ImageNet pre-trained models with batch normalization
This work provides incremental improvements for researchers and practitioners using Caffe by offering enhanced pre-trained models.
The authors tackled the problem of improving pre-trained convolutional neural networks for the Caffe framework by introducing new models with batch normalization, resulting in all models outperforming previous ones with the same architecture.
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