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| | | - (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions |
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| | | `OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)` |
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| | | - compile to .exe (X64 & Release) and put .dll`s near with .exe: |
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| | | - compile to .exe (X64 & Release) and put .dll-s near with .exe: |
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| | | `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64 |
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| | | `cusolver64_80.dll, curand64_80.dll, cudart64_80.dll, cublas64_80.dll` - 80 for CUDA 8.0 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin |
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| | | ## How to train (Pascal VOC Data): |
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| | | 1. Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory `build\darknet\x64` |
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| | | 2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc`: http://pjreddie.com/projects/pascal-voc-dataset-mirror/ will be created file `voc_label.py` and `\VOCdevkit\` dir |
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| | | 3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe |
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| | | 4. Run command: `python build\darknet\x64\data\voc\voc_label.py` (to generate files: 2007_test.txt, 2007_train.txt, 2007_val.txt, 2012_train.txt, 2012_val.txt) |
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| | | 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` |
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| | | 6. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` |
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| | | If required change pathes in the file `build\darknet\x64\data\voc.data` |
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| | | More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc |
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