ImageNet pre-trained models with batch normalization

Marcel Simon; Erik Rodner; Joachim Denzler

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arXiv · 1612.01452

ImageNet pre-trained models with batch normalization

Marcel Simon·Erik Rodner·Joachim Denzler

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Abstract

Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with popular state-of-the-art architectures for the Caffe framework. The first release includes Residual Networks (ResNets) with generation script as well as the batch-normalization-variants of AlexNet and VGG19. All models outperform previous models with the same architecture. The models and training code are available at http://www.inf-cv.uni-jena.de/Research/CNN+Models.html and https://github.com/cvjena/cnn-models

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Marcel Simon, Erik Rodner, Joachim Denzler. 2016-12-05. ImageNet pre-trained models with batch normalization. https://arxiv.org/abs/1612.01452

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