Machine Learning use in React Native - The Practical Guide

Create a new React Native project and learn to implement an image picker to capture or select images from the gallery. Prepare these images for a machine learning model.

Create a practical React Native GUI for image input by adding an image picker, enabling gallery and camera capture, and wiring a responsive UI with file and camera buttons.

Learn to integrate the React Native image picker to select images from the gallery, handle user cancellation, and display the chosen image in a React Native app.

Learn to capture images in React Native by launching the device camera via an image picker library, then display captured images from camera or gallery on emulator or real device.

Learn to scaffold a react native project, install and import an image picker library, and use gallery and camera features to capture images and update ui via state.

Explore how to use pre-trained machine learning models in a React Native app, covering image classification, object detection with SSD MobileNet and YOLO, pose estimation, and image segmentation.

Train and deploy machine learning models using labeled images to recognize fruit categories and other objects, enabling image classification, object detection, and image segmentation in real apps.

Learn how to run machine learning models on mobile devices with TensorFlow Lite by converting models to tflite format and using the TensorFlow Lite interpreter for on-device inference.

Build a React Native app that uses MobileNet image classification to identify 2000 classes from gallery or camera photos, operating at 224 by 224, with real-time predictions from camera frames.

Set up a React Native project for image classification with a machine learning model, install and link the library, and add the model and label files for Android and iOS.

Import the library into a React Native project, initialize it, and load the model and label file to enable image classification.

Learn to perform image classification by passing a selected image to a classification model using the run model an image method, with mean 128, std 128, and top 3 predictions.

Show image classification predictions on screen in a React Native app by storing a prediction list in state and rendering each label with its percentage confidence beneath the image.

Review progress on a React Native image classification app, including image capture with the image picker, loading a model and labels from assets, and displaying classification labels with confidence.

Explore quantization in TensorFlow Lite, converting 32-bit floating point values to 8-bit integers to shrink model size and latency for Android apps, with minor accuracy impact.

Launch a new React Native project with a command-line interface, install the React Native Camera package, and build a live feed image classification app using camera frames.

Learn to perform image classification with live camera footage in a React Native app by integrating React Native camera, linking the package, configuring Gradle and assets, and running real-time predictions.

Learn to render live camera footage in a React Native app and run a mobile net model on each frame, displaying the classification outputs on screen.

Show live image classification by formatting the model output: round the confidence to two decimals, sort predictions by descending confidence, and display the top three on screen.

Learn to build a React Native app, integrate and run a machine learning model, display live camera footage, process results, and customize the UI with adjustable view and camera sizes.

Learn object detection in React Native by comparing image classification and detection, using SSD Mobile Armada and tiny YOLO to detect objects and draw bounding boxes.

Build a React Native object detection app that locates items and draws bounding rectangles, starting from the starter folder and running on Android emulator with the Flight React Native package.

Load the ssd mobilenet model and label files in a react native app, then perform object detection on images with a 30% threshold and per class limits.

Learn to parse the array of predictions from the model output, extract class names and confidence, scale bounding boxes to image size, and render rectangles in React Native.

Draw red-bordered rectangles around detected objects by overlaying absolute-positioned views on the image, scale prediction coordinates to image size, and assign unique keys to each overlay.

Show how to display the detected class name with a text component over the image and draw rectangles for each prediction using a YOLO model in React Native.

Switch to yolo v2 for object detection in a React Native app, loading the model and labels, and using detect objects to image with adjusted parameters.

Explore pose estimation to detect and track body joints like elbow, knee, and wrist, and learn to build a React Native app that runs the model and draws joints.

Build a pose estimation app in React Native by importing starter code, loading a model, and drawing body joints such as elbows, wrists, shoulders, and hips from gallery or camera.

Learn pose estimation in react native using the posenet model: load the net model, pass an image to the model, and draw points at body joints.

Demonstrate pose estimation in a React Native app by identifying multiple people and rendering body joints as labeled key points with confidence scores on the image.

Explore how image segmentation classifies each pixel and separates foreground from background. Use the D-Black model to segment images from camera or gallery and display the results.

Learn to build an image segmentation app in React Native from a starter project, use image picker or camera, and load the model and label file to perform segmentation.

Load the deeplab image segmentation model in a React Native app, then perform segmentation on an image and render the output overlay with adjustable opacity.

Apply image segmentation in a React Native app by capturing an image, generating a transparent PNG mask, and overlaying it with adjustable opacity to visualize pixel-wise object classification.

Train your own image classification models from custom datasets using a digital machine platform and transfer learning, then deploy them in React Native apps with live camera recognition.

Master how to train an image classification model for mobile apps by collecting dog and cat images, training and testing, and converting to rdf light format with the flashlight framework.

Prepare a dog breed recognition model by collecting or selecting image datasets, then train and integrate it into your app using sources like Kaggle and Google data science resources.

Extract and organize the downloaded dog image dataset into ten breed folders to structure the training data for image classification.

Train an image classification model using Teachable Machine, create 10 dog breed classes, upload images, adjust hyperparameters like epochs and batch size, train, test, and export for mobile apps.

Test the trained model with image files to observe its predictions on Afghan hound and boxer images. Export and download the model in standard flow light format for mobile use.

Train and test a dog breed recognition model with Teachable Machine using ten classes. Convert the trained model to a flight format and download it for the mobile app.

Deploy a dog breed recognition model in a React Native app by replacing the model and label files, then run on Android to test predictions and verify accuracy.