name: 16-layer model from the arXiv paper: "Very Deep Convolutional Networks for Large-Scale Image Recognition"
caffemodel: VGG_ILSVRC_16_layers
caffemodel_url: http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
license: see http://www.robots.ox.ac.uk/~vgg/research/very_deep/
caffe_version: NVCaffe 0.16
gist_id: c07a629558dcc47fc2ba1bc01345c88e
This is a fork of original caffe model with modification for TensorRT inference.
The model is an improved version of the 16-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 D 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 8.1% (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 7.4% on the test set of ILSVRC-2012 (see Table 4 in the arXiv paper).