From b923019283d53dc93673faf282e65114aef80b6d Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Tue, 13 Dec 2016 13:23:16 +0000 Subject: [PATCH] Update Readme.md - how to train on Pascal VOC --- README.md | 67 ++++++++++++++++++++++++++------- 1 files changed, 53 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 90cecd0..8330081 100644 --- a/README.md +++ b/README.md @@ -16,24 +16,44 @@ * **OpenCV 2.4.9**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download - To compile without OpenCV - remove define OPENCV from: Visual Studio->Project->Properties->C/C++->Preprocessor - To compile with different OpenCV version - change in file yolo.c each string look like **#pragma comment(lib, "opencv_core249.lib")** from 249 to required version. - - With OpenCV will show image or video detection in window + - With OpenCV will show image or video detection in window and store result to: test_dnn_out.avi ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): -* `yolo.cfg` (256 MB) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights -* `yolo-tiny.cfg` (60 MB) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo-voc.weights +* `yolo.cfg` (256 MB COCO-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo.weights +* `yolo-voc.cfg` (256 MB VOC-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights +* `tiny-yolo.cfg` (60 MB COCO-model) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo.weights +* `tiny-yolo-voc.cfg` (60 MB VOC-model) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo-voc.weights Put it near compiled: darknet.exe +You can get cfg-files by path: `darknet/cfg/` + ##### Examples of results: [](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME") Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg -##### Example of usage in cmd-files: +### How to use: -* `darknet_demo_voc.cmd` - initialization with 256 MB model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4 -* `darknet_net_cam_voc.cmd` - initialization with 256 MB model, play video from network video-camera mjpeg-stream (also from you phone) +##### Example of usage in cmd-files from `build\darknet\x64\`: + +* `darknet_voc.cmd` - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file +* `darknet_demo_voc.cmd` - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: test_dnn_out.avi +* `darknet_net_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) and store result to: test_dnn_out.avi +* `darknet_web_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from Web-Camera number #0 and store result to: test_dnn_out.avi + +##### How to use on the command line: +* 256 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2` +* Alternative method 256 MB COCO-model - image: `darknet.exe detect yolo.cfg yolo.weights -i 0 -thresh 0.2` +* 256 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0` +* 256 MB COCO-model - video: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0` +* 256 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` +* Alternative method 256 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` +* 60 MB VOC-model for video: `darknet.exe detector demo data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights test.mp4 -i 0` +* 256 MB COCO-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +* 256 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +* 256 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0` ##### For using network video-camera mjpeg-stream with any Android smartphone: @@ -47,15 +67,16 @@ 3. Start Smart WebCam on your phone 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: -``` -darknet.exe yolo demo yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0 -``` + +* 256 MB COCO-model: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +* 256 MB VOC-model: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` + ### 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 `yolo-windows\build\darknet\darknet.sln` and do the: Build -> Build darknet +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 -2. If you have other version of CUDA (not 8.0) then open `darknet\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 +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 @@ -74,7 +95,7 @@ `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 -- add to project all .c & .cu files from yolo-windows\src +- add to project all .c & .cu files from `\src` - (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)` @@ -84,10 +105,28 @@ - (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions `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: +- compile to .exe (X64 & Release) and put .dll-s near with .exe: -`pthreadVC2.dll, pthreadGC2.dll` from yolo-windows\3rdparty\dll\x64 +`pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64 `cusolver64_80.dll, curand64_80.dll, cudart64_80.dll, cublas64_80.dll` - 80 for CUDA 8.0 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin +## How to train (Pascal VOC Data): + +1. 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` + +2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc`: http://pjreddie.com/projects/pascal-voc-dataset-mirror/ will be created file `voc_label.py` and `\VOCdevkit\` dir + +3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe + +4. Run command: `python build\darknet\x64\data\voc\voc_label.py` (to generate files: 2007_test.txt, 2007_train.txt, 2007_val.txt, 2012_train.txt, 2012_val.txt) + +5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` + +6. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` + +If required change pathes in the file `build\darknet\x64\data\voc.data` + +More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc + -- Gitblit v1.10.0