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Docs: Update HUB Teams page (#13215)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Sergiu Waxmann 2024-05-29 20:24:38 +03:00 • committed by GitHub
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docs/en/hub/app/android.md
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---
comments: true
description: Experience rapid object detection on your Android device with the Ultralytics YOLO model app. Click to learn more!.
keywords: Ultralytics, YOLO, Android App, real-time object detection, TensorFlow Lite, hardware acceleration, FP16, INT8, GPU
description: Discover real-time object detection on your Android with the Ultralytics App using optimized YOLO models.
keywords: Ultralytics, YOLO, Android app, real-time object detection, TensorFlow Lite, model optimization, AI mobile application
---
# Ultralytics Android App: Real-time Object Detection with YOLO Models

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docs/en/hub/app/index.md
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---
comments: true
description: Unlock the power of YOLO models on iOS & Android with the Ultralytics HUB App. Experience optimized performance on-the-go!.
keywords: Ultralytics HUB App, YOLOv5, YOLOv8, mobile object detection, iOS, Android, real-time image recognition
description: Unlock real-time YOLO model detection directly on your mobile! Discover apps for iOS and Android with YOLOv5 and YOLOv8.
keywords: Ultralytics HUB App, YOLOv5, YOLOv8, real-time detection, mobile AI, iOS, Android, Apple Neural Engine, Android GPU, Ultralytics
---
# Ultralytics HUB App

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docs/en/hub/app/ios.md
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---
comments: true
description: Transform your iOS device into a powerful object detection tool with the Ultralytics iOS App, powered by YOLO models.
keywords: Ultralytics, iOS app, YOLO, object detection, real-time, iPhone, iPad, quantization, Apple Neural Engine, Core ML
description: Discover real-time object detection on iOS devices using powerful YOLO models, optimized via Apple Neural Engine.
keywords: Ultralytics, iOS app, YOLO models, real-time detection, object detection, iPhone, iPad, Apple Neural Engine, Core ML, quantization, FP16, INT8
---
# Ultralytics iOS App: Real-time Object Detection with YOLO Models