Package Exports

darknet

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (darknet) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

Darknet.JS

A Node wrapper of pjreddie's open source neural network framework Darknet, using the Foreign Function Interface Library. Read: YOLOv3 in JavaScript.

Prerequisites

Linux, Windows (Linux sub-system), Mac (probably)
Node (most versions will work, darknet.js <=1.1.5 only works on node <=8.11.2)
Build tools (make, gcc, etc.)

Installation

Super easy, just install it with npm:

npm install darknet

Usage

To create an instance of darknet.js, you need a three things. The trained weights, the configuration file they were trained with and a list of the names of all the classes.

import { Darknet } from 'darknet';

// Init
let darknet = new Darknet({
    weights: './cats.weights',
    config: './cats.cfg',
    names: [ 'dog', 'cat' ]
});

// Detect
console.log(darknet.detect('/image/of/a/dog/.jpg'));

Example configuration

You can download pre-trained weights and configuration from pjreddie's website. The latest version (yolov3-tiny) is linked below:

If you don't want to download that stuff manually, navigate to the examples directory and issue the ./example command. This will download the necessary files and run some detections.

Async

By default, darknet.js will run the detections synchronously. If this isn't your style, you can run detections asynchronously, using the detectAsync method.

darknet.detectAsync('/image/of/a/dog/.jpg')
    .then(detections => console.log(detections));

Unfortunately only Promises are supported at this time, but support for callbacks will be coming soon.

Activating Turbo Mode

If you want to use CUDA, you'll have to enable it yourself. To do this, navigate to the directory where darknet.js is installed (node_modules/darknet), and edit the makefile inside the darknet repo, darknet/Makefile to say GPU=1. With this enabled, issue the command make && cp libdarknet* .. to compile darknet library and copy it to the root of darknet.js. After doing this, Darknet.js should work with your CUDA enabled GPU.

darknet