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Darknet Logo   map_fps https://arxiv.org/abs/1612.08242

Yolo-Windows v2

"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:
Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):

Put it near compiled: darknet.exe

You can get cfg-files by path: darknet/cfg/

Examples of results:

Everything Is AWESOME

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

  1. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
  2. Start Smart WebCam on your phone
  3. 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\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;`

  1. 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

  2. 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")`
  1. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself.

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

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

  1. Create file obj.names in the directory build\darknet\x64\data\, with objects names - each in new line

  2. 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/

  1. Put image-files (.jpg) of your objects in the directory build\darknet\x64\data\obj\

  2. 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
  1. 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

  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. Start training by using the command line: darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23

  3. 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

Yolo_v2_training Yolo_v2_training

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