A Tensorflow.js implementation of YOLOv3 and YOLOv3-tiny
Note: Must [email protected]+
- can recognize images of any size
- Support both yolov3 and yolov3-tiny
npm install tfjs-yolov3
import { yolov3, yolov3Tiny } from 'tfjs-yolov3'
async function start () {
const yolo = await yolov3Tiny() // pre-load model (35M)
// or
// const yolo = await yolov3() // pre-load model (245M)
const $img = document.getElementById('img')
const boxes = await yolo($img)
draw(boxes) // Some draw function
}
start()The yolov3 and yolov3Tiny functions accept an options object and return a function
export declare function yolov3 (
{ modelUrl, anchors }? :
{ modelUrl?: string, anchors?: number[] }
): Promise<yolo>
export declare function yolov3Tiny (
{ modelUrl, anchors }? :
{ modelUrl?: string, anchors?: number[] }
): Promise<yolo>| Parameters | Description |
|---|---|
| modelUrl | Optional, pre-train the model's url, you can download the model to the local, speed up the loading of the pre-training model |
| anchors | Optional, custom anchors, format referenceconfig |
After the above two functions are called, the pre-training model will be loaded, and a function will be returned. This function can be used to identify the image and return the identified box list. The parameters are as follows:
type yolo = ($img: HTMLImageElement) => Promise<Box[]>
interface Box {
top: number
left: number
bottom: number
right: number
width: number
height: number
scores: number
classes: string
}