Machine Learning use in Flutter - The Complete 2025 Guide

Install the Flutter SDK on macOS by downloading the correct package for your chip, placing it in a development folder, adding Flutter to your path, and verifying with flutter version.

Set up Android Studio to build Flutter apps by downloading and installing Android Studio, agreeing to licenses, and installing the Flutter plugin, then restart to enable Flutter project creation.

Install and configure Xcode on macOS to enable Flutter apps to run on iOS. Set the command line tools in Xcode preferences and prepare Android Studio for iOS development.

Create a new Flutter project in Android Studio, select Kotlin for Android and Swift for iOS, enable platforms, and run the default counter app on iOS simulator and Android emulator.

Learn to set up an android emulator on macOS, create a pixel seven with android 14, download the image, launch the emulator, and install your flutter ml starter app.

Download the flutter SDK for Windows, unzip it, and place it in the C drive. Configure the PATH to access Flutter from anywhere, then run flutter doctor to verify installation.

Install Android Studio and configure Flutter for app development by installing the Dart and Flutter plugins, managing the SDK tools, and accepting licenses to pass Flutter doctor.

Create an Android virtual device in Android Studio using the Virtual Device Manager, pick a device like Pixel 7 Pro, install Android 14, and launch the emulator to test apps.

Build a Flutter image picker app to choose or capture images from the gallery or camera and display the selected image. Prepare the image for use with machine learning models.

Learn to add image picking in Flutter with the Image Picker library, install it via pub.dev, and configure iOS and Android permissions so users can choose or capture images.

Learn to use Flutter's image picker to select images from gallery or capture with a camera, initialize it in initState, and display the chosen image on screen with setState.

Capture images in Flutter by long-pressing a button to open the camera, using image picker with camera source, and display the captured image.

Learn how to build a Flutter image picker app: add the image picker library, request permissions, select or capture images, convert the xFile object to a file, and display it.

Create a new Flutter project, add the camera package, and implement a live camera feed to pass frames to machine learning models in real time, with platform-specific permissions.

Configure a Flutter app to display live camera footage by integrating the camera package, obtaining available cameras, initializing a camera controller, and rendering a CameraPreview with initialization checks.

Capture camera frames one by one in flutter, feed each camera image to a machine learning model in real time, and display the results while validating frame dimensions.

Display live camera footage in a Flutter app using the camera package, configuring dependencies, permissions, and a camera controller for preview and starting image streams.

Learn image labeling in Flutter with ML Kit, recognizing hundreds of items via the default model and optional custom models, and apply real-time labeling from camera or gallery.

Clone the starter Flutter project from GitHub and open it in Android Studio. Install dependencies and run on an emulator to begin building the image labeling app using images.

Learn to add image labeling in Flutter using Google ML Kit, install via pub.dev, and configure iOS and Android deployment targets, SDK versions, and 64-bit requirements for both platforms.

Learn to perform image labeling in Flutter by capturing or selecting images, converting them into the input image format, and processing them with an image labeler to display labeled results.

Display image labeling results in Flutter by updating the UI with a text widget, building a results string from model predictions, and formatting confidence scores.

Add the google ml kit image labeling dependency in pubspec.yaml and initialize the image labeler in init state; label images via the image picker and show names with confidence scores.

Improve the image labeling gui in Flutter by styling the image container and card, add gallery and camera buttons, and customize the app bar with lime accent color.

Learn to improve image labeling visuals in Flutter by wrapping text in a card and container, setting width with media query, and adjusting margin, padding, font size, and colors.

Clone the starter Flutter project from GitHub, import it into Android Studio, and run it in an Android emulator to build real-time image labeling with live camera frames.

Add Google ML Kit image labeling to Flutter, then configure iOS 12 deployment target, Android SDK 31, and target and compile SDK versions 33+ to process live camera frames.

Learn to set up a ML Kit image labeler in Flutter, convert camera frames to input images, and process frames one by one using a busy boolean.

Pass input frames to the image labeler, process images asynchronously, and display each predicted name with its confidence score in real time on the Flutter user interface.

Enhance realtime image classification UI by wrapping the camera preview in an aspect ratio and container, adding rounded corners, margins, and a styled results card with a themed app bar.

Overlay a frame image on live camera footage in Flutter by adding the image to assets, declaring it in pubspec.yaml, and stacking it over the camera preview with fit: BoxFit.fill.

Learn barcode scanning in Flutter using ML Kit to process images from gallery or camera and to perform real-time scanning with live camera feed on Android and iOS.

Build a flutter barcode scanning app using firebase ml kit to scan barcodes from gallery or live camera, set up from github, install dependencies, and run in an emulator.

Learn how to add Google ML Kit barcode scanning to a Flutter app, configure Android and iOS requirements, convert images to input image, and initialize a barcode scanner with formats.

Initialize a barcode scanner in Flutter, process input images to detect barcodes, extract type, bounding box, and values, handle wifi and URL data, and display results in the app.

Review how the starter app uses an image picker and Google ML Kit barcode scanning to detect barcodes, extract type, bounding box, display and raw values, and update the UI.

Build a real-time barcode scanning app in Flutter using Firebase ML Kit with live camera footage and process one frame at a time to display results.

Install and configure Google ML Kit barcode scanning for a Flutter live camera feed, convert frames to input images, process barcodes, and display Wi-Fi and URL data.

Test a real-time barcode scanning app on a real device, revealing wifi credentials and URLs from scanned QR codes. The app accurately detects and displays embedded information from diverse codes.

Explore real time barcode scanning in Flutter by streaming live camera frames to a Google ML Kit barcode scanner. Show results in real time on screen.

Learn to use ML Kit face detection in a Flutter app to detect faces, landmarks, contours, smiles, and eye status in images and real-time video.

Learn to implement face detection in a Flutter app by selecting gallery or camera images, running a detection model, and drawing rectangles and landmarks such as eyes, nose, and lips.

Install the google ml kit face detection library from pub.dev, update pubspec.yaml, and configure android and ios requirements, then initialize a face detector with landmarks, contours, classification, and tracking.

Initialize the face detector in init state to load the model and process images, returning faces with bounding boxes, landmarks, smiling probability, and tracking IDs.

Learn to draw red bounding boxes around detected faces on images in Flutter using a custom paint widget and a canvas, leveraging Firebase ML Kit for face detection.

Draw facial contours by capturing landmark points for detected faces and converting to offsets. Render green points on the canvas to visualize eyes, eyebrows, nose, lips, and the face outline.

Enable facial landmark detection, iterate through detected faces, and draw centered rectangles around landmarks such as left ear, right ear, left eye, right eye, and mouth using canvas drawing.

Explore face classification and emotion detection by using smiling probability from detected faces, applying a 0.5 threshold to label faces as smiling or serious and display results.

Explore how to implement face detection in a Flutter app using Google ML Kit, enabling face position, contour and landmark detection, plus smiling vs serious classification, with drawn overlays.

Build a real-time face detection feature in Flutter using live camera footage, starting from a GitHub starter app, and learn to switch between front and back cameras.

Learn to build a Flutter app that presents live camera footage in an emulator, toggles front and back cameras, and performs face detection with Google ML Kit on frames.

Learn to add Google ML Kit face detection to Flutter by updating pubspec.yaml and configure contour and landmark options to detect faces from live camera frames.

Draw rectangles around detected faces in real time by layering a custom painter over live camera preview in Flutter, handling front and back cameras with the scan result and direction.

Apply the face detector painter to draw red stroked rectangles around detected faces by scaling model points to the live camera in a custom paint widget.

Test real-time face detection on a device by toggling between front and back cameras, with the model continuously detecting faces and drawing moving rectangles.

Enable contour detection to draw facial contours in real time on the live camera feed. Convert contour points to offsets and render them with a green stroke.

Test real-time facial contours detection on a real device, drawing a rectangle, contour points, and facial landmarks on faces as the camera moves, including back camera testing.

Explore real-time face detection in Flutter apps using Google ML Kit, camera live preview, and a custom painter to draw rectangles and facial contours for front and back cameras.

Explore object detection in Flutter using ML Kit to recognize and locate objects in images or live video, draw bounding boxes, and enable real-time on-device tracking.

Build a Flutter object detection app that lets users pick images from gallery or capture with camera, runs a detection model, and draws bounding rectangles around detected objects.

Learn to implement object detection in a Flutter app using the Google ML Kit base model, including image conversion, detector setup, and drawing bounding boxes with labels and confidence.

Draw rectangles around detected objects by rendering the image on a custom paint canvas and drawing red bounding boxes from the object detector.

Learn to display the names of detected items by drawing labels at the top-left of each bounding box using a text span, layout, and text painter in Flutter.

Learn to set up a real-time object detection app in flutter by integrating live camera feed, cloning starter code, and handling one frame at a time for live detection.

Detect objects in live camera frames within a Flutter app using Google ML Kit, converting frames to input images and streaming detections with bounding boxes and labels.

Draws rectangles around objects detected by the model on live camera footage by overlaying a transparent view and using a custom painter to map model results to screen coordinates.

This lecture demonstrates realtime object detection testing in a Flutter ML workflow, drawing bounding boxes and labeling detected objects like monitors, keyboards, glasses, headphones, and shoes.

Learn how to perform real-time object detection in Flutter using the ml kit base model, draw rectangles around detected objects, and display the most confident label for each.

Explore real-time object detection in a Flutter app, drawing names of detected classes as the camera identifies shoes, flowers, and a monitor, and classifies each item.

Explore real-time object detection in Flutter with the ML Kit base model, using a live camera feed and overlaid rectangles with class names drawn by a custom painter.