ImageNet-21K Pretraining for the Masses

• URL: http://arxiv.org/abs/2104.10972v1
• Date: Thu, 22 Apr 2021 10:10:14 GMT
• Title: ImageNet-21K Pretraining for the Masses
• Authors: Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, Lihi Zelnik-Manor
• Abstract summary: ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset contains more pictures and classes. This paper aims to make high-quality efficient pretraining on ImageNet-21K available for everyone.
• Score: 12.339884639594624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which contains more pictures and classes, is used less frequently for pretraining, mainly due to its complexity, and underestimation of its added value compared to standard ImageNet-1K pretraining. This paper aims to close this gap, and make high-quality efficient pretraining on ImageNet-21K available for everyone. % Via a dedicated preprocessing stage, utilizing WordNet hierarchies, and a novel training scheme called semantic softmax, we show that various models, including small mobile-oriented models, significantly benefit from ImageNet-21K pretraining on numerous datasets and tasks. We also show that we outperform previous ImageNet-21K pretraining schemes for prominent new models like ViT. % Our proposed pretraining pipeline is efficient, accessible, and leads to SoTA reproducible results, from a publicly available dataset. The training code and pretrained models are available at: https://github.com/Alibaba-MIIL/ImageNet21K

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