tfjs-yolo
YOLO object detection with Tensorflow.js. Supports YOLO v3 and Tiny YOLO v1, v2, v3.
Demo
-
Detect objects using your webcam: https://shaqian.github.io/tfjs-yolo-demo/
-
Not hotdog PWA: https://shaqian.github.io/Not-Hotdog/
Install
npm install tfjs-yolo
Usage
Import module
import yolo from 'tfjs-yolo';Initialize and load model
// Use default models (stored in my GitHub demo repo)
let myYolo = await yolo.v1tiny();
let myYolo = await yolo.v2tiny();
let myYolo = await yolo.v3tiny();
let myYolo = await yolo.v3();
// or specify path or handler, see https://js.tensorflow.org/api/0.13.3/#loadModel
let myYolo = await yolo.v3tiny("https://.../model.json");
// or use frozen model, see https://js.tensorflow.org/api/0.13.3/#loadFrozenModel
let myYolo = await yolo.v3tiny(
"https://.../weights_manifest.json",
"https://.../tensorflowjs_model.pb"
);Run model
Supported input html element:
- img
- canvas
- video
const boxes = await myYolo.predict(canvas);
// Optional settings
const boxes = await myYolo.predict(
canvas,
{
maxBoxes: 5, // defaults to 20
scoreThreshold: .2, // defaults to .5
iouThreshold: .5, // defaults to .3
numClasses: 80, // defaults to 80 for yolo v3, tiny yolo v2, v3 and 20 for tiny yolo v1
anchors: [...], // See ./src/config.js for examples
classNames: [...], // defaults to coco classes for yolo v3, tiny yolo v2, v3 and voc classes for tiny yolo v1
inputSize: 416, // defaults to 416
}
);Output box format
{
top, // Float
left, // Float
bottom, // Float
right, // Float
height, // Float
width, // Float
score, // Float
class // String, e.g. person
}Dispose model
myYolo.dispose();