tensorflow-lite

---

name: tensorflow-lite description: > TensorFlow Lite integration for on-device ML in React Native. Trigger: When working with object detection, image classification, or ML models. license: Apache-2.0 metadata: author: 333-333-333 version: "1.0" scope: [mobile] auto_invoke: - "Implementing object detection" - "Working with ML models" - "Image classification tasks"

When to Use

• Implementing object detection (COCO-SSD)
• Image classification (MobileNet)
• Loading and running TensorFlow models
• Processing camera frames for ML
• Optimizing ML performance on mobile

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Critical Patterns

Setup

Copy# Install TensorFlow.js and React Native adapter
npm install @tensorflow/tfjs @tensorflow/tfjs-react-native

# Install models
npm install @tensorflow-models/coco-ssd @tensorflow-models/mobilenet

# iOS: Install pods
cd ios && pod install

Initialization

Copyimport * as tf from '@tensorflow/tfjs';
import { bundleResourceIO } from '@tensorflow/tfjs-react-native';

// MUST call this before using TensorFlow
async function initTensorFlow() {
  await tf.ready();
  console.log('TensorFlow.js ready, backend:', tf.getBackend());
}

---

Object Detection (COCO-SSD)

Copyimport * as cocoSsd from '@tensorflow-models/coco-ssd';

class ObjectDetector {
  constructor() {
    this.model = null;
  }

  async load() {
    // lite_mobilenet_v2 is fastest, mobilenet_v2 is most accurate
    this.model = await cocoSsd.load({
      base: 'lite_mobilenet_v2',
    });
  }

  async detect(imageTensor, minConfidence = 0.6) {
    if (!this.model) throw new Error('Model not loaded');
    
    const predictions = await this.model.detect(imageTensor);
    
    return predictions
      .filter(p => p.score >= minConfidence)
      .map(p => ({
        class: p.class,
        score: p.score,
        bbox: p.bbox, // [x, y, width, height]
      }));
  }
}

COCO-SSD Classes (80 objects)

---

Image Classification (MobileNet)

Copyimport * as mobilenet from '@tensorflow-models/mobilenet';

class ImageClassifier {
  constructor() {
    this.model = null;
  }

  async load() {
    // version: 1 or 2, alpha: 0.25, 0.5, 0.75, 1.0
    // Higher alpha = more accurate but slower
    this.model = await mobilenet.load({
      version: 2,
      alpha: 0.5,
    });
  }

  async classify(imageTensor, topK = 3) {
    if (!this.model) throw new Error('Model not loaded');
    
    const predictions = await this.model.classify(imageTensor, topK);
    
    return predictions.map(p => ({
      label: p.className,
      probability: p.probability,
    }));
  }
}

---

Processing Camera Images

Copyimport * as tf from '@tensorflow/tfjs';
import { decodeJpeg } from '@tensorflow/tfjs-react-native';

async function imageToTensor(imageUri) {
  // Read image as base64
  const response = await fetch(imageUri);
  const imageData = await response.arrayBuffer();
  
  // Convert to tensor
  const imageTensor = decodeJpeg(new Uint8Array(imageData));
  
  return imageTensor;
}

// From base64 string
async function base64ToTensor(base64String) {
  const raw = atob(base64String);
  const bytes = new Uint8Array(raw.length);
  for (let i = 0; i < raw.length; i++) {
    bytes[i] = raw.charCodeAt(i);
  }
  return decodeJpeg(bytes);
}

---

Combined Vision Model

Copyimport * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-react-native';
import * as cocoSsd from '@tensorflow-models/coco-ssd';
import * as mobilenet from '@tensorflow-models/mobilenet';

class VisionModel {
  constructor() {
    this.detector = null;
    this.classifier = null;
    this.isReady = false;
  }

  async initialize() {
    await tf.ready();
    
    // Load models in parallel
    const [detector, classifier] = await Promise.all([
      cocoSsd.load({ base: 'lite_mobilenet_v2' }),
      mobilenet.load({ version: 2, alpha: 0.5 }),
    ]);
    
    this.detector = detector;
    this.classifier = classifier;
    this.isReady = true;
  }

  async analyze(imageTensor) {
    if (!this.isReady) throw new Error('Models not ready');

    const [objects, classifications] = await Promise.all([
      this.detector.detect(imageTensor),
      this.classifier.classify(imageTensor, 3),
    ]);

    return {
      objects: objects.filter(o => o.score > 0.6),
      scene: classifications[0]?.className || 'unknown',
    };
  }

  dispose() {
    // Clean up tensors to prevent memory leaks
    tf.dispose();
  }
}

---

Performance Optimization

Memory Management

Copy// Option 1: Manual dispose
const tensor = imageToTensor(image);
const result = await model.detect(tensor);
tensor.dispose(); // IMPORTANT!

// Option 2: tf.tidy (automatic cleanup)
const result = tf.tidy(() => {
  const tensor = imageToTensor(image);
  return model.detect(tensor);
});

Reduce Image Size

Copyfunction resizeImage(imageTensor, targetSize = 300) {
  return tf.tidy(() => {
    return tf.image.resizeBilinear(imageTensor, [targetSize, targetSize]);
  });
}

---

Spanish Labels

Copyconst LABELS_ES = {
  person: 'persona',
  chair: 'silla',
  cup: 'taza',
  bottle: 'botella',
  cell_phone: 'celular',
  book: 'libro',
  dog: 'perro',
  cat: 'gato',
  car: 'auto',
  // ... add more as needed
};

function translateLabel(label) {
  return LABELS_ES[label.toLowerCase()] || label;
}

---

Commands

Copy# Clear Metro cache (fixes tensor issues)
npx expo start --clear

# Check TensorFlow backend
console.log(tf.getBackend()); // 'rn-webgl' or 'cpu'

# Memory debugging
console.log(tf.memory()); // { numTensors, numBytes, ... }

---

Troubleshooting