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Since this repository https://github.com/gyonluks/go-darknet is no longer maintained I decided to move on and make little different bindings for Darknet.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

• Why
• Requirements
• Installation
• Usage
• Documentation
• License

Why

Why does this repository exist?

Because this repository https://github.com/gyonluks/go-darknet is no longer maintained.

What is purpose of this bindings when you can have GoCV (bindings to OpenCV) and it handle Darnet YOLO perfectly?

Well, you don't need bunch of OpenCV dependencies and OpenCV itself sometimes.

Example of such project here: https://github.com/LdDl/license_plate_recognition#license-plate-recognition-with-go-darknet---- .

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.