From a7af0709cc4e032d4e0fee5a3fc4d6a6865d62a6 Mon Sep 17 00:00:00 2001 From: AlexeyAB <alexeyab84@gmail.com> Date: Tue, 13 Dec 2016 12:55:55 +0000 Subject: [PATCH] Added: train_voc.cmd --- README.md | 30 ++++++++++++++++-------------- 1 files changed, 16 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 56fe716..379d8fa 100644 --- a/README.md +++ b/README.md @@ -16,14 +16,18 @@ * **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") @@ -35,11 +39,13 @@ ##### 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 -* `darknet_net_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) +* `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` @@ -47,6 +53,7 @@ * 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: @@ -60,14 +67,9 @@ 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-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0 -``` -##### How to use COCO instead of VOC: - -* Get synset names from `build\darknet\x64\data\coco.names`: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/data/coco.names -* And change list `char *voc_names[] = ` to COCO-names in file `yolo.c`: https://github.com/AlexeyAB/darknet/blob/master/src/yolo.c#L30 +* 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: @@ -93,7 +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)` @@ -105,7 +107,7 @@ `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: -`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 -- Gitblit v1.10.0