Yolo-v3 and Yolo-v2 for Windows and Linux

(neural network for detection) - Tensor Cores can be used on Linux and Windows

\"CircleCI\"

  1. Improvements in this repository
  2. How to use
  3. How to compile on Linux
  4. How to compile on Windows
  5. How to train (Pascal VOC Data)
  6. How to train (to detect your custom s)
  7. When should I stop training
  8. How to calculate mAP on PascalVOC 2007
  9. How to improve detection
  10. How to mark bounded boxes of s and create annotation files
  11. Using Yolo9000
  12. How to use Yolo as DLL
\"Darknet   \"map_fps\" mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf

\"You Only Look Once: Unified, Real-Time Detection (versions 2 & 3)\"

A Yolo cross-platform Windows and Linux version (for 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 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn\'t supported)
  • 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

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

Improvements in this repository

  • added support for Windows
  • improved binary neural network performance 2x-4x times for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg
  • improved neural network performance ~7% by fusing 2 s into 1: Convolutional + Batch-norm
  • improved neural network performance Detection 3x times, Training 2 x times on GPU Volta (Tesla V100, Titan V, ...) using Tensor Cores if CUDNN_HALF defined in the Makefile or darknet.sln
  • improved performance ~1.2x times on FullHD, ~2x times on 4K, for detection on the video (file/stream) using darknet detector demo...
  • improved performance 3.5 X times of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta
  • improved performance of detection and training on Intel CPU with AVX (Yolo v3 ~85%, Yolo v2 ~10%)
  • fixed usage of [reorg]-
  • optimized memory allocation during network resizing when random=1
  • optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1
  • added correct calculation of mAP, F1, IoU, Precision-Recall using command darknet detector map...
  • added drawing of chart of average loss during training
  • added calculation of anchors for training
  • added example of Detection and Tracking s: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
  • fixed code for use Web-cam on OpenCV 3.x
  • run-time tips and warnings if you use incorrect cfg-file or dataset
  • many other fixes of code...

And added manual - How to train Yolo v3/v2 (to detect your custom s)

Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): https://github.com/AlexeyAB/yolo2_light

How to use:

Example of usage in cmd-files from build\\darknet\\x64\\:

  • darknet_yolo_v3.cmd - initialization with 236 MB Yolo v3 COCO-model yolov3.weights & yolov3.cfg and show detection on the image: dog.jpg

  • 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/yolov3.cfg ./yolov3.weights

  • Yolo v3 COCO - image: darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25
  • Output coordinates of s: darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg
  • Yolo v3 COCO - video: darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4
  • Yolo v3 COCO - WebCam 0: darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0
  • Yolo v3 COCO for net-videocam - Smart WebCam: darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg
  • Yolo v3 - save result to the file res.avi: darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi
  • Yolo v3 Tiny COCO - video: darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4
  • Yolo v3 Tiny on GPU #0: darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4
  • Alternative method Yolo v3 COCO - image: darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25
  • 186 MB Yolo9000 - image: darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights
  • 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 data/train.txt and save results of detection to result.txt use:
    darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt
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:

  • Yolo v3 COCO-model: darknet.exe detector demo data/coco.data yolov3.cfg yolov3.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:

  • 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)
  • CUDNN_HALF=1 to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x
  • 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=./:$LD_LIBRARY_PATH ./uselib test.mp4 How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp or use in such a way: LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4

To run Darknet on Linux use examples from this article, just use ./darknet instead of darknet.exe, i.e. use this command: ./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights

How to compile on Windows:

  1. If you have MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x (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 https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable cudnn with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg NOTE: If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see #500).

    1.1. Find files opencv_world320.dll and opencv_ffmpeg320_64.dll (or opencv_world340.dll and opencv_ffmpeg340_64.dll) in C:\\opencv_3.0\\opencv\\build\\x64\\vc14\\bin and put it near with darknet.exe

    1.2 Check that there are bin and include folders in the C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.1 if aren\'t, then copy them to this folder from the path where is CUDA installed

    1.3. To install CUDNN (speedup neural network), do the following:

    1.4. If you want to build without CUDNN then: open \\darknet.sln -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: CUDNN;

  2. If you have other version of CUDA (not 10.0) then open build\\darknet\\darknet.vcxproj by using Notepad, find 2 places with \"CUDA 10.0\" 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 -> er -> General -> Additional Library Directories: C:\\opencv_2.4.13\\opencv\\build\\x64\\vc14\\lib

  5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x: \\darknet.sln -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: CUDNN_HALF;

    Note: CUDA must be installed only after that MSVS2015 had been installed.

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 -> er -> 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: https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg

    • 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 s (154 MB): http://pjreddie.com/media/files/darknet53.conv.74 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 yolov3-voc.cfg:

  7. Start training by using train_voc.cmd or by using the command line:

    darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74

(Note: To disable Loss-Window use flag -dont_show. 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 : http://pjreddie.com/darknet/yolo/#train-voc

Note: If during training you see nan values for avg (loss) field - then training goes wrong, but if nan is in some other lines - then training goes well.

How to train with multi-GPU:

  1. Train it first on 1 GPU for like 1000 iterations: darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74

  2. Then stop and by using partially-trained model /backup/yolov3-voc_1000.weights run training with multigpu (up to 4 GPUs): darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3

Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set learning_rate = 0.00025 (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times burn_in = and max_batches = in your cfg-file. I.e. use burn_in = 4000 instead of 1000.

https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ

How to train (to detect your custom s):

(to train old Yolo v2 yolov2-voc.cfg, yolov2-tiny-voc.cfg, yolo-voc.cfg, yolo-voc.2.0.cfg, ... click by the )

Training Yolo v3:

  1. Create file yolo-obj.cfg with the same content as in yolov3.cfg (or copy yolov3.cfg to yolo-obj.cfg) and:

So if classes=1 then should be filters=18. If classes=2 then write filters=21.

(Do not write in the cfg-file: filters=(classes + 5)x3)

(Generally filters depends on the classes, coords and number of masks, i.e. filters=(classes + coords + 1)*<number of mask>, where mask is indices of anchors. If mask is absence, then filters=(classes + coords + 1)*num)

So for example, for 2 s, your file yolo-obj.cfg should differ from yolov3.cfg in such lines in each of 3 [yolo]- s:

[convolutional]
filters=21

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