simple_face.ts

import * as tf from '@tensorflow/tfjs'

import type { Model, TaskProvider } from "../index.js";
import { models } from '../index.js'
import baseModel from '../models/mobileNetV2_35_alpha_2_classes.js'

export const simpleFace: TaskProvider<"image", "federated"> = {
  getTask() {
    return Promise.resolve({
      id: 'simple_face',
      dataType: "image",
      displayInformation: {
        title: 'Simple Face',
        summary: {
          preview: 'Can you detect if the person in a picture is a child or an adult?',
          overview: 'Simple face is a small subset of the public face_task dataset from Kaggle'
        },
        dataFormatInformation: '',
        dataExample:
          "https://storage.googleapis.com/deai-313515.appspot.com/example_training_data/simple_face-example.png",
        sampleDataset: {
          link: "https://storage.googleapis.com/deai-313515.appspot.com/example_training_data.tar.gz",
          instructions:
            'Opening the link should start downloading a zip file which you can unzip. Inside the "example_training_data" directory you should find the "simple_face" folder which contains the "adult" and "child" folders. To connect the data, select the Group option below and connect adults and children image groups.',
        },
      },
      trainingInformation: {
        epochs: 50,
        roundDuration: 1,
        validationSplit: 0.2,
        batchSize: 10,
        IMAGE_H: 200,
        IMAGE_W: 200,
        LABEL_LIST: ['child', 'adult'],
        scheme: 'federated',
        aggregationStrategy: 'mean',
        minNbOfParticipants: 2,
        tensorBackend: 'tfjs'
      }
    });
  },

  async getModel (): Promise<Model<'image'>> {
    const model = await tf.loadLayersModel({
      load: async () => Promise.resolve(baseModel),
    });

    model.compile({
      optimizer: tf.train.sgd(0.001),
      loss: 'categoricalCrossentropy',
      metrics: ['accuracy']
    })

    return new models.TFJS('image', model)
  }
}