Hello! Thank you for this excellent model & paper!

I am interested in reproducing the linear probing results in the paper for ImageNet (using SGD). Can the authors provide some insights into how they achieve the results in Table 10 of the paper? My attempts using ViT-32 have significantly inferior test time performance, it seems that it fails to learn very badly. I have followed the example

Thank you!

Originally created by @tudorcebere on GitHub (Nov 12, 2023). Original GitHub issue: https://github.com/openai/CLIP/issues/400 Hello! Thank you for this excellent model & paper! I am interested in reproducing the linear probing results in the paper for ImageNet (using SGD). Can the authors provide some insights into how they achieve the results in Table 10 of the paper? My attempts using ViT-32 have significantly inferior test time performance, it seems that it fails to learn very badly. I have followed the example Thank you!
bsakaguchi commented 2026-10-31 21:14:07 +00:00
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@jongwook commented on GitHub (Nov 12, 2023):

Hi, we used full-batch linear regression using L-BFGS. For Imagenet with 1M+ images in the training split it was quite slow and requires huge memory especially considering the hyperparameter sweep for the L2 regularization term (C). Some discussions can be found in:

<!-- gh-comment-id:1807275926 --> @jongwook commented on GitHub (Nov 12, 2023): Hi, we used full-batch linear regression using L-BFGS. For Imagenet with 1M+ images in the training split it was quite slow and requires huge memory especially considering the hyperparameter sweep for the L2 regularization term (C). Some discussions can be found in: - https://github.com/openai/CLIP/issues/39#issuecomment-778034767 - https://github.com/openai/CLIP/issues/64#issuecomment-804444364
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