From e83d8e56d56978e5c2e571b8b4bf05b90a851ce0 Mon Sep 17 00:00:00 2001 From: AlexeyAB <alexeyab84@gmail.com> Date: Sun, 15 Jan 2017 22:31:55 +0000 Subject: [PATCH] Fixed paths to OpenCV from vc12 to vc14 --- README.md | 21 ++++++++++++++++++++- 1 files changed, 20 insertions(+), 1 deletions(-) diff --git a/README.md b/README.md index e86062a..72075c9 100644 --- a/README.md +++ b/README.md @@ -76,7 +76,16 @@ ### How to compile: -1. If you have CUDA 8.0, OpenCV 2.4.9 (C:\opencv_2.4.9) and MSVS 2015 then start MSVS, open `build\darknet\darknet.sln` and do the: Build -> Build darknet +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 @@ -211,6 +220,16 @@ 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` +9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` + + * 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: + +Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_3000.weights` + +|  |  | +|---|---| ## How to mark bounded boxes of objects and create annotation files: -- Gitblit v1.10.0