Yolo-Windows v2

1. How to use
2. How to compile
3. How to train (Pascal VOC Data)
4. How to train (to detect your custom objects)
5. How to mark bounded boxes of objects and create annotation files

	https://arxiv.org/abs/1612.08242

"You Only Look Once: Unified, Real-Time Object Detection (version 2)"

A yolo windows version (for object detection)

Contributtors: https://github.com/pjreddie/darknet/graphs/contributors

This repository is forked from Linux-version: https://github.com/pjreddie/darknet

More details: http://pjreddie.com/darknet/yolo/

Requires:

• MS Visual Studio 2015 (v140): https://www.microsoft.com/download/details.aspx?id=48146
• CUDA 8.0 for Windows x64: https://developer.nvidia.com/cuda-downloads
• 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 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 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:

Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg

How to use:

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:

1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam

Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam

2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
3. Start Smart WebCam on your phone
4. Replace the address below, on shown in the phone application (Smart WebCam) and launch:

• 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 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\vc12\lib or vc14\lib), then start MSVS, open build\darknet\darknet.sln, set x64 and Release, and do the: Build -> Build darknet

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

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.

5. 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;

How to compile (custom):

Also, you can to create your own darknet.sln & darknet.vcxproj, this example for CUDA 8.0 and OpenCV 2.4.9

Then add to your created project:

• (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
• 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)

• (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:

..\..\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:

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

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

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
• for example: <x> = <absolute_x> / <image_width> or <height> = <absolute_height> / <image_height>
• 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

9. After training is complete - get result yolo-obj_final.weights from path build\darknet\x64\backup\

• After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy yolo-obj_2000.weights from build\darknet\x64\backup\ to build\darknet\x64\ and start training using: darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights

• 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, air1-6.txt, bird1-4.txt for 2 classes of objects (air, bird) and train_obj.cmd with example how to train this image-set with Yolo v2