Update Readme.md

README.md

@@ -126,11 +126,11 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c
 
 ### How to compile on Windows:
 
-1. If you have **MSVS 2015, CUDA 8.0 and OpenCV 3.0** (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**, and do the: Build -> Build darknet
+1. If you have **MSVS 2015, CUDA 9.1 and OpenCV 3.0** (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**, and do the: Build -> Build darknet
 
     1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe`
 
-2. If you have other version of **CUDA (not 8.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 8.0" and change it to your CUDA-version, then do step 1
+2. If you have other version of **CUDA (not 9.1)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1
 
 3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (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_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu
 
@@ -150,13 +150,13 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c
 
 ### How to compile (custom):
 
-Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 8.0 and OpenCV 3.0
+Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 9.1 and OpenCV 3.0
 
 Then add to your created project:
 - (right click on project) -> properties  -> C/C++ -> General -> Additional Include Directories, put here: 
 
 `C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include`
-- (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 8.0 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 all .c & .cu files from `\src`
 - (right click on project) -> properties  -> Linker -> General -> Additional Library Directories, put here: 
 
@@ -172,9 +172,9 @@ Then add to your created project:
 
     * `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64
 
-    * `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
+    * `cusolver64_91.dll, curand64_91.dll, cudart64_91.dll, cublas64_91.dll` - 91 for CUDA 9.1 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin
 
-    * For OpenCV 3.X: `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` from `C:\opencv_3.0\opencv\build\x64\vc14\bin` 
+    * For OpenCV 3.2: `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` from `C:\opencv_3.0\opencv\build\x64\vc14\bin` 
     * For OpenCV 2.4.13: `opencv_core2413.dll`, `opencv_highgui2413.dll` and `opencv_ffmpeg2413_64.dll` from  `C:\opencv_2.4.13\opencv\build\x64\vc14\bin`
 
 ## How to train (Pascal VOC Data):
@@ -403,7 +403,7 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`
 ## How to use Yolo as DLL
 
 1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open in MSVS2015 file `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll
-    * You should have installed **CUDA 8.0**
+    * You should have installed **CUDA 9.1**
     * To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;`
 
 2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll