From 541fac19a98945d8dd6e68878d1e7e61f2299203 Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Sun, 15 Jan 2017 22:30:34 +0000 Subject: [PATCH] Update Readme.md --- README.md | 48 ++++++++++++++++++++++++++++++++++++++++++++---- 1 files changed, 44 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 794829b..72075c9 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ - +|  |  https://arxiv.org/abs/1612.08242 | +|---|---| + # Yolo-Windows v2 # "You Only Look Once: Unified, Real-Time Object Detection (version 2)" @@ -74,7 +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 @@ -83,6 +94,13 @@ 3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories 3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories + + 3.3 Open file: `\src\yolo.c` and change 3 lines to your OpenCV-version - `249` (for 2.4.9), `2413` (for 2.4.13), ... : + + * `#pragma comment(lib, "opencv_core249.lib")` + * `#pragma comment(lib, "opencv_imgproc249.lib")` + * `#pragma comment(lib, "opencv_highgui249.lib")` + 4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself. @@ -94,9 +112,9 @@ - (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here: `C:\opencv_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include` -- right click on project -> Build dependecies -> Build Customizations -> set check on CUDA 8.0 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg +- (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 8.0 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` -- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: +- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: `C:\opencv_2.4.9\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` - (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here: @@ -104,6 +122,12 @@ `..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)` - (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions +- open file: `\src\yolo.c` and change 3 lines to your OpenCV-version - `249` (for 2.4.9), `2413` (for 2.4.13), ... : + + * `#pragma comment(lib, "opencv_core249.lib")` + * `#pragma comment(lib, "opencv_imgproc249.lib")` + * `#pragma comment(lib, "opencv_highgui249.lib")` + `OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)` - compile to .exe (X64 & Release) and put .dll-s near with .exe: @@ -196,3 +220,19 @@ 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: + +Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2: https://github.com/AlexeyAB/Yolo_mark + +With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 -- Gitblit v1.10.0