##Information
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: trained using a custom Caffe-based framework
gist_id: 211839e770f7b538e2d8
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).
Hi
I want to extract features from my own data set using this network, I have extracted features using caffe reference model as explained in http://caffe.berkeleyvision.org/gathered/examples/feature_extraction.html and it has worked just fine. I need to make an imagenet_val.prototxt as that of the caffe referece model which is exactly like its deploy.prototxt with the following lines:
name: "CaffeNet"
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
transform_param {
mirror: false
crop_size: 227
mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
}
image_data_param {
source: "examples/fe/file_list.txt"
batch_size: 10
new_height: 256
new_width: 256
}
}
at the begining instead of:
name: "CaffeNet"
input: "data"
input_shape {
dim: 10
dim: 3
dim: 227
dim: 227
}
so following this example I made an imagenet_val.prototxt file (available on https://gist.github.com/szm2015/37ce9f126d69bbafa5267f29b3e63336) out of the deploy.prototxt provided here. but when I ran the feature extraction command (./extract_features models/VGG_ILSVRC_16_layers/VGG_ILSVRC_16_layers.caffemodel examples/fe/imagenet_val.prototxt fc7 examples/fe/features 58 leveldb GPU) I got this error:
Unknown bottom blob 'data' (layer 'conv1_1', bottom index 0)
I searched and It seemed than the problem is with the old notation mixing with new one (layers instead of layer and DATA intstead of "Data" etc.) so I fixed it but still I get the same error (the gist provided is the fixed one).
I appreciate any help in advance and sorry if the comment is too long!