##Information
name: 19-layer model from the arXiv paper: "Very Deep Convolutional Networks for Large-Scale Image Recognition"
caffemodel: VGG_ILSVRC_19_layers
caffemodel_url: http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel
license: see http://www.robots.ox.ac.uk/~vgg/research/very_deep/
caffe_version: trained using a custom Caffe-based framework
gist_id: 3785162f95cd2d5fee77
The model is an improved version of the 19-layer model used by the VGG team in the ILSVRC-2014 competition. The details can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
arXiv:1409.1556
Please cite the paper if you use the model.
In the paper, the model is denoted as the configuration E trained with scale jittering. The input images should be zero-centered by mean pixel (rather than mean image) subtraction. Namely, the following BGR values should be subtracted: [103.939, 116.779, 123.68].
The models are currently supported by the dev branch of Caffe, but are not yet compatible with master.
An example of how to use the models in Matlab can be found in matlab/caffe/matcaffe_demo_vgg.m
Using dense single-scale evaluation (the smallest image side rescaled to 384), the top-5 classification error on the validation set of ILSVRC-2012 is 8.0% (see Table 3 in the arXiv paper).
Using dense multi-scale evaluation (the smallest image side rescaled to 256, 384, and 512), the top-5 classification error is 7.5% on the validation set and 7.3% on the test set of ILSVRC-2012 (see Tables 4 and 6 in the arXiv paper).
Have anyone got the test accuracy reported? I can only get 73.034% TOP1 accuracy and 91.654% TOP5 accuracy. It is close, but still not exactly the result. Especially for the TOP1 accuracy, reported is 25.5% error rate which is 74.5% accuracy. It is 1.5% lower.
Any advice?