From b5938098d12d5c9fe48e7cc71ae3d75b7306833f Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Mon, 02 Jan 2017 12:33:31 +0000 Subject: [PATCH] Update Readme.md - pragma-libs in How to compile --- README.md | 90 ++++++++++++++++++++++++++++++++++++++++++-- 1 files changed, 85 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index f0205a5..e86062a 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)" @@ -83,6 +85,13 @@ 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 +103,9 @@ - (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 +113,12 @@ `..\..\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: @@ -132,8 +147,73 @@ ## How to train with multi-GPU: -1. Train it first on 1 gpu for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` +1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` -2. Then stop and run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23 -gpus 0,1,2,3` +2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.cfg yolo-voc_1000.weights -gpus 0,1,2,3` https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ + +## How to train (to detect your custom objects): + +1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.cfg` (or copy `yolo-voc.cfg` to `yolo-obj.cfg)` and: + + * change line `classes=20` to your number of objects + * change line `filters=425` to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) + + For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines: + + ``` + [convolutional] + filters=35 + + [region] + classes=2 + ``` + +2. Create file `obj.names` in the directory `build\darknet\x64\data\`, with objects names - each in new line + +3. Create file `obj.data` in the directory `build\darknet\x64\data\`, containing (where **classes = number of objects**): + + ``` + classes= 2 + train = train.txt + valid = test.txt + names = obj.names + backup = backup/ + ``` + +4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\` + +5. Create `.txt`-file for each `.jpg`-image-file - with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: `<object-class> <x> <y> <width> <height>` + + 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 + * atention: `<x> <y>` - are center of rectangle (are not top-left corner) + + For example for `img1.jpg` you should create `img1.txt` containing: + + ``` + 1 0.716797 0.395833 0.216406 0.147222 + 0 0.687109 0.379167 0.255469 0.158333 + 1 0.420312 0.395833 0.140625 0.166667 + ``` + +6. Create file `train.txt` in directory `build\darknet\x64\data\`, with filenames of your images, each filename in new line, with path relative to `darknet.exe`, for example containing: + + ``` + data/obj/img1.jpg + data/obj/img2.jpg + data/obj/img3.jpg + ``` + +7. Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory `build\darknet\x64` + +8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` + + +## 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