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ILSVRC-2014 model (VGG team) with 16 weight layers
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readme.md

##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

Description

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].

Caffe compatibility

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

ILSVRC-2012 performance

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).

Raw
VGG_ILSVRC_16_layers_deploy.prototxt
name: "VGG_ILSVRC_16_layers"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224
layers {
bottom: "data"
top: "conv1_1"
name: "conv1_1"
type: CONVOLUTION
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv1_1"
top: "conv1_1"
name: "relu1_1"
type: RELU
}
layers {
bottom: "conv1_1"
top: "conv1_2"
name: "conv1_2"
type: CONVOLUTION
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv1_2"
top: "conv1_2"
name: "relu1_2"
type: RELU
}
layers {
bottom: "conv1_2"
top: "pool1"
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool1"
top: "conv2_1"
name: "conv2_1"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv2_1"
top: "conv2_1"
name: "relu2_1"
type: RELU
}
layers {
bottom: "conv2_1"
top: "conv2_2"
name: "conv2_2"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv2_2"
top: "conv2_2"
name: "relu2_2"
type: RELU
}
layers {
bottom: "conv2_2"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool2"
top: "conv3_1"
name: "conv3_1"
type: CONVOLUTION
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_1"
top: "conv3_1"
name: "relu3_1"
type: RELU
}
layers {
bottom: "conv3_1"
top: "conv3_2"
name: "conv3_2"
type: CONVOLUTION
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_2"
top: "conv3_2"
name: "relu3_2"
type: RELU
}
layers {
bottom: "conv3_2"
top: "conv3_3"
name: "conv3_3"
type: CONVOLUTION
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_3"
top: "conv3_3"
name: "relu3_3"
type: RELU
}
layers {
bottom: "conv3_3"
top: "pool3"
name: "pool3"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool3"
top: "conv4_1"
name: "conv4_1"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_1"
top: "conv4_1"
name: "relu4_1"
type: RELU
}
layers {
bottom: "conv4_1"
top: "conv4_2"
name: "conv4_2"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_2"
top: "conv4_2"
name: "relu4_2"
type: RELU
}
layers {
bottom: "conv4_2"
top: "conv4_3"
name: "conv4_3"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_3"
top: "conv4_3"
name: "relu4_3"
type: RELU
}
layers {
bottom: "conv4_3"
top: "pool4"
name: "pool4"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool4"
top: "conv5_1"
name: "conv5_1"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_1"
top: "conv5_1"
name: "relu5_1"
type: RELU
}
layers {
bottom: "conv5_1"
top: "conv5_2"
name: "conv5_2"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_2"
top: "conv5_2"
name: "relu5_2"
type: RELU
}
layers {
bottom: "conv5_2"
top: "conv5_3"
name: "conv5_3"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_3"
top: "conv5_3"
name: "relu5_3"
type: RELU
}
layers {
bottom: "conv5_3"
top: "pool5"
name: "pool5"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool5"
top: "fc6"
name: "fc6"
type: INNER_PRODUCT
inner_product_param {
num_output: 4096
}
}
layers {
bottom: "fc6"
top: "fc6"
name: "relu6"
type: RELU
}
layers {
bottom: "fc6"
top: "fc6"
name: "drop6"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
}
layers {
bottom: "fc6"
top: "fc7"
name: "fc7"
type: INNER_PRODUCT
inner_product_param {
num_output: 4096
}
}
layers {
bottom: "fc7"
top: "fc7"
name: "relu7"
type: RELU
}
layers {
bottom: "fc7"
top: "fc7"
name: "drop7"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
}
layers {
bottom: "fc7"
top: "fc8"
name: "fc8"
type: INNER_PRODUCT
inner_product_param {
num_output: 1000
}
}
layers {
bottom: "fc8"
top: "prob"
name: "prob"
type: SOFTMAX
}
@roseperrone
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roseperrone commented Nov 30, 2014

Where can I find the train_val.prototxt and solver.prototxt?

@EvanWeiner
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EvanWeiner commented Dec 9, 2014

Is this supported in Caffe Master branch yet?

Also - any Python examples of using the .caffemodel?

@dabilied
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dabilied commented Dec 16, 2014

@EvanWeiner you can write py script under matcaffe_demo_vgg_mean_pix.m file in Dev , it runs well on Master

@EvanWeiner
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EvanWeiner commented Dec 19, 2014

@dabilied, thank you. What do you mean by write a script "under"?

@segalinc
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segalinc commented Jan 3, 2015

How to avoid protobuf warning due to too long message to read. I cannot read VGG_19_layers model

@whereaswhile
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whereaswhile commented Jan 11, 2015

@cristinasegalin you can change the upper limit in the following line:
coded_input->SetTotalBytesLimit(1073741824, 536870912);
but this function itself is limited by the maximum integer size

@JesperTornberg-tobii
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JesperTornberg-tobii commented Jan 28, 2015

Is there a train_val.prototxt and solver.prototxt file available?

@andresromero
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andresromero commented Feb 17, 2015

Does someone have the train_val.prototxt file?

@karpathy
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karpathy commented Mar 1, 2015

I also came looking for the train_val file for convenience, which doesn't seem to be available. I wrote this one for ImageNet, in case it's helpful to others: http://cs.stanford.edu/people/karpathy/vgg_train_val.prototxt

Note that I've zerod out all blobs_lr everywhere - presumably you want to build on this.
Set up this way and running this with

./build/tools/caffe train \
--solver=models/vgg/solver.prototxt \
--weights models/vgg/VGG_ILSVRC_16_layers.caffemodel

I get, on validation set:
Test net output #0: accuracy@1 = 0.683579
Test net output #1: accuracy@5 = 0.884442
Test net output #2: loss/loss = 1.30089 (* 1 = 1.30089 loss)

(i.e. top5 val error is 11.5% with no bells and whistles and single forward pass).
Swapping out the path to val lmdb to train lmdb, we can see also on training set:
Test net output #0: accuracy@1 = 0.77418
Test net output #1: accuracy@5 = 0.938281
Test net output #2: loss/loss = 0.852522 (* 1 = 0.852522 loss)

@jmendozais
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jmendozais commented Apr 6, 2015

Was someone able to train this model using a gpu with 4gb ram?. I just trained this model with batch size(bs) of 10, greater bs give me an out of memory error. I am running the model on an ec2 g2 instance.
Thanks in advance !!!

@wuxinhong
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wuxinhong commented Apr 8, 2015

@karpathy
could you give your VGG's solver.prototxt？

@databig
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databig commented Jun 15, 2015

@karpathy
could you give your VGG's solver.prototxt？

@jongsony
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jongsony commented Jul 17, 2015

@karpathy
could you give your VGG's solver.prototxt?

@Linzert
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Linzert commented Jul 30, 2015

@karpathy When I finetune by the way you provided,I got an error message below:
Check failed: ShapeEquals(proto) shape mismatch(reshape not set).
Could you tell me if you modify something else?

@Silver-Shen
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Silver-Shen commented Jul 31, 2015

I am trying to start training from VGG-A(from the paper we can find we should train from Configuration A to Configuration D(VGG16) or Configuration E(VGG19)), but the loss still not decrease, has anybody met the same problem?

@safwanwshah
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safwanwshah commented Aug 24, 2015

it looks that the weights and bias values are not initialized the file provided by karpathy

@YutingZhang
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YutingZhang commented Nov 3, 2015

@karpathy
I got pretty the same validation accuracy as you. However, in their paper ( http://arxiv.org/pdf/1409.1556.pdf ), it claims an accuracy of 100-27.0=73.0 (Table 3 - D).
I converted the fully connected layers to convolutional layers, convolved the whole network (224x224 input) on the 256x256 images, and fuse the predictions by sum-pooling. Do I miss anything?

@Venkatesh-Murthy
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Venkatesh-Murthy commented Nov 23, 2015

Even I tested it and got the same numbers as reported by @karpathy. I think what is missing is dense multi-scale evaluation procedure.

@mingminzhen
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mingminzhen commented Nov 24, 2015

why can not i get the matlab/caffe/matcaffe_demo_vgg.m? who can help paste the code?

@coldmanck
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coldmanck commented Dec 12, 2015

@mingminzhen: May be you can refer to this
@Linzert: Did you change the last layer's name to finetune the network?

@1292765944
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1292765944 commented Mar 5, 2016

the vgg 16caffemodel cannot be loaded by caffe?
the error is below:
F0305 21:19:47.159997 28198 upgrade_proto.cpp:75] Check failed: ReadProtoFromBinaryFile(param_file, param) Failed to parse NetParameter file: ../data/model/VGG16-NET/VGG_ILSVRC_16_layers.caffemodel
*** Check failure stack trace: ***
@ 0x7f34ac35b61c google::LogMessage::Fail()
@ 0x7f34ac35b568 google::LogMessage::SendToLog()
@ 0x7f34ac35af6a google::LogMessage::Flush()
@ 0x7f34ac35df01 google::LogMessageFatal::~LogMessageFatal()
@ 0x7f34ac827ebe caffe::ReadNetParamsFromBinaryFileOrDie()
@ 0x7f34ac72aa47 caffe::Net<>::CopyTrainedLayersFromBinaryProto()
@ 0x7f34ac72aab6 caffe::Net<>::CopyTrainedLayersFrom()
@ 0x406907 Classifier::Classifier()
@ 0x4082d7 main
@ 0x7f34ab886ec5 (unknown)
@ 0x406739 (unknown)

@szm-R
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szm-R commented May 14, 2016

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!

@toshi-k
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toshi-k commented May 22, 2016

I have a question about license.
In VGG web site, VGG model is provided by "CC BY 4.0" (commercial use is allowed).
http://www.robots.ox.ac.uk/~vgg/research/very_deep/

In this page, however, VGG model is provided by "CC BY-NC 4.0" (non-commercial use only).
Why is there difference, although caffemodel_URL is exactly the same ?

@mrgloom
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mrgloom commented Sep 18, 2016

Here is working example of VGG-16 that I have trained using NVIDIA DIGITS with Caffe backend.
https://github.com/mrgloom/kaggle-dogs-vs-cats-solution/tree/master/learning_from_scratch/Models/VGG-16

@lingchaoa
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lingchaoa commented Sep 25, 2017

Thanks

@RafaRuiz
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RafaRuiz commented Mar 9, 2018

Link is broken

@xiaoyanzhuo
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xiaoyanzhuo commented May 5, 2018

Previous link is broken and you can check this one:
https://github.com/mrgloom/kaggle-dogs-vs-cats-solution/tree/master/learning_from_scratch/VGG16

@burtonw0814
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burtonw0814 commented Jul 25, 2018

So I want to the use Keras implementation of this model, which links to this page.

Just to clarify, I understand the model was trained on pixel-wise mean-centered images.

But should the input images be in RGB format or BGR format?

Section 2.1 of the paper says "RGB" but the description section on this page uses the term "BGR".

@ZhihaoLZH
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ZhihaoLZH commented Feb 18, 2020

So I want to the use Keras implementation of this model, which links to this page.

Just to clarify, I understand the model was trained on pixel-wise mean-centered images.

But should the input images be in RGB format or BGR format?

Section 2.1 of the paper says "RGB" but the description section on this page uses the term "BGR".

I have the same question. Have you fixed it ?

@pablodz
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pablodz commented Jan 18, 2021

https://github.com/vadimkantorov/caffemodel2pytorch

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