/darknet

Windows and Linux version of Darknet Yolo v2 Neural Networks for object detection

Primary LanguageCOtherNOASSERTION

Yolo-v2 Windows and Linux version

CircleCI

  1. How to use
  2. How to compile on Linux
  3. How to compile on Windows
  4. How to train (Pascal VOC Data)
  5. How to train (to detect your custom objects)
  6. When should I stop training
  7. How to calculate mAP on PascalVOC 2007
  8. How to improve object detection
  9. How to mark bounded boxes of objects and create annotation files
  10. Using Yolo9000
  11. 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 cross-platform Windows and Linux 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/

This repository supports:

  • both Windows and Linux
  • both OpenCV 3.x and OpenCV 2.4.13
  • both cuDNN v5-v7
  • CUDA >= 7.5
  • also create SO-library on Linux and DLL-library on Windows
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 194 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 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4
  • darknet_demo_store.cmd - initialization with 194 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: res.avi
  • darknet_net_cam_voc.cmd - initialization with 194 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone)
  • darknet_web_cam_voc.cmd - initialization with 194 MB VOC-model, play video from Web-Camera number #0
  • darknet_coco_9000.cmd - initialization with 186 MB Yolo9000 COCO-model, and show detection on the image: dog.jpg
  • darknet_coco_9000_demo.cmd - initialization with 186 MB Yolo9000 COCO-model, and show detection on the video (if it is present): street4k.mp4, and store result to: res.avi
How to use on the command line:

On Linux use ./darknet instead of darknet.exe, like this:./darknet detector test ./cfg/coco.data ./cfg/yolo.cfg ./yolo.weights

  • 194 MB COCO-model - image: darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2
  • Alternative method 194 MB COCO-model - image: darknet.exe detect yolo.cfg yolo.weights -i 0 -thresh 0.2
  • 194 MB VOC-model - image: darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0
  • 194 MB COCO-model - video: darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0
  • 194 MB VOC-model - video: darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0
  • 194 MB COCO-model - save result to the file res.avi: darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0 -out_filename res.avi
  • 194 MB VOC-model - save result to the file res.avi: darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi
  • Alternative method 194 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
  • 194 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
  • 194 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
  • 194 MB VOC-model - WebCamera #0: darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0
  • 186 MB Yolo9000 - image: darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights
  • 186 MB Yolo9000 - video: darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4
  • Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
  • To process a list of images image_list.txt and save results of detection to result.txt use:
    darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights < image_list.txt > result.txt You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509
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:

  • 194 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
  • 194 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 on Linux:

Just do make in the darknet directory. Before make, you can set such options in the Makefile: link

  • GPU=1 to build with CUDA to accelerate by using GPU (CUDA should be in /usr/local/cuda)
  • CUDNN=1 to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in /usr/local/cudnn)
  • OPENCV=1 to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams
  • DEBUG=1 to bould debug version of Yolo
  • OPENMP=1 to build with OpenMP support to accelerate Yolo by using multi-core CPU
  • LIBSO=1 to build a library darknet.so and binary runable file uselib that uses this library. Or you can try to run so LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4 How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp

How to compile on Windows:

  1. If you have MSVS 2015, CUDA 9.1 and OpenCV 3.0 (with paths: C:\opencv_3.0\opencv\build\include & C:\opencv_3.0\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. Find files opencv_world320.dll and opencv_ffmpeg320_64.dll in C:\opencv_3.0\opencv\build\x64\vc14\bin and put it near with darknet.exe

  2. If you have other version of CUDA (not 9.1) then open build\darknet\darknet.vcxproj by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1

  3. If you don't have GPU, but have MSVS 2015 and OpenCV 3.0 (with paths: C:\opencv_3.0\opencv\build\include & C:\opencv_3.0\opencv\build\x64\vc14\lib), then start MSVS, open build\darknet\darknet_no_gpu.sln, set x64 and Release, and do the: Build -> Build darknet_no_gpu

  4. If you have OpenCV 2.4.13 instead of 3.0 then you should change pathes after \darknet.sln is opened

    4.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: C:\opencv_2.4.13\opencv\build\include

    4.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: C:\opencv_2.4.13\opencv\build\x64\vc14\lib

  5. 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 9.1 and OpenCV 3.0

Then add to your created project:

  • (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:

C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include

C:\opencv_3.0\opencv\build\x64\vc14\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

OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;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_91.dll, curand64_91.dll, cudart64_91.dll, cublas64_91.dll - 91 for CUDA 9.1 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin

    • For OpenCV 3.2: opencv_world320.dll and opencv_ffmpeg320_64.dll from C:\opencv_3.0\opencv\build\x64\vc14\bin

    • For OpenCV 2.4.13: opencv_core2413.dll, opencv_highgui2413.dll and opencv_ffmpeg2413_64.dll from C:\opencv_2.4.13\opencv\build\x64\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. Set batch=64 and subdivisions=8 in the file yolo-voc.2.0.cfg: link

  7. Start training by using train_voc.cmd or by using the command line: darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23 (Note: If you are using CPU, try darknet_no_gpu.exe instead of darknet.exe.)

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.2.0.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.2.0.cfg /backup/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.2.0.cfg (or copy yolo-voc.2.0.cfg to yolo-obj.cfg) and:
  • change line batch to batch=64
  • change line subdivisions to subdivisions=8
  • change line classes=20 to your number of objects
  • change line #237 from filters=125 to: filters=(classes + 5)x5, so if classes=2 then should be filters=35. Or if you use classes=1 then write filters=30, do not write in the cfg-file: filters=(classes + 5)x5.

(Generally filters depends on the classes, num and coords, i.e. equal to (classes + coords + 1)*num, where num is number of anchors)

So for example, for 2 objects, your file yolo-obj.cfg should differ from yolo-voc.2.0.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  = data/train.txt
valid  = data/test.txt
names = data/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

    (file yolo-obj_xxx.weights will be saved to the build\darknet\x64\backup\ for each 100 iterations)

  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.

How to train tiny-yolo (to detect your custom objects):

Do all the same steps as for the full yolo model as described above. With the exception of:

  • Download default weights file for tiny-yolo-voc: http://pjreddie.com/media/files/tiny-yolo-voc.weights
  • Get pre-trained weights tiny-yolo-voc.conv.13 using command: darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13
  • Make your custom model tiny-yolo-obj.cfg based on tiny-yolo-voc.cfg instead of yolo-voc.2.0.cfg
  • Start training: darknet.exe detector train data/obj.data tiny-yolo-obj.cfg tiny-yolo-voc.conv.13

For training Yolo based on other models (DenseNet201-Yolo or ResNet50-Yolo), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.

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.xxxxxx avg no longer decreases at many iterations 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

To get weights from Early Stopping Point:

2.1. At first, in your file obj.data you must specify the path to the validation dataset valid = valid.txt (format of valid.txt as in train.txt), and if you haven't validation images, just copy data\train.txt to data\valid.txt.

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

(If you use another GitHub repository, then use darknet.exe detector recall... instead of darknet.exe detector map...)

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

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

Choose weights-file with the highest IoU (intersect of union) and mAP (mean average precision)

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

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

  • IoU (intersect of union) - average instersect of union of objects and detections for a certain threshold = 0.24

  • mAP (mean average precision) - mean value of average precisions for each class, where average precision is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf

In terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but IoU always has the same meaning.

precision_recall_iou

How to calculate mAP on PascalVOC 2007:

  1. To calculate mAP (mean average precision) on PascalVOC-2007-test:

(The article specifies the value of mAP = 76.8% for YOLOv2 416×416, page-4 table-3: https://arxiv.org/pdf/1612.08242v1.pdf. We get values lower - perhaps due to the fact that the model was trained on a slightly different source code than the code on which the detection is was done)

  • if you want to get mAP for tiny-yolo-voc.cfg model, then un-comment line for tiny-yolo-voc.cfg and comment line for yolo-voc.cfg in the .cmd-file
  • if you have Python 2.x instead of Python 3.x, and if you use Darknet+Python-way to get mAP, then in your cmd-file use reval_voc.py and voc_eval.py instead of reval_voc_py3.py and voc_eval_py3.py from this directory: https://github.com/AlexeyAB/darknet/tree/master/scripts

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:
  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

Using Yolo9000

Simultaneous detection and classification of 9000 objects:

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 9.1
    • 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
    • you can see source code of simple example for detection on the video 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, bool use_mean = false);
	std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
	static image_t load_image(std::string image_filename);
	static void free_image(image_t m);

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