我正在基于here的开源工作进行年龄分类 python代码有
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))
其中.prototxt
文件显示如下。我仍然是一个".caffemodel"
的文件。作为源代码,他之前提供过它。但是,我想基于我的面部数据库再次创建它。你有任何教程或某种方式来创建它吗?我假设我有一个包含100张图像的文件夹图像,并且划分属于每个年龄组(1到1),例如
image1.png 1
image2.png 1
..
image10.png 1
image11.png 2
image12.png 2
...
image100.png 10
这是原型文件。提前致谢
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"
}
答案 0 :(得分:3)
要获得caffemodel,您需要训练网络。该prototxt文件仅用于部署模型,不能用于训练它。
您需要添加指向数据库的数据层。要使用你提到的文件列表,图层的来源应该是HDF5。您可能希望添加具有平均值的transform_param。出于效率目的,可以使用LMDB或LevelDB数据库替换映像文件。
在网络结束时,您将不得不替换' prob'有损失的图层'层。像这样:
layers { 名称:"损失" 类型:SoftmaxWithLoss 底部:" fc8" 顶部:"损失" }
可以在此处找到图层目录:
http://caffe.berkeleyvision.org/tutorial/layers.html
或者,因为您的网络是众所周知的...只需看看本教程:P。
http://caffe.berkeleyvision.org/gathered/examples/imagenet.html
正确的培训原型文件包含在caffe中(' train_val.prototxt')。