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Computer Science > Machine Learning

arXiv:2212.07143 (cs)
[Submitted on 14 Dec 2022 (v1), last revised 13 Jul 2024 (this version, v2)]

Title:Reproducible scaling laws for contrastive language-image learning

Authors:Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev
View a PDF of the paper titled Reproducible scaling laws for contrastive language-image learning, by Mehdi Cherti and 8 other authors
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Abstract:Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data \& models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study will be available at this https URL
Comments: CVPR 2023. Version with minor extension. Original: this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2212.07143 [cs.LG]
  (or arXiv:2212.07143v2 [cs.LG] for this version)
  https://j8ftcxlxtb.proxynodejs.usequeue.com/10.48550/arXiv.2212.07143
arXiv-issued DOI via DataCite
Journal reference: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 2818-2829
Related DOI: https://j8ftcxlxtb.proxynodejs.usequeue.com/10.1109/CVPR52729.2023.00276
DOI(s) linking to related resources

Submission history

From: Jenia Jitsev [view email]
[v1] Wed, 14 Dec 2022 10:24:50 UTC (1,394 KB)
[v2] Sat, 13 Jul 2024 14:20:07 UTC (1,402 KB)
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