Browser-Based ML Inference Guide

Run machine learning models directly in the browser without server backends. This guide compares the major frameworks and tools available.

Complete Comparison Table

Detailed Breakdowns

1. TensorFlow.js

Machine learning library for JavaScript that runs in the browser and on Node.js. Essential for real-time ML applications.

Key Features:

• Run ML models directly in the browser (no server needed)
• GPU acceleration via WebGL or WebGPU
• Convert PyTorch/TensorFlow models to browser format
• Pre-trained models for common tasks
• Automatic differentiation for training in the browser

Relevant to Your Work:

• Run LipNet inference in real-time on client
• Process audio for lip-sync without server latency
• Integrate with MediaPipe for face tracking
• Optimize models with quantization for mobile

→ TensorFlow.js Docs → GitHub

2. ONNX Runtime Web

High-performance runtime for ONNX models in the browser with WebGPU support for next-gen performance.

Advantages:

• Better performance than TensorFlow.js for many models
• WebGPU support (3-5× faster than WebGL)
• Supports quantized models natively
• Works offline after model download

Performance Metrics:

• WebGL: 77-225ms latency (ResNet50)
• WebGPU: 30-50fps effective inference
• CPU fallback: 10-30fps effective

→ Official Site

3. MediaPipe

Google’s framework for building multimodal machine learning pipelines. Excellent for face tracking, pose detection, and hand tracking.

Core Solutions (Relevant to Avatar Work):

• Face Mesh: 468-point face landmark detection in real-time
• Face Landmarker: Enhanced face detection with iris tracking
• Hand Tracking: Real-time hand gesture recognition
• Pose Estimation: Full-body pose tracking

Use Cases:

• Real-time facial expression tracking for avatar control
• Mouth position detection for lip-sync synchronization
• Combine with LipNet for more natural mouth animation
• Mobile-friendly (works on iOS and Android)

Performance:

• Desktop: 30+ FPS (Chrome)
• Mobile: 15-25 FPS (Android), 6-7 FPS (iOS)

→ Official Site

4. Transformers.js

State-of-the-art NLP models run directly in the browser. Perfect for text processing without server calls.

Available Models:

• Text classification
• Named entity recognition
• Question answering
• Summarization
• Translation

→ GitHub

5. OpenCV.js

JavaScript binding of OpenCV for computer vision tasks in the browser.

Capabilities:

• Image processing
• Feature detection
• Object tracking
• Calibration and 3D reconstruction

→ Official Docs

Performance Comparison by Task

Face Tracking (Desktop)

Model Inference (WebGL vs WebGPU)

Mobile Performance (Browser)

Choosing the Right Tool

START: I want to run ML in my browser

├─ Do you need face/pose tracking?
│  ├─ YES → Use MediaPipe
│  └─ NO → Continue
│
├─ Do you need maximum performance?
│  ├─ YES → Use ONNX Runtime Web (WebGPU)
│  └─ NO → Continue
│
├─ Do you need NLP capabilities?
│  ├─ YES → Use Transformers.js
│  └─ NO → Continue
│
├─ Do you have a model to convert?
│  ├─ TensorFlow/PyTorch → Use TensorFlow.js
│  ├─ ONNX format → Use ONNX Runtime Web
│  └─ Other → Research model conversion first
│
└─ For general CV: Use OpenCV.js

Implementation Tips

For Real-Time Performance:

1. Use quantized models (INT8/FP16)
2. Leverage GPU acceleration (WebGL/WebGPU)
3. Cache model loads
4. Use Web Workers for inference to avoid blocking UI

For Model Conversion:

• TensorFlow → TensorFlow.js: Use tensorflowjs_converter
• PyTorch → ONNX → ONNX Runtime Web
• Check model format compatibility before converting

For Production Deployment:

• Lazy load models
• Monitor memory usage
• Provide fallback for unsupported browsers
• Test on actual mobile devices

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