Image classification without normalization that is faster and better than with normalization
Image classification without normalization that is faster and better than with normalization
High-Performance Large-Scale Image Recognition Without Normalization
arXiv paper abstract https://arxiv.org/abs/2102.06171
arXiv PDF paper https://arxiv.org/pdf/2102.06171.pdf
Papers With Code https://paperswithcode.com/paper/high-performance-large-scale-image
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.
... a significantly improved class of Normalizer-Free ResNets.
... smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and
... largest models attain a new state-of-the-art top-1 accuracy of 86.5%.
... 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%. ...
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
Comments