I am working in age classification based on the opensource at here The python code has
age_net_pretrained='./age_net.caffemodel'
age_net_model_file='./deploy_age.prototxt'
age_net = caffe.Classifier(age_net_model_file, age_net_pretrained,
channel_swap=(2,1,0),
raw_scale=255,
image_dims=(256, 256))
In which .prototxt
file is shown as below. I remain one file that is ".caffemodel"
. As the source code, he provided it before. However, I would like to create it again based on my face database. Could you have any tutorial or some way to create it? I assume that I have a folder image that include 100 images and divided belongs to each age groups (1 to 1) such as
image1.png 1
image2.png 1
..
image10.png 1
image11.png 2
image12.png 2
...
image100.png 10
This is prototxt file. Thanks in advance
name: "CaffeNet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 227
input_dim: 227
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 7
stride: 4
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm1"
type: LRN
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "norm1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm2"
type: LRN
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layers{
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 512
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 512
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 8
}
}
layers {
name: "prob"
type: SOFTMAX
bottom: "fc8"
top: "prob"
}
To get a caffemodel you need to train the network. That prototxt file is only to deploy the model and cannot be used to train it.
You need to add a data layer that points to your database. To use a list of files as you mention, the source of the layer should be HDF5. You will probably want to add a transform_param with the mean value. The image files can be replaced by a LMDB or LevelDB database for efficiency purposes.
At the end of the network you will have to substitute the 'prob' layer with a 'loss' layer. Something like this:
layers { name: "loss" type: SoftmaxWithLoss bottom: "fc8" top: "loss" }
The layer catalogue can be found here:
http://caffe.berkeleyvision.org/tutorial/layers.html
Or, as your network is a well known one... just look at this tutorial :P.
http://caffe.berkeleyvision.org/gathered/examples/imagenet.html
The correct prototxt file for training is included in caffe ('train_val.prototxt').