# ImageNet pre-trained models with batch normalization

Source: [ImageNet pre-trained models with batch normalization](<https://www.sota2.com/research/paper/68fa62fe1e209131ce9fc19f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:30:18.740Z

Authors: Marcel Simon, Erik Rodner, Joachim Denzler

Published: 2016-12-05T17:49:41.000Z

## 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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1612.01452>)

[PDF](<https://arxiv.org/pdf/1612.01452.pdf>)