flutter_vision

A Flutter plugin for managing Yolov5, Yolov8, and Yolov11 accessing with LiteRT (TensorFlow Lite). Support object detection and segmentation on Android. iOS not updated, working in progress.

Installation

Add flutter_vision as a dependency in your pubspec.yaml file.

Android

In android/app/build.gradle, add the following setting in android block.

    android{
        aaptOptions {
            noCompress 'tflite'
            noCompress 'lite'
        }
    }

iOS

Coming soon ...

Usage

For YoloV5, YoloV8, and YoloV11 MODEL

Create a assets folder and place your labels file and model file in it. In pubspec.yaml add:

  assets:
   - assets/labels.txt
   - assets/yolovx.tflite

Import the library:

import 'package:flutter_vision/flutter_vision.dart';

Initialized the flutter_vision library:

 FlutterVision vision = FlutterVision();

Load the model and labels: modelVersion: yolov5 or yolov8 or yolov8seg or yolo11 or yolov11

await vision.loadYoloModel(
        labels: 'assets/labelss.txt',
        modelPath: 'assets/yolov5n.tflite',
        modelVersion: "yolov5",
        quantization: false,
        numThreads: 1,
        useGpu: false);

For camera live feed

Make your first detection: confThreshold work with yolov5 other case it is omited.

Make use of camera plugin

final result = await vision.yoloOnFrame(
        bytesList: cameraImage.planes.map((plane) => plane.bytes).toList(),
        imageHeight: cameraImage.height,
        imageWidth: cameraImage.width,
        iouThreshold: 0.4,
        confThreshold: 0.4,
        classThreshold: 0.5);

For static image

Make your first detection or segmentation:

final result = await vision.yoloOnImage(
        bytesList: byte,
        imageHeight: image.height,
        imageWidth: image.width,
        iouThreshold: 0.8,
        confThreshold: 0.4,
        classThreshold: 0.7);

Release resources:

await vision.closeYoloModel();

About results

For Yolo v5, v8, or v11 in detection task

result is a List<Map<String,dynamic>> where Map have the following keys:

   Map<String, dynamic>:{
    "box": [x1:left, y1:top, x2:right, y2:bottom, class_confidence]
    "tag": String: detected class
   }

For YoloV8 in segmentation task

result is a List<Map<String,dynamic>> where Map have the following keys:

   Map<String, dynamic>:{
    "box": [x1:left, y1:top, x2:right, y2:bottom, class_confidence]
    "tag": String: detected class
    "polygons": List<Map<String, double>>: [{x:coordx, y:coordy}]
   }