@article{,
title= {ImageNet-21K-P dataset (processed from fall11_whole.tar)},
journal= {},
author= {https://arxiv.org/pdf/2104.10972},
year= {},
url= {},
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 different 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.},
keywords= {imagenet, deep learning, imagenet21K, imagenet-21K, pretraining, fall11_whole.tar},
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license= {},
superseded= {}
}