darknet

go-darknet: Go bindings for Darknet (Yolo V4, Yolo V3)

go-darknet is a Go package, which uses Cgo to enable Go applications to use YOLO V4/V3 in Darknet.

Since this repository https://github.com/gyonluks/go-darknet is no longer maintained I decided to move on and make little different bindings for Darknet.

This bindings aren't for official implementation but for AlexeyAB's fork.

Paper Yolo v4: https://arxiv.org/abs/2004.10934

Paper Yolo v3: https://arxiv.org/abs/1804.02767

Table of Contents

• Requirements
• Installation
• Usage
• Documentation
• License

Requirements

You need to install fork of darknet. Latest commit I've tested is here

Use provided Makefile.

• For CPU-based instalattion:

  make install
  

• For both CPU and GPU-based instalattion:

  make install_gpu
  

  Note: If you want to have GPU-acceleration before running command above install CUDA and cuDNN (Latest CUDA version I've tested is 10.2 and cuDNN is 7.6.5)

Installation

go get github.com/LdDl/go-darknet

Usage

Example Go program is provided in the example directory. Please refer to the code on how to use this Go package.

Building and running program:

• Navigate to example folder

  cd $GOPATH/github.com/LdDl/go-darknet/example/base_example
  

• Download dataset (sample of image, coco.names, yolov4.cfg (or v3), yolov4.weights(or v3)).

  #for yolo v4
  ./download_data.sh
  #for yolo v3
  ./download_data_v3.sh
  

• Note: you don't need coco.data file anymore, because sh-script above does insert coco.names into 'names' field in yolov4.cfg file (so AlexeyAB's fork can deal with it properly) So last rows in yolov4.cfg file will look like:

  ......
  [yolo]
  .....
  iou_loss=ciou
  nms_kind=greedynms
  beta_nms=0.6
  
  names = coco.names # this is path to coco.names file
  

• Also do not forget change batch and subdivisions sizes from:

  batch=64
  subdivisions=8
  

  to

  batch=1
  subdivisions=1
  

  It will reduce amount of VRAM used for detector test.

• Build and run program Yolo V4:

  go build main.go && ./main --configFile=yolov4.cfg --weightsFile=yolov4.weights --imageFile=sample.jpg
  

  Output should be something like this:

  traffic light (9): 73.5039% | start point: (238,73) | end point: (251, 106)
  truck (7): 96.6401% | start point: (95,79) | end point: (233, 287)
  truck (7): 96.4774% | start point: (662,158) | end point: (800, 321)
  truck (7): 96.1841% | start point: (0,77) | end point: (86, 333)
  truck (7): 46.8695% | start point: (434,173) | end point: (559, 216)
  car (2): 99.7370% | start point: (512,188) | end point: (741, 329)
  car (2): 99.2533% | start point: (260,191) | end point: (422, 322)
  car (2): 99.0333% | start point: (425,201) | end point: (547, 309)
  car (2): 83.3919% | start point: (386,210) | end point: (437, 287)
  car (2): 75.8621% | start point: (73,199) | end point: (102, 274)
  car (2): 39.1925% | start point: (386,206) | end point: (442, 240)
  bicycle (1): 76.3121% | start point: (189,298) | end point: (253, 402)
  person (0): 97.7213% | start point: (141,129) | end point: (283, 362)
  

  Yolo V3:

  go build main.go && ./main --configFile=yolov3.cfg --weightsFile=yolov3.weights --imageFile=sample.jpg
  

  Output should be something like this:

  truck (7): 49.5197% | start point: (0,136) | end point: (85, 311)
  car (2): 36.3747% | start point: (95,152) | end point: (186, 283)
  truck (7): 48.4384% | start point: (95,152) | end point: (186, 283)
  truck (7): 45.6590% | start point: (694,178) | end point: (798, 310)
  car (2): 76.8379% | start point: (1,145) | end point: (84, 324)
  truck (7): 25.5731% | start point: (107,89) | end point: (215, 263)
  car (2): 99.8783% | start point: (511,185) | end point: (748, 328)
  car (2): 99.8194% | start point: (261,189) | end point: (427, 322)
  car (2): 99.6408% | start point: (426,197) | end point: (539, 311)
  car (2): 74.5610% | start point: (692,186) | end point: (796, 316)
  car (2): 72.8053% | start point: (388,206) | end point: (437, 276)
  bicycle (1): 72.2932% | start point: (178,270) | end point: (268, 406)
  person (0): 97.3026% | start point: (143,135) | end point: (268, 343)
  

Documentation

See go-darknet's API documentation at GoDoc.

License

go-darknet follows Darknet's license.