``` javascript

await faceapi.loadFaceDetectionModel('/models')

As an alternative, you can also create instance of the neural nets:

Using instances, you can also load the weights as a Float32Array (in case you want to use the uncompressed models):

const detections = await faceapi.locateFaces(myImg, minConfidence, maxResults)

const { boxes, scores } = net.forward('myImg')

const descriptor1 = await faceapi.computeFaceDescriptor('myImg')

const descriptor2 = await faceapi.computeFaceDescriptor(document.getElementById('myCanvas'))

const t = net.forward('myImg')

const landmarks = await faceapi.detectLandmarks(myImg)

const detections = await faceapi.locateFaces(input)

const landmarksByFace = await Promise.all(faceTensors.map(t => faceapi.detectLandmarks(t)))

After face detection has been performed, I would recommend to align the bounding boxes of the detected faces before passing them to the face recognition net, which will make the computed face descriptor much more accurate. Fortunately, the api can do this for you under the hood. You can obtain the full face descriptions (location, landmarks and descriptor) of each face in an input image as follows:

``` javascript

const fullFaceDescriptions = await faceapi.allFaces(input, minConfidence)

const fullFaceDescription0 = fullFaceDescriptions[0]

console.log(fullFaceDescription0.detection) // bounding box & score

console.log(fullFaceDescription0.landmarks) // 68 point face landmarks

console.log(fullFaceDescription0.descriptor) // face descriptors

You can also do everything manually as shown in the following:

const detections = await faceapi.locateFaces(input, minConfidence)

const faceLandmarks = await faceapi.detectLandmarks(faceTensor)

faceTensor => faceapi.computeFaceDescriptor(faceTensor)

import { FaceDetection } from './faceDetectionNet/FaceDetection';

import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';

export declare class FullFaceDescription {

private _detection;

private _landmarks;

private _descriptor;

constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);

readonly detection: FaceDetection;

readonly landmarks: FaceLandmarks;

readonly descriptor: Float32Array;

forSize(width: number, height: number): FullFaceDescription;

import * as tf from '@tensorflow/tfjs-core';

import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';

import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';

import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';

import { FullFaceDescription } from './FullFaceDescription';

import { NetInput } from './NetInput';

export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput, minConfidence: number) => Promise<FullFaceDescription[]>;

export * from './FaceDetection';