VGG-19 和 VGG-16 的 prototxt文件
VGG-19 和 VGG-16 的 prototxt文件
VGG-19 和 VGG-16 的 prototxt文件
VGG-16:
prototxt 地址：https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md
caffemodel 地址：http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
VGG-19:
prototxt 地址：https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md
caffemodel 地址：http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel
VGG_16.prototxt 文件：
name: "VGG_ILSVRC_19_layer"
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
image_data_param {
batch_size: 12
source: "../../fine_tuning_data/HAT_fineTuning_data/train_data_fineTuning.txt"
root_folder: "../../fine_tuning_data/HAT_fineTuning_data/train_data/"
}
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
}
image_data_param {
batch_size: 10
source: "../../fine_tuning_data/HAT_fineTuning_data/test_data_fineTuning.txt"
root_folder: "../../fine_tuning_data/HAT_fineTuning_data/test_data/"
}
}
layer {
bottom:"data"
top:"conv1_1"
name:"conv1_1"
type:"Convolution"
convolution_param {
num_output:64
pad:1
kernel_size:3
}
}
layer {
bottom:"conv1_1"
top:"conv1_1"
name:"relu1_1"
type:"ReLU"
}
layer {
bottom:"conv1_1"
top:"conv1_2"
name:"conv1_2"
type:"Convolution"
convolution_param {
num_output:64
pad:1
kernel_size:3
}
}
layer {
bottom:"conv1_2"
top:"conv1_2"
name:"relu1_2"
type:"ReLU"
}
layer {
bottom:"conv1_2"
top:"pool1"
name:"pool1"
type:"Pooling"
pooling_param {
pool:MAX
kernel_size:2
stride:2
}
}
layer {
bottom:"pool1"
top:"conv2_1"
name:"conv2_1"
type:"Convolution"
convolution_param {
num_output:128
pad:1
kernel_size:3
}
}
layer {
bottom:"conv2_1"
top:"conv2_1"
name:"relu2_1"
type:"ReLU"
}
layer {
bottom:"conv2_1"
top:"conv2_2"
name:"conv2_2"
type:"Convolution"
convolution_param {
num_output:128
pad:1
kernel_size:3
}
}
layer {
bottom:"conv2_2"
top:"conv2_2"
name:"relu2_2"
type:"ReLU"
}
layer {
bottom:"conv2_2"
top:"pool2"
name:"pool2"
type:"Pooling"
pooling_param {
pool:MAX
kernel_size:2
stride:2
}
}
layer {
bottom:"pool2"
top:"conv3_1"
name: "conv3_1"
type:"Convolution"
convolution_param {
num_output:256
pad:1
kernel_size:3
}
}
layer {
bottom:"conv3_1"
top:"conv3_1"
name:"relu3_1"
type:"ReLU"
}
layer {
bottom:"conv3_1"
top:"conv3_2"
name:"conv3_2"
type:"Convolution"
convolution_param {
num_output:256
pad:1
kernel_size:3
}
}
layer {
bottom:"conv3_2"
top:"conv3_2"
name:"relu3_2"
type:"ReLU"
}
layer {
bottom:"conv3_2"
top:"conv3_3"
name:"conv3_3"
type:"Convolution"
convolution_param {
num_output:256
pad:1
kernel_size:3
}
}
layer {
bottom:"conv3_3"
top:"conv3_3"
name:"relu3_3"
type:"ReLU"
}
layer {
bottom:"conv3_3"
top:"conv3_4"
name:"conv3_4"
type:"Convolution"
convolution_param {
num_output:256
pad:1
kernel_size:3
}
}
layer {
bottom:"conv3_4"
top:"conv3_4"
name:"relu3_4"
type:"ReLU"
}
layer {
bottom:"conv3_4"
top:"pool3"
name:"pool3"
type:"Pooling"
pooling_param {
pool:MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom:"pool3"
top:"conv4_1"
name:"conv4_1"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv4_1"
top:"conv4_1"
name:"relu4_1"
type:"ReLU"
}
layer {
bottom:"conv4_1"
top:"conv4_2"
name:"conv4_2"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv4_2"
top:"conv4_2"
name:"relu4_2"
type:"ReLU"
}
layer {
bottom:"conv4_2"
top:"conv4_3"
name:"conv4_3"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv4_3"
top:"conv4_3"
name:"relu4_3"
type:"ReLU"
}
layer {
bottom:"conv4_3"
top:"conv4_4"
name:"conv4_4"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv4_4"
top:"conv4_4"
name:"relu4_4"
type:"ReLU"
}
layer {
bottom:"conv4_4"
top:"pool4"
name:"pool4"
type:"Pooling"
pooling_param {
pool:MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom:"pool4"
top:"conv5_1"
name:"conv5_1"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv5_1"
top:"conv5_1"
name:"relu5_1"
type:"ReLU"
}
layer {
bottom:"conv5_1"
top:"conv5_2"
name:"conv5_2"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv5_2"
top:"conv5_2"
name:"relu5_2"
type:"ReLU"
}
layer {
bottom:"conv5_2"
top:"conv5_3"
name:"conv5_3"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv5_3"
top:"conv5_3"
name:"relu5_3"
type:"ReLU"
}
layer {
bottom:"conv5_3"
top:"conv5_4"
name:"conv5_4"
type:"Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom:"conv5_4"
top:"conv5_4"
name:"relu5_4"
type:"ReLU"
}
layer {
bottom:"conv5_4"
top:"pool5"
name:"pool5"
type:"Pooling"
pooling_param {
pool:MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom:"pool5"
top:"fc6_"
name:"fc6_"
type:"InnerProduct"
inner_product_param {
num_output: 4096
}
}
layer {
bottom:"fc6_"
top:"fc6_"
name:"relu6"
type:"ReLU"
}
layer {
bottom:"fc6_"
top:"fc6_"
name:"drop6"
type:"Dropout"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
bottom:"fc6_"
top:"fc7"
name:"fc7"
type:"InnerProduct"
inner_product_param {
num_output: 4096
}
}
layer {
bottom:"fc7"
top:"fc7"
name:"relu7"
type:"ReLU"
}
layer {
bottom:"fc7"
top:"fc7"
name:"drop7"
type:"Dropout"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
bottom:"fc7"
top:"fc8_"
name:"fc8_"
type:"InnerProduct"
inner_product_param {
num_output: 43
}
}
layer {
name: "sigmoid"
type: "Sigmoid"
bottom: "fc8_"
top: "fc8_"
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8_"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "fc8_"
bottom: "label"
top: "loss"
}
name: "VGG_ILSVRC_16_layer"
layers {
name: "data"
type: IMAGE_DATA
top: "data"
top: "label"
include {
phase: TRAIN
} image_data_param {
batch_size: 80
source: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/Sun_100_Labeled_Train_0.5_.txt"
root_folder: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/train_image_sun_256_256/"
new_height: 224
new_width: 224
}
} layers {
name: "data"
type: IMAGE_DATA
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
}
image_data_param {
batch_size: 10
source: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/Sun_100_Test_0.5_.txt"
root_folder: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/test_image_sun_227_227/"
new_height:224
new_width: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: 88
}
}
layers {
name: "accuracy"
type: ACCURACY
bottom: "fc8_"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layers{
name: "loss"
type: SOFTMAX_LOSS
bottom: "fc8_"
bottom: "label"
top: "loss"
}
VGG-19 和 VGG-16 的 prototxt文件的更多相关文章
- Web 在线文件管理器学习笔记与总结（15）剪切文件夹 （16）删除文件夹
(15)剪切文件夹 ① 通过rename($oldname,$newname) 函数实现剪切文件夹的操作 ② 需要检测目标文件夹是否存在,如果存在还要检测目标目录中是否存在同名文件夹,如果不存在则剪切 ...
- HTTP 错误 500.19 – Internal Server Error web.config 文件的 system.webServer/httpErrors 节中不允许绝对物理路径“C:\inetpub\custerr”[转]
给ASP或者ASP.NET等需要配置IIS服务器的过程中,很可能会遇到以下两种错误.尤其是用Win7系统的,配置IIS7.0版本比用XP系统配置IIS5.1版本而言要复杂复杂一些.当同时需要配置ASP ...
- caffe生成deploy.prototxt文件
参考: http://blog.csdn.net/cham_3/article/details/52682479 以caffe工程自带的mnist数据集,lenet网络为例: 将lenet_train ...
- 根据 train_test.prototxt文件生成 deploy.prototxt文件
本文参考博文 (1)介绍 *_train_test.prototxt文件与 *_deploy.prototxt文件的不同:http://blog.csdn.net/sunshine_in_moon/a ...
- train_val.prototxt文件和deploy.prototxt文件开头的区别
1.开头不同 对train_val.prototxt文件来说,开头部分定义训练和测试的网络及参数 对deploy.prototxt文件来说,开头部分定义实际运用场景的配置文件,其参数不定义数据来源,仅 ...
- 浅谈caffe中train_val.prototxt和deploy.prototxt文件的区别
本文以CaffeNet为例: 1. train_val.prototxt 首先,train_val.prototxt文件是网络配置文件.该文件是在训练的时候用的. 2.deploy.prototxt ...
- C/C++ 读取16进制文件
1.为什么有这种需求 因为有些情况需要避免出现乱码.不管什么编码都是二进制的,这样表示为16进制就可以啦. 2.如何读取16进制文件 最近编程用这一问题,网上查了一下,感觉还是自己写吧. 16进制数据 ...
- 解析prototxt文件的python库 prototxt-parser(使用parsy自定义文件格式解析)
解析prototxt文件的python库 prototxt-parser https://github.com/yogin16/prototxt_parser https://test.pypi.or ...
- VGG 19
关于VGG19的一些参考资料 http://www.cnblogs.com/vipyoumay/archive/2017/11/23/7884472.html https://cloud.tencen ...
随机推荐
- SharePoint 列表应用实例 - 显示约束
博客地址:http://blog.csdn.net/FoxDave 有时会碰到这样的需求,比如上传周报到文档库,周报只能领导和自己看到,其他同事是看不到的.通常我们开发的人遇到这种情况条件反射地想到的 ...
- iOS: 枚举类型 enum,NS_ENUM,NS_OPTIONS
一般情况下,我们采用C风格的enum关键字可以定义枚举类型. enum{ UIViewAnimationTransitionNone, UIViewAnimationTransitionFlipFro ...
- Linux物理内存相关数据结构
节点:pg_data_t typedef struct pglist_data { zone_t node_zones[MAX_NR_ZONES]; zonelist_t node_zonelists ...
- PHP中的抽象类和接口
PHP抽象类和抽象方法的应用 抽象类 接口 多态 抽象类是以特殊的类,接口是一种特殊的抽象类声明抽象类和接口,以及一些需要的技术抽象类: 什么是抽象方法? 定义:如果一个类中的方法 ...
- HDU 4888 (网络流）
Poroblem Redraw Beautiful Drawings (HDU4888) 题目大意 一个n行m列的矩形,只能填0~k的数字. 给定各行各列的数字和,判定有无合法的方案数.一解给出方案, ...
- 如何运用inno在安装和卸载时提示用户结束进程？
我尝试着写了一段,但是卸载段存在问题,请指点! CODE [Files]Source: ISTask.dll; DestDir: {app}; Flags: ignoreversion [Code]f ...
- 第二个Sprint冲刺第七天
讨论地点:宿舍 讨论成员:邵家文.李新.朱浩龙.陈俊金 讨论:整理已完成的功能
- JS基础知识（-）
1 , call方法: 语法:call([thisObj[,arg1[, arg2[, [,.argN]]]]]) 定义:调用一个对象的一个方法,以另一个对象替换当前对象. 说明: call 方法 ...
- [转]CentOS6.3安装JDK和环境配置
转自:http://www.linuxidc.com/Linux/2012-09/70780.htm 1.CentOS默认情况下,会安装OpenOffice之类的软件,这些软件需要Java的支持,默认 ...
- Jquery.KinSlideshow图片轮播插件
KinSlideshow无缝滑动幻灯片jquery特效代码Jquery幻灯片特效jquery.KinSlideshow-1.1.js 兼容IE6/IE7/IE8/IE9,FireFox,Chrome* ...