| | |
|---|
| | | |
|---|
| | | `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 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` |
|---|
| | | - add to project all `.c`, `.cpp` & `.cu` files from `\src` |
|---|
| | | - (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: |
|---|
| | | |
|---|
| | | `C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` |
|---|
| | |
|---|
| | | |
|---|
| | | More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc |
|---|
| | | |
|---|
| | | **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. |
|---|
| | | |
|---|
| | | ## How to train with multi-GPU: |
|---|
| | | |
|---|
| | | 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` |
|---|
