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Alexey
2017-01-16 13:04:48 +03:00
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@ -78,15 +78,6 @@ Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg
1. If you have MSVS 2015, CUDA 8.0 and OpenCV 2.4.9 (with paths: `C:\opencv_2.4.9\opencv\build\include` & `C:\opencv_2.4.9\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 If you want to build with CUDNN to speed up, then:
* download and install CUDNN: https://developer.nvidia.com/cudnn
* add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
* open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;`
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
3. If you have other version of OpenCV 2.4.x (not 2.4.9) then you should change pathes after `\darknet.sln` is opened
@ -104,6 +95,14 @@ Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg
4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself.
5. If you want to build with CUDNN to speed up then:
* download and install CUDNN: https://developer.nvidia.com/cudnn
* add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
* open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;`
### How to compile (custom):
Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 8.0 and OpenCV 2.4.9
@ -197,7 +196,8 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
Where:
* `<object-class>` - integer number of object from `0` to `(classes-1)`
* `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0
* `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0
* for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>`
* atention: `<x> <y>` - are center of rectangle (are not top-left corner)
For example for `img1.jpg` you should create `img1.txt` containing:
@ -222,6 +222,8 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\`
* After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights`
* Also you can get result earlier than all 45000 iterations, for example, usually sufficient 2000 iterations for each class(object). I.e. for 6 classes to avoid overfitting - you can stop training after 12000 iterations and use `yolo-obj_12000.weights` to detection.
### Custom object detection: