go-darknet: Go bindings for Darknet (Yolo V4, Yolo V7-tiny, Yolo V3)

go-darknet is a Go package, which uses Cgo to enable Go applications to use V4/V7-tiny/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Since this repository https://github.com/gyonluks/go-darknet is no longer maintained I decided to move on and make little different bindings for Darknet.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 v7: https://arxiv.org/abs/2207.02696 (WARNING: Only 'tiny' variation works currently)

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Because this repository https://github.com/gyonluks/go-darknet is no longer maintained.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 Darknet 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. It corresponds last official YOLOv4 release

Use provided Makefile.

• For CPU-based instalattion:

  make install_darknet

• For both CPU and GPU-based instalattion if you HAVE CUDA installed:

  make install_darknet_gpu

  Note: I've tested CUDA 10.2 and cuDNN is 7.6.5)

• For both CPU and GPU-based instalattion if you HAVE NOT CUDA installed:

  make install_darknet_gpu_cuda

  Note: There is some struggle in Makefile for cuDNN, but I hope it works in Ubuntu atleast. Do not forget provide proper CUDA and cuDNN versions.

Installation

go get github.com/LdDl/go-darknet

Usage

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

Building and running program:

• Navigate to examples folder

  cd ${YOUR PATH}/github.com/LdDl/go-darknet/cmd/examples

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

  #for yolo v4
  ./download_data.sh
  #for yolo v4 tiny
  ./download_data_v4_tiny.sh
  #for yolo v7 tiny
  ./download_data_v7_tiny.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 example program

  Yolo v7 tiny:

  go build -o base_example/main base_example/main.go && ./base_example/main --configFile=yolov7-tiny.cfg --weightsFile=yolov7-tiny.weights --imageFile=sample.jpg

  Output should be something like this:

  truck (7): 53.2890% | start point: (0,143) | end point: (89, 328)
  truck (7): 42.1364% | start point: (685,182) | end point: (800, 318)
  truck (7): 26.9703% | start point: (437,170) | end point: (560, 217)
  car (2): 87.7818% | start point: (509,189) | end point: (742, 329)
  car (2): 87.5633% | start point: (262,191) | end point: (423, 322)
  car (2): 85.4743% | start point: (427,198) | end point: (549, 309)
  car (2): 71.3772% | start point: (0,147) | end point: (87, 327)
  car (2): 62.5698% | start point: (98,151) | end point: (197, 286)
  car (2): 61.5811% | start point: (693,186) | end point: (799, 316)
  car (2): 49.6343% | start point: (386,206) | end point: (441, 286)
  car (2): 28.2012% | start point: (386,205) | end point: (440, 236)
  bicycle (1): 71.9609% | start point: (179,294) | end point: (249, 405)
  person (0): 85.4390% | start point: (146,130) | end point: (269, 351)

  Yolo v4:

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

  Output should be something like this:

  traffic light (9): 73.5040% | 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): 46.8694% | start point: (434,173) | end point: (559, 216)
  car (2): 99.7370% | start point: (512,188) | end point: (741, 329)
  car (2): 99.2532% | start point: (260,191) | end point: (422, 322)
  car (2): 99.0333% | start point: (425,201) | end point: (547, 309)
  car (2): 83.3920% | 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 v4 tiny:

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

  Output should be something like this:

  truck (7): 77.7936% | start point: (0,138) | end point: (90, 332)
  truck (7): 55.9773% | start point: (696,174) | end point: (799, 314)
  car (2): 53.1286% | start point: (696,184) | end point: (799, 319)
  car (2): 98.0222% | start point: (262,189) | end point: (424, 330)
  car (2): 97.8773% | start point: (430,190) | end point: (542, 313)
  car (2): 81.4099% | start point: (510,190) | end point: (743, 325)
  car (2): 43.3935% | start point: (391,207) | end point: (435, 299)
  car (2): 37.4221% | start point: (386,206) | end point: (429, 239)
  car (2): 32.0724% | start point: (109,196) | end point: (157, 289)
  person (0): 73.0868% | start point: (154,132) | end point: (284, 382)

  Yolo V3:

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

  Output should be something like this:

  truck (7): 49.5123% | start point: (0,136) | end point: (85, 311)
  car (2): 36.3694% | start point: (95,152) | end point: (186, 283)
  truck (7): 48.4177% | start point: (95,152) | end point: (186, 283)
  truck (7): 45.6520% | start point: (694,178) | end point: (798, 310)
  car (2): 76.8402% | start point: (1,145) | end point: (84, 324)
  truck (7): 25.5920% | start point: (107,89) | end point: (215, 263)
  car (2): 99.8782% | start point: (511,185) | end point: (748, 328)
  car (2): 99.8193% | start point: (261,189) | end point: (427, 322)
  car (2): 99.6405% | start point: (426,197) | end point: (539, 311)
  car (2): 74.5627% | start point: (692,186) | end point: (796, 316)
  car (2): 72.7975% | start point: (388,206) | end point: (437, 276)
  bicycle (1): 72.2760% | start point: (178,270) | end point: (268, 406)
  person (0): 97.3007% | start point: (143,135) | end point: (268, 343)

Documentation

See go-darknet's API documentation at GoDoc.

License

go-darknet follows Darknet's license.