https://github.com/wimspaargaren/yolov3

Go implementation of the yolo v3 object detection system
https://github.com/wimspaargaren/yolov3

Last synced: 3 months ago
JSON representation

Go implementation of the yolo v3 object detection system

• Host: GitHub
• URL: https://github.com/wimspaargaren/yolov3
• Owner: wimspaargaren
• License: mit
• Created: 2021-04-06T21:28:13.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2024-06-10T15:19:27.000Z (9 months ago)
• Last Synced: 2024-08-03T01:25:42.597Z (7 months ago)
• Language: Go
• Size: 4.27 MB
• Stars: 76
• Watchers: 1
• Forks: 15
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-yolo-object-detection - wimspaargaren/yolov3
• awesome-yolo-object-detection - wimspaargaren/yolov3

README

        
# Go YOLO V3

[![Build Status](https://github.com/wimspaargaren/yolov3/workflows/CI/badge.svg)](https://github.com/wimspaargaren/yolov3/actions)

![Coverage Badge](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/wimspaargaren/336567a087162bcad4e55ebe4b17b457/raw/yolov3__heads_master.json)

[![Go Reference](https://pkg.go.dev/badge/github.com/wimspaargaren/yolov3.svg)](https://pkg.go.dev/github.com/wimspaargaren/yolov3)

This repository provides a plug and play implementation of the [Yolo V3](https://pjreddie.com/darknet/yolo/) object detection system in Go, leveraging [gocv](https://github.com/hybridgroup/gocv).

# Prerequisites

Since this implementation builds on top of the [gocv](https://github.com/hybridgroup/gocv) library, make sure you either use one of the provided [docker images](https://github.com/hybridgroup/gocv/blob/release/Dockerfile) to run the example, or install the opencv dependencies on your system.

Furthermore, make sure you've got the yolov3 models downloaded before running the examples. 

Simply run `$ make models`

# Run the examples

## Bird example

`$ make bird-example`

Output



## Street example

`$ make street-example`

Output



## Webcam example

`$ make webcam-example`

Note that this will not run smoothly on most machines, as the default net target type is set to `NetTargetCPU`. If you have cuda installed, adjust the net initialization to:

```GOLANG

	conf := yolov3.DefaultConfig()

	// Adjust the backend and target type

	conf.NetBackendType = gocv.NetBackendCUDA

	conf.NetTargetType = gocv.NetTargetCUDA

	// Create the net with created config

	yolonet, err := yolov3.NewNetWithConfig(yolov3WeightsPath, yolov3ConfigPath, cocoNames, conf)

	if err != nil {

		log.WithError(err).Fatal("unable to create yolo net")

	}

```

## Cuda example

Execute 50 fps test render with cuda, also see the [CUDA](#CUDA) section.

`$ make cuda-example`

# CUDA

If you're interested in running yolo in Go with CUDA support, check the `cmd/example_cuda` to see a dummy example and test results of running object detection at 50 fps. The [gocv cuda README](https://github.com/hybridgroup/gocv/blob/release/cuda/README.md) provides detailed installation instructions.

# Issues

If you have any issues, feel free to open a PR or create an issue!