/darknet

Windows version of Yolo Convolutional Neural Networks

Primary LanguageCOtherNOASSERTION

Note, This repo is forked from https://github.com/AlexeyAB/darknet and in addition contains implementation of my new layers and custom modifications of the original darknet.

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. When should I stop training
  6. How to improve object detection
  7. How to mark bounded boxes of objects and create annotation files
  8. How to use Yolo as DLL
Darknet Logo   map_fps https://arxiv.org/abs/1612.08242
Darknet Logo   map_fps 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:
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

  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

1.1. Find files opencv_core249.dll, opencv_highgui249.dll and opencv_ffmpeg249_64.dll in C:\opencv_2.4.9\opencv\build\x64\vc12\bin or vc14\bin and put it near with darknet.exe

  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.

  2. If you want to build with CUDNN to speed up then:

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

opencv_core249.dll, opencv_highgui249.dll and opencv_ffmpeg249_64.dll in C:\opencv_2.4.9\opencv\build\x64\vc12\bin or vc14\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 will be created dir build\darknet\x64\data\voc\VOCdevkit\:

    2.1 Download file voc_label.py to dir build\darknet\x64\data\voc: http://pjreddie.com/media/files/voc_label.py

  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 #224 from filters=125 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 - in the same directory and 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
  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\

  • 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.

When should I stop training:

Usually sufficient 2000 iterations for each class(object). But for a more precise definition when you should stop training, use the following manual:

  1. During training, you will see varying indicators of error, and you should stop when no longer decreases 0.060730 avg:

Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8 Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8

9002: 0.211667, 0.060730 avg, 0.001000 rate, 3.868000 seconds, 576128 images Loaded: 0.000000 seconds

  • 9002 - iteration number (number of batch)
  • 0.060730 avg - average loss (error) - the lower, the better

When you see that average loss 0.060730 avg enough low at many iterations and no longer decreases then you should stop training.

  1. Once training is stopped, you should take some of last .weights-files from darknet\build\darknet\x64\backup and choose the best of them:

For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. Overfitting - is case when you can detect objects on images from training-dataset, but can't detect ojbects on any others images. You should get weights from Early Stopping Point:

Overfitting

2.1. At first, you should put filenames of validation images to file data\voc.2007.test (format as in train.txt) or if you haven't validation images - simply copy data\train.txt to data\voc.2007.test.

2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:

  • darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights
  • darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights
  • darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights

And comapre last output lines for each weights (7000, 8000, 9000):

7586 7612 7689 RPs/Img: 68.23 IOU: 77.86% Recall:99.00%

  • IOU - the bigger, the better (says about accuracy) - better to use
  • Recall - the bigger, the better (says about accuracy)

For example, bigger IUO gives weights yolo-obj_8000.weights - then use this weights for detection.

precision_recall_iou

Custom object detection:

Example of custom object detection: darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights

Yolo_v2_training Yolo_v2_training

How to improve object detection:

  1. Before training:
  • set flag random=1 in your .cfg-file - it will increase precision by training Yolo for different resolutions: link
  1. After training - for detection:

  • Increase network-resolution by set in your .cfg-file (height=608 and width=608) or (height=832 and width=832) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: link

    • you do not need to train the network again, just use .weights-file already trained for 416x416 resolution
    • if error Out of memory occurs then in .cfg-file you should increase subdivisions=16, 32 or 64: link

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

How to use Yolo as DLL

  1. To compile Yolo as C++ DLL-file yolo_cpp_dll.dll - open in MSVS2015 file build\darknet\yolo_cpp_dll.sln, set x64 and Release, and do the: Build -> Build yolo_cpp_dll

    • You should have installed CUDA 8.0
    • To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: CUDNN;

  2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file build\darknet\yolo_console_dll.sln, set x64 and Release, and do the: Build -> Build yolo_console_dll

    • you can run your console application from Windows Explorer build\darknet\x64\yolo_console_dll.exe
    • or you can run from MSVS2015 (before this - you should copy 2 files yolo-voc.cfg and yolo-voc.weights to the directory build\darknet\ )
    • after launching your console application and entering the image file name - you will see info for each object: <obj_id> <left_x> <top_y> <width> <height> <probability>
    • to use simple OpenCV-GUI you should uncomment line //#define OPENCV in yolo_console_dll.cpp-file: link

yolo_cpp_dll.dll-API: link

class Detector {
public:
	Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
	~Detector();

	std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2);
	std::vector<bbox_t> detect(image_t img, float thresh = 0.2);

#ifdef OPENCV
	std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2);
#endif
};