windows vs2013 python2.7 matlab2014以上
添加upsample 、BN层。支持Segnet，添加Segnet-C++、python接口demo
打开windows-Caffe.sln编译即可，其他编译问题同微软官方caffe步骤
添加了cpm层,输入层数据可以旋转变换
使用方法参考：https://github.com/CMU-Perceptual-Computing-Lab/caffe_train
这个没有DenseImageDataLayer，不过可以如下代替：
data 和label分开输入
layer {
name: "data"
type: "Data"
top:"data"
include {
phase: TRAIN
}
transform_param {
mean_file: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_train_mean.binaryproto"
}
data_param {
source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_train"
batch_size: 1
backend: LMDB
}
}
layer {
name: "label"
type: "Data"
top:"label"
include {
phase: TRAIN
}
data_param {
source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Label_train"
batch_size: 1
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
include {
phase: TEST
}
transform_param {
mean_file: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_val_mean.binaryproto"
}
data_param {
source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_val"
batch_size: 1
backend: LMDB
}
}
layer {
name: "label"
type: "Data"
top: "label"
include {
phase: TEST
}
data_param {
source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Label_val"
batch_size: 1
backend: LMDB
}
}
这里上传一个简化版本的训练测试工程，解压后放在D盘即可，训练在C++训练。
链接：http://pan.baidu.com/s/1skY2wwd 密码：kxdx
copyright @Qingsong Liu