| | |
|---|
| | | |
|---|
| | | 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 |
|---|
| | |
|---|
| | | |
|---|
| | | 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 |
|---|
| | |
|---|
| | | |
|---|
| | | 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: |
|---|
| | |
|---|
| | | |
|---|
| | | 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: |
|---|
