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| | | `C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include` |
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| | | - (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 |
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| | | - add to project all .c & .cu files from `\src` |
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| | | - add to project all `.c` & `.cu` files and file `http_stream.cpp` from `\src` |
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| | | - (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: |
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| | | `C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` |
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| | | More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc |
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| | | **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. |
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| | | ## How to train with multi-GPU: |
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| | | 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` |
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| | | `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -heigh 416` |
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| | | then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds |
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| | | * desirable that your training dataset include images with objects (without labels) that you do not want to detect - negative samples |
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| | | * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box |
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| | | |
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| | | * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file |
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