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| | | 5. If you want to build with CUDNN to speed up then: |
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| | | |
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| | | * download and install CUDNN: https://developer.nvidia.com/cudnn |
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| | | * download and install **cuDNN 5.1 for CUDA 8.0**: https://developer.nvidia.com/cudnn |
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| | | |
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| | | * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg |
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| | | |
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| | |
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| | | |
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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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| | | |
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| | | `opencv_core249.dll`, `opencv_highgui249.dll` and `opencv_ffmpeg249_64.dll` in `C:\opencv_2.4.9\opencv\build\x64\vc12\bin` or `vc14\bin` |
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| | | |
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| | | ## How to train (Pascal VOC Data): |
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| | | |
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| | |
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| | | * after launching your console application and entering the image file name - you will see info for each object: |
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| | | `<obj_id> <left_x> <top_y> <width> <height> <probability>` |
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| | | * to use simple OpenCV-GUI you should uncomment line `//#define OPENCV` in `yolo_console_dll.cpp`-file: [link](https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5) |
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| | | |
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| | | `yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L31) |
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| | | ``` |
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| | | class Detector { |
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| | | public: |
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| | | Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); |
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| | | ~Detector(); |
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| | | |
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| | | std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2); |
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| | | std::vector<bbox_t> detect(image_t img, float thresh = 0.2); |
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| | | |
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| | | #ifdef OPENCV |
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| | | std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2); |
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| | | #endif |
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| | | }; |
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| | | ``` |
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