https://arxiv.org/abs/2102.06171

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2102.06171 (cs)
[Submitted on 11 Feb 2021]

Title:High-Performance Large-Scale Image Recognition Without
Normalization

Authors:Andrew Brock, Soham De, Samuel L. Smith, Karen Simonyan
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    Abstract: Batch normalization is a key component of most image
    classification models, but it has many undesirable properties
    stemming from its dependence on the batch size and interactions
    between examples. Although recent work has succeeded in training
    deep ResNets without normalization layers, these models do not
    match the test accuracies of the best batch-normalized networks,
    and are often unstable for large learning rates or strong data
    augmentations. In this work, we develop an adaptive gradient
    clipping technique which overcomes these instabilities, and
    design a significantly improved class of Normalizer-Free ResNets.
    Our smaller models match the test accuracy of an EfficientNet-B7
    on ImageNet while being up to 8.7x faster to train, and our
    largest models attain a new state-of-the-art top-1 accuracy of
    86.5%. In addition, Normalizer-Free models attain significantly
    better performance than their batch-normalized counterparts when
    finetuning on ImageNet after large-scale pre-training on a
    dataset of 300 million labeled images, with our best models
    obtaining an accuracy of 89.2%. Our code is available at this
    https URL deepmind-research/tree/master/nfnets

Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine
          Learning (cs.LG); Machine Learning (stat.ML)
Cite as:  arXiv:2102.06171 [cs.CV]
          (or arXiv:2102.06171v1 [cs.CV] for this version)

Submission history

From: Andrew Brock [view email]
[v1] Thu, 11 Feb 2021 18:23:20 UTC (241 KB)
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