### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows)
### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg)
* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/
* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/
* **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions))
* **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions))
* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or [OpenCV Releases](https://opencv.org/releases.html)
* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or [OpenCV Releases](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` where are `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png))
* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (set system variable `CUDNN` = `C:\cudnn` where did you unpack cuDNN. On Linux in `.bashrc`-file, on Windows see the [image](https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg) )
* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
* on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15#
* on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15#
1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include`&`C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).
1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include`&`C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).
1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe`
1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe`
* download and install **cuDNN v7.4.1 for CUDA 10.0**: https://developer.nvidia.com/rdp/cudnn-archive
* download and install **cuDNN v7.4.1 for CUDA 10.0**: https://developer.nvidia.com/rdp/cudnn-archive
* add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
* add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg
* copy file `cudnn64_7.dll` to the folder `\build\darknet\x64` near with `darknet.exe`
* copy file `cudnn64_7.dll` to the folder `\build\darknet\x64` near with `darknet.exe`
@ -226,7 +227,7 @@ Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example
Then add to your created project:
Then add to your created project:
- (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:
- (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:
- (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
- (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
- add to project:
- add to project:
* all `.c` files
* all `.c` files
@ -235,7 +236,7 @@ Then add to your created project:
* file `darknet.h` from `\include` directory
* file `darknet.h` from `\include` directory
- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
Alexey
6 years ago
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