yoloConfig = {...}
 yolo = ml5.YOLO(videoSource, yoloConfig, () => {
     yolo.detect((err, results) => {
         yolo.draw(results,outputCanvas)
        });
    });
///////////////////// OR //////////////////
yolo = ml5.YOLO(() => {
    yolo.detect(image,(err, results) => {
        yolo.draw(results,outputCanvas)
    });
});

 {
  // Model URL
  modelURL: 'https://raw.githubusercontent.com/ml5js/ml5-data-and-models/master/models/YOLO/model.json',
  // Model version : this is important as there is some post processing changes 'v2' ||'v3'
  version: 'v2',
  // this is the size of the model input image : we can lower this to gain more performance
  //  the ones that i found to be a good compromise between perf/accracy are 224, 256, 320
  modelSize: 416, // 128 , 160 , 192 , 224 , 256 , 288 , 320 , 352 , 384 , 416,

  // Intersection Over Union threshhold and Class probability threshold : we use this to filter the output of the cnn
  iouThreshold: 0.5,
  classProbThreshold: 0.5,

  // class labels
  labels: COCO_CLASSES,

  // more info see  Dimension priors : https://arxiv.org/pdf/1612.08242.pdf
  anchors: [
    [0.57273, 0.677385],
    [1.87446, 2.06253],
    [3.33843, 5.47434],
    [7.88282, 3.52778],
    [9.77052, 9.16828],
  ],
  // in yolo v3  the cnn gives 2 or more outputs(set of boxes) so this mask splits the anchors to groups
  // each corresponding to a spesific layer/output.
  // for example  the tiny yolo v2 outputs 1 output that has 13x13x5 boxes (if you use 416 as a model size)
  masks: [[0, 1, 2, 3, 4]],

  // this is just more customization options concerning the preprocessing  pahse
  preProcessingOptions: {
    // 'NearestNeighbor'  - this output a more accurate image but but take a bit longer
    // 'Bilinear' - this faster but scrifices image quality
    ResizeOption: 'Bilinear',
    AlignCorners: true,
  },
};

// example yolov3-tiny config : 
const tinyyolov3config = {
    modelURL: 'models/yolov3-tiny/model.json',
    version: 'v3',
    modelSize: 224,
    anchors: [[10, 14],[23, 27],[37, 58],[81, 82],[135, 169],[344, 319]],
    masks: [[3, 4, 5],[0, 1, 2]],
}