caffe實現年齡及性別預測
阿新 • • 發佈：2018-08-01
工程 stat pad play app 進入 end iat back
一、相關代碼及訓練好的模型
eveningglow/age-and-gender-classification: Age and Gender Classification using Convolutional Neural Network https://github.com/eveningglow/age-and-gender-classification
二、部署
1、打開Caffe.sln工程，編譯方法見：https://www.cnblogs.com/smbx-ztbz/p/9380273.html
2、將相關源文件及模型拷貝至如下目錄：
3、在examples中新建工程，且將對應源碼添加進來
4、屬性設置：
（1）進入“C/C++”，選中“常規”，“附加包含目錄”輸入如下：
D:\Projects\caffe_gpu\caffe\build\include D:\Projects\caffe_gpu\caffe\build C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\include\boost-1_61 C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\include C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\include\opencv D:\Projects\caffe_gpu\caffe\include C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\Include
其中tingpan改成自己電腦的用戶名。
（2） “C/C++” –>“預處理器”—> “預處理器定義”， 輸入如下：
WIN32 _WINDOWS NDEBUG CAFFE_VERSION=1.0.0 BOOST_ALL_NO_LIB USE_LMDB USE_LEVELDB USE_CUDNN USE_OPENCV CMAKE_WINDOWS_BUILD GLOG_NO_ABBREVIATED_SEVERITIES GOOGLE_GLOG_DLL_DECL=__declspec(dllimport) GOOGLE_GLOG_DLL_DECL_FOR_UNITTESTS=__declspec(dllimport) H5_BUILT_AS_DYNAMIC_LIB=1 CMAKE_INTDIR="Release"
（3）“鏈接器” –>”輸入” –>“附加依賴項”
kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib D:\Projects\caffe_gpu\caffe\build\install\lib\caffe.lib D:\Projects\caffe_gpu\caffe\build\install\lib\caffeproto.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\boost_system-vc140-mt-1_61.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\boost_thread-vc140-mt-1_61.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\boost_filesystem-vc140-mt-1_61.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\glog.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\Lib\gflags.lib shlwapi.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\libprotobuf.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\caffehdf5_hl.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\caffehdf5.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\cmake\..\lib\caffezlib.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\lmdb.lib ntdll.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\leveldb.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\cmake\..\lib\boost_date_time-vc140-mt-1_61.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\cmake\..\lib\boost_filesystem-vc140-mt-1_61.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\cmake\..\lib\boost_system-vc140-mt-1_61.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\snappy_static.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\caffezlib.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cudart.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\curand.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cublas.lib C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\cudnn.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\x64\vc14\lib\opencv_highgui310.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\x64\vc14\lib\opencv_imgcodecs310.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\x64\vc14\lib\opencv_imgproc310.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\x64\vc14\lib\opencv_core310.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\libopenblas.dll.a C:\Users\tingpan\AppData\Local\Programs\Python\Python35\libs\python35.lib C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\lib\boost_python-vc140-mt-1_61.lib
去掉勾選 “從父級或項目默認設置繼承”。其中tingpan改成自己電腦的用戶名。
（4）將D:\Projects\caffe_gpu\caffe\build\install\bin添加到環境變量。
5、編譯
如果出現一些錯誤，提示缺少dll庫文件，則從C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\x64\vc14\bin\或C:\Users\tingpan\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\bin\中拷貝對應的dll文件到D:\Projects\caffe_gpu\caffe\build\install\bin目錄下。
6、測試
參數輸入：
model/deploy_gender2.prototxt model/gender_net.caffemodel model/deploy_age2.prototxt model/age_net.caffemodel model/mean.binaryproto img/0008.jpg輸出結果如下：
caffe實現年齡及性別預測