technology software

Chinese

Domain 'ml5demo-3lm4upsjco.edgeone.app' uses the application-focused generic top-level domain (.app), featuring subdomain 'ml5demo-3lm4upsjco'. Its registrable label 'edgeone' stretches across 7 characters holding four vowels versus 3 consonants. Tokenizing the label suggests 2 words: edge, one. The median word length lands at 3.5 characters. No strong language cues emerged from the frequency lists.

html/a4/06/a4068ada-fb53-4657-83e5-9240978649bc.html.gz

sha256=:iQbe0yNN03kqLc73kMHycDPwWTE/i4W8j4HhBCDow0Y=:

require-trusted-types-for 'script';report-uri /recaptcha/challengepage/_/RecaptchaChallengePageUi/cspreport, script-src 'report-sample' 'nonce-Oot6Gm-d6HsCiyBJl7D5Mw' 'unsafe-inline';object-src 'none';base-uri www.google.com;report-uri /recaptcha/challengepage/_/RecaptchaChallengePageUi/cspreport;worker-src 'self', script-src 'unsafe-inline' 'unsafe-eval' blob: data: 'self' https://apis.google.com https://ssl.gstatic.com https://www.google.com https://www.googletagmanager.com https://www.gstatic.com https://www.google-analytics.com https://recaptcha-staging.corp.google.com/;report-uri /recaptcha/challengepage/_/RecaptchaChallengePageUi/cspreport/allowlist;base-uri www.google.com

script-src 'unsafe-inline' 'unsafe-eval' blob: data: https://www.google.com/recaptcha/enterprise.js https://www.gstatic.com/_/mss/boq-one-google/_/ https://www.gstatic.com/og/_/js/ https://apis.google.com/js/api.js https://apis.google.com/js/client.js https://www.googletagmanager.com/gtag/js https://www.google-analytics.com/analytics.js https://www.googletagmanager.com/gtag/destination https://www.gstatic.com/_/mss/boq-recaptcha/_/js/k=boq-recaptcha.RecaptchaChallengePageUi.en_US.-yqnqw7WWGw.2018.O/ https://apis.google.com/_/scs/abc-static/_/js/ https://www.gstatic.com/recaptcha/releases/;report-uri /recaptcha/challengepage/_/RecaptchaChallengePageUi/cspreport/fine-allowlist

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viewport

html/a4/06/a4068ada-fb53-4657-83e5-9240978649bc_nodriver.html.gz

Ml5.js 训练与推理演示

Prudence Malinki

MarkMonitor Inc.

edgeone.app

GC Abuse

Google LLC

GOOGLE-CLOUD


    let mobilenet;
    let classifier;
    let video;
    let uNet;
    let isModelReady = false;
    let isTraining = false;
    
    // 计数器
    let countA = 0;
    let countB = 0;

    // DOM 元素引用
    const btnA = document.getElementById('btnA');
    const btnB = document.getElementById('btnB');
    const btnTrain = document.getElementById('btnTrain');
    const statusSpan = document.getElementById('status');
    const lossSpan = document.getElementById('loss');
    const resultSpan = document.getElementById('result');
    const confSpan = document.getElementById('confidence');

    // p5.js 的 setup 函数 - 初始化
    function setup() {
      // 创建画布并放入指定容器
      const canvas = createCanvas(320, 240);
      canvas.parent('video-container');

      // 捕获摄像头视频
      video = createCapture(VIDEO);
      video.size(320, 240);
      video.hide(); // 隐藏原始 HTML 视频元素，只在 canvas 上绘制

      // 初始化 MobileNet 特征提取器
      // 使用 MobileNet 作为基础模型，剥离最后一层用于迁移学习
      const options = { numLabels: 2 }; 
      mobilenet = ml5.featureExtractor('MobileNet', options, modelLoaded);
      
      // 创建分类器 (这是我们要训练的部分)
      classifier = mobilenet.classification(video, videoReady);

      // 绑定按钮事件
      btnA.onclick = function() {
        if(isModelReady) {
          classifier.addImage('物品 A');
          countA++;
          btnA.innerText = `采集 "物品 A" (${countA})`;
        }
      };

      btnB.onclick = function() {
        if(isModelReady) {
          classifier.addImage('物品 B');
          countB++;
          btnB.innerText = `采集 "物品 B" (${countB})`;
        }
      };

      btnTrain.onclick = function() {
        if (countA > 0 && countB > 0) {
          statusSpan.innerText = "正在训练...";
          isTraining = true;
          // 开始训练
          classifier.train(whileTraining);
        } else {
          alert("请先为 A 和 B 两类都采集一些图片数据！");
        }
      };
    }

    // p5.js 的 draw 函数 - 循环渲染
    function draw() {
      background(0);
      // 将视频画面绘制到 canvas 上
      image(video, 0, 0, 320, 240);
      
      // 如果正在训练，可以在画面上加个蒙版
      if (isTraining) {
        fill(0, 0, 0, 150);
        rect(0, 0, 320, 240);
        fill(255);
        textAlign(CENTER, CENTER);
        text("训练中...", 160, 120);
      }
    }

    // 模型加载完成回调
    function modelLoaded() {
      console.log('MobileNet 基础模型已加载');
    }

    // 视频和分类器准备就绪回调
    function videoReady() {
      console.log('视频流已连接');
      statusSpan.innerText = "模型就绪，请采集数据";
      isModelReady = true;
    }

    // 训练过程中的回调函数 (Loss function)
    function whileTraining(loss) {
      if (loss == null) {
        // loss 为 null 表示训练结束
        statusSpan.innerText = "训练完成！正在进行推理...";
        isTraining = false;
        btnTrain.disabled = true; // 禁用训练按钮
        btnA.disabled = true;
        btnB.disabled = true;
        
        // 开始实时分类推理
        classifier.classify(gotResults);
      } else {
        // 更新 Loss 显示 (越接近 0 越好)
        lossSpan.innerText = loss.toFixed(4);
      }
    }

    // 推理结果回调函数
    function gotResults(error, results) {
      if (error) {
        console.error(error);
      } else {
        // results 是一个数组，按置信度排序
        // results[0].label 是预测类别
        // results[0].confidence 是置信度
        
        resultSpan.innerText = results[0].label;
        const confidence = (results[0].confidence * 100).toFixed(2);
        confSpan.innerText = confidence + "%";
        
        // 修改文字颜色以匹配类别
        if (results[0].label === '物品 A') {
            resultSpan.style.color = '#e74c3c';
        } else {
            resultSpan.style.color = '#3498db';
        }

        // 继续下一帧的预测 (形成循环)
        classifier.classify(gotResults);
      }
    }
  

Requests	IP Address	Location	AS Autonomous System
13	216.58.206.91	United States	AS15169GOOGLE
12	104.18.0.22	United States	AS13335CLOUDFLARENET
12	43.152.26.58	Singapore	AS139341ACE
12	35.244.233.98	Kansas City, Missouri, United States	AS396982GOOGLE-CLOUD-PLATFORM
12	104.17.25.14	Singapore	Unknown
61	5	-	-

Security Scan Report: ml5demo-3lm4upsjco.edgeone.app

Summary

AI Security Verdict

Safe Website

Safety Factors

Details

Screenshot

Page Load Overview

Language Analysis

Primary Language

Detection Details

Website Classification

Primary Category

All Detected Categories

Detected Features

Domain & IP Information

Detected Technologies3

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

ssdeep (Context Triggered Piecewise Hashing)

sdhash (Similarity Digest Hashing)

Image Hashes

Perceptual Hashes

Other Hashes

Scan History