ConvNets Match Vision Transformers at Scale

Sam Smith, Andrew Brock, Leonard Berrada, Soham De

ConvNets Match Vision Transformers at Scale: 21 upvotes on Hugging Face Daily Papers, #7 of 13 papers on 2023-10-26. Day-by-day upvote history.

Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web-scale. We challenge this belief by evaluating a performant ConvNet architecture pre-trained on JFT-4B, a large labelled dataset of images often used for training foundation models. We consider pre-training compute budgets between 0.4k and 110k TPU-v4 core compute hours, and train a series of networks of increasing depth and width from the NFNet model family. We observe a log-log scaling law between held out loss and compute budget. After fine-tuning on ImageNet, NFNets match the reported performance of Vision Transformers with comparable compute budgets. Our strongest fine-tuned model achieves a Top-1 accuracy of 90.4%.

Paper page on Hugging Face · arXiv

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