ultralytics/yolo-flutter-app

Official Ultralytics YOLO Flutter plugin for real-time inference on Android and iOS across major vision tasks.

What it solves

This project provides an official Flutter plugin that allows developers to integrate YOLO models into iOS and Android applications. It eliminates the complexity of manually managing different mobile AI runtimes by providing a single Dart API for cross-platform computer vision tasks.

How it works

The plugin acts as a bridge to mobile-optimized runtimes: it uses LiteRT (TFLite) for Android and Core ML for iOS. It supports three primary model loading flows: using official model IDs (which are downloaded and cached automatically), loading custom exported models from local assets or files, and loading models via remote URLs. For Android devices with Snapdragon processors, it offers an optional opt-in for Qualcomm NPU (QNN) acceleration via the ONNX Runtime.

Who it’s for

Flutter developers who want to add real-time computer vision capabilities—such as object detection, segmentation, and pose estimation—to their mobile apps without needing to write native iOS or Android code.

Highlights

  • Multi-task Support: Supports object detection, instance and semantic segmentation, depth estimation, classification, and pose estimation.
  • Two Primary APIs: YOLO for single-image inference and YOLOView for real-time camera streams.
  • Automatic Metadata: Automatically infers task types and class labels from embedded model metadata.
  • Hardware Acceleration: Supports GPU and optional Qualcomm NPU (QNN) acceleration on Android.
  • YOLO Family Support: Compatible with YOLO26 and YOLO11 model families.

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