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TensorFlow Lite

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Architect
Architect
May 31, 2025 · Artificial Intelligence

Edge Intelligence Implementation in the Vivo Official App: Architecture, Feature Engineering, and Model Deployment

The article details how edge intelligence is applied to the Vivo official app to improve product recommendation on the smart‑hardware floor by abstracting the problem, designing feature engineering pipelines, training TensorFlow models, converting them to TFLite, and deploying inference on mobile devices, while also covering monitoring and performance considerations.

Model DeploymentTensorFlow Liteedge AI
0 likes · 19 min read
Edge Intelligence Implementation in the Vivo Official App: Architecture, Feature Engineering, and Model Deployment
Sohu Tech Products
Sohu Tech Products
Mar 6, 2024 · Mobile Development

On‑Device Deployment of Large Language Models Using Sohu’s Hybrid AI Engine and GPT‑2

The article outlines how Sohu’s Hybrid AI Engine enables on‑device deployment of a distilled GPT‑2 model by converting it to TensorFlow Lite, detailing the setup, customization with Keras, inference workflow, and core SDK calls, and argues that this approach offers fast, private, and cost‑effective AI for mobile devices despite typical LLM constraints.

GPT-2Hybrid AIKeras
0 likes · 9 min read
On‑Device Deployment of Large Language Models Using Sohu’s Hybrid AI Engine and GPT‑2
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 28, 2023 · Artificial Intelligence

Running AI on the Frontend: Pose Estimation with TensorFlow Lite MoveNet

This article explains how front‑end developers can run AI locally on web, Android, iOS and Raspberry Pi devices using TensorFlow Lite MoveNet for real‑time pose estimation, walks through setup, model variants, Python code examples, and practical applications such as yoga‑pose classification.

AndroidFrontend AIMoveNet
0 likes · 13 min read
Running AI on the Frontend: Pose Estimation with TensorFlow Lite MoveNet
Sohu Tech Products
Sohu Tech Products
Jan 20, 2021 · Mobile Development

Hybrid AI Engine: Integrating On‑Device Image Recognition with TensorFlow Lite and HiAI

This article introduces three traditional approaches for deploying machine‑learning models on mobile devices, analyzes their drawbacks, and presents a hybrid AI engine that combines TensorFlow Lite and system‑level HiAI to provide a unified, lightweight, and developer‑friendly on‑device image‑recognition solution, including code examples.

Android DevelopmentHybrid AI EngineTensorFlow Lite
0 likes · 12 min read
Hybrid AI Engine: Integrating On‑Device Image Recognition with TensorFlow Lite and HiAI
Sohu Tech Products
Sohu Tech Products
Oct 28, 2020 · Mobile Development

Android Studio 4.1 Stable Release – New Features and Improvements

Android Studio 4.1 introduces a suite of enhancements—including a Database Inspector, Material Design component updates, integrated TensorFlow Lite support, improved Apply Changes, native memory profiling, and expanded emulator capabilities—aimed at boosting productivity and code quality for Android developers.

DaggerDatabase InspectorMobile Development
0 likes · 13 min read
Android Studio 4.1 Stable Release – New Features and Improvements
Laravel Tech Community
Laravel Tech Community
Oct 14, 2020 · Mobile Development

Android Studio 4.1 Stable Release: New Design, Development, Build & Test, and Optimization Features

Android Studio 4.1 stable release introduces upgraded Material Design components, a built‑in Database Inspector, direct Android Emulator support, Dagger navigation, TensorFlow Lite model integration, foldable‑device emulator capabilities, and a native memory profiler, enhancing the overall Android development experience.

Database InspectorMaterial DesignMobile Development
0 likes · 5 min read
Android Studio 4.1 Stable Release: New Design, Development, Build & Test, and Optimization Features
Tencent Music Tech Team
Tencent Music Tech Team
May 8, 2020 · Mobile Development

Mobile Machine Learning Frameworks Overview and Deployment Practices in Q Music

The article reviews four mobile‑focused machine‑learning frameworks—NCNN, TensorFlow Lite, PyTorch Mobile (Caffe2) and FeatherKit—detailing their size, speed, and resource trade‑offs, and explains Q Music’s edge‑inference pipeline, optimization strategies, and the challenges of performance variability on heterogeneous mobile devices.

FeatherKitNCNNPyTorch Mobile
0 likes · 25 min read
Mobile Machine Learning Frameworks Overview and Deployment Practices in Q Music
58 Tech
58 Tech
Jan 15, 2020 · Artificial Intelligence

Mobile AI Vehicle and VIN Recognition: From TensorFlow to TensorFlow Lite Deployment on Android and iOS

This article details how the 58 Used‑Car mobile team built, trained, and optimized TensorFlow‑based object‑detection models for on‑device vehicle and VIN code recognition, covering data preparation, model conversion to TF‑Lite, performance improvements, engineering integration on Android/iOS, and real‑world deployment results.

AndroidTensorFlowTensorFlow Lite
0 likes · 14 min read
Mobile AI Vehicle and VIN Recognition: From TensorFlow to TensorFlow Lite Deployment on Android and iOS
iQIYI Technical Product Team
iQIYI Technical Product Team
May 30, 2019 · Mobile Development

SmileAR: iQIYI’s Mobile AR Solution Powered by TensorFlow Lite

SmileAR, iQIYI’s self‑developed mobile AR platform powered by TensorFlow Lite, delivers real‑time face, body and gesture recognition across iQIYI’s apps through MobileNet‑based models, quantization‑aware training, multi‑task learning and encrypted SDKs, achieving fast, lightweight, cross‑platform AR experiences for millions of users.

ARComputer VisionTensorFlow Lite
0 likes · 10 min read
SmileAR: iQIYI’s Mobile AR Solution Powered by TensorFlow Lite
Xianyu Technology
Xianyu Technology
Sep 25, 2018 · Artificial Intelligence

TensorFlow Lite Applications and UI2Code at Xianyu (Idle Fish)

Xianyu leverages a custom TensorFlow Lite framework to power AI‑driven features such as dynamic video‑cover selection, video fingerprinting, and furniture recognition for smart rentals, while its UI2Code tool transforms screenshots into pixel‑perfect production UI code, emphasizing extensibility, security, and online model updates.

TensorFlowTensorFlow LiteUI2Code
0 likes · 7 min read
TensorFlow Lite Applications and UI2Code at Xianyu (Idle Fish)
Xianyu Technology
Xianyu Technology
Jun 16, 2018 · Artificial Intelligence

Watermark Detection and Removal on iOS using TensorFlow Lite SSD and OpenCV

The article presents a complete iOS pipeline that trains an SSD‑MobileNet detector with TensorFlow, optimizes and converts it to TensorFlow Lite, runs C++ inference, applies non‑maximum suppression, and finally removes detected watermarks using OpenCV inpainting or pixel‑wise inversion, while discussing practical limitations.

TensorFlow LiteWatermark RemovaliOS
0 likes · 12 min read
Watermark Detection and Removal on iOS using TensorFlow Lite SSD and OpenCV
Xianyu Technology
Xianyu Technology
May 24, 2018 · Artificial Intelligence

Custom TensorFlow Lite OP Pipeline: Architecture, Server and Client Implementation

The article provides an engineering‑focused guide to creating a custom TensorFlow Lite operation pipeline, covering its definition, server‑side registration and compilation, client‑side downloading, verification, decryption and dynamic loading, and discusses current limitations and possible extensions such as compression and new tensor types.

Custom OPModel EncryptionTensorFlow Lite
0 likes · 9 min read
Custom TensorFlow Lite OP Pipeline: Architecture, Server and Client Implementation
Xianyu Technology
Xianyu Technology
Apr 20, 2018 · Artificial Intelligence

Client‑Side Voice Recognition with TensorFlow Lite and MFCC Optimization

The paper presents a client‑side speech recognizer that uses a compact TensorFlow Lite Inception‑v3 CNN model combined with an optimized MFCC feature pipeline and ARM‑NEON‑accelerated, multi‑threaded processing, achieving low‑latency, high‑accuracy voice recognition on mobile and embedded devices.

Audio ProcessingMFCCTensorFlow Lite
0 likes · 14 min read
Client‑Side Voice Recognition with TensorFlow Lite and MFCC Optimization