Train Image & Object Detection Models & build Flutter Apps

Train a custom image classification model with Teachable Machine, a no-code platform, and explore audio classification and pose detection, then convert to tflite for Flutter.

Explore data collection for training object detection and image classification models, including public datasets, web scraping, taking photographs, and data augmentation, with emphasis on varied sizes and 50–500 images per class.

Learn to collect data for a fruit recognition model using a Kaggle fruits dataset, gathering at least 40 images per fruit and exploring folders like apples and banana for training.

Train a custom image classification model for Flutter using Teachable Machine, uploading images for four fruit classes—apple, watermelon, pineapple, and banana—and then train the model with default parameters.

Test the trained model with a file or webcam, export to TensorFlow Lite, and download the tflite model and labels for use in a mobile fruit recognition app.

Fine-tune hyperparameters such as epochs, batch size, and learning rate to improve fruit recognition accuracy in Teachable Machine, retrain with added classes, test, and export to tflite for Flutter.

Discover how Google Colab lets you run Python code in your browser with zero configuration, access free GPUs and TPUs, train image classifiers, and pre-process models for Flutter apps.

Attach metadata to a trained tflite model using a Colab notebook, uploading the model and labels, and generating a flutter-ready metadata tflite for fruit recognition.

Apply transfer learning to retrain a preexisting image classification model on a fruits dataset in Google Colab, using MobileNet and EfficientNet, and convert to tflite for flutter apps.

Learn how to prepare and upload a fruits dataset to Google Drive, create a zipped archive, and set up training on Google Colab for a custom image classification model.

Upload your dataset to Google Drive, open the 2023 training image classifier notebook in Google Colab, and train your model, converting to tflite for flutter deployment.

Mount Google Drive in Colab, load and unzip the fruits dataset, and set the image path. Split the data into train, test, and validation sets for training and evaluation.

Train an image classification model via transfer learning by retraining MobileNet v2 on your dataset, set epochs, and monitor training data and validation data loss and accuracy.

Tune hyperparameters to boost model accuracy during retraining. Adjust learning rate, batch size, and epochs, then apply the improvements to MobileNet v2 and efficient Net lite to model for Flutter.

Explore transfer learning with EfficientNet lite 2 for Flutter, retraining on a fruits dataset, evaluating with training, validation, and testing metrics, exporting a tflite model for mobile apps.

Create a cats and dogs dataset, zip it as animal, upload to Google Drive, and run a notebook to train a flutter image classifier with folder, zip, and path.

Create a new flutter project and build a gui for an image picker app that lets users choose or capture images from gallery or camera, displaying the selected image.

Add the image picker package to a flutter project, run pub get, and configure iOS permissions for gallery and camera in Info.plist, noting Android requires no changes.

Learn to integrate an image picker in a Flutter app to select images from the gallery, capture new ones, and display the chosen image.

Capture images in Flutter by adding a long-press listener to a button to launch the camera, retrieve the image as an x file, and display it by updating state.

Test the image picker on a real device by long-pressing the button, opening the camera, capturing an image, and confirming the captured image displays correctly.

Build a Flutter image picker app by creating a project, adding image picker package, and using a picker to choose from gallery or capture with camera, then display the image.

learn to implement image classification in flutter with ML Kit's default image labeling model and custom tflite models, supporting gallery or camera input and real-time results, and compare performance.

Build a machine learning powered flutter app that classifies images from gallery or camera. Use a starter project and image picker code to run image labeling and show results.

Add the Google ML Kit image labeling package to pubspec.yaml, then configure Android and iOS by setting minimum sdk 21, target/compile sdk 33, and iOS deployment target 12 in Xcode.

Integrate ML Kit's image labeling into flutter by creating an image labeler to recognize more than 400 items, process user images, and display predictions with confidence scores.

Learn to integrate custom trained image classification models in flutter using images by loading tflite models from assets via pubspec.yaml and path provider for image labeling and testing.

Compare the trained MobileNet model with the teachable machine model in a flutter app, using model_mobile_net, hot reload, and gallery images to classify fruits.

Apply the Android additional step by configuring your app's build.gradle to prevent tflite model compression during builds, following the library's Android setup docs, then run and test on Android.

Create a new flutter project named live feed app, integrate the camera package to display live footage, and configure Android and iOS settings for emulator testing with machine learning models.

Initialize and use the flutter camera package to display live camera footage on Android and iOS, selecting the back camera and showing a full-screen camera preview.

Learn to build a real-time Flutter app by integrating a camera, listing cameras, initializing a CameraController, and displaying live footage with CameraPreview; pass frames to machine learning models for results.

Import a starter Flutter app from GitHub to process live camera frames for real-time image classification and object detection, using a one-frame-at-a-time pipeline with a busy flag.

Install the google ml kit image labeling package from pub.dev and update pubspec.yaml. Configure android min sdk 21 and ios deployment target 12 in Xcode, including armv7 exclusion.

Initialize a Flutter image labeler, set a 0.5 confidence threshold, and convert live camera frames into input images to feed the ML Kit model, then switch to a custom model.

Process live camera frames in Flutter using ML Kit image labeling, converting frames to input images, and display detected item names with two-decimal confidence scores in real time.

Test real time image classification in a Flutter app using the device camera to label objects like vehicle, bus, and man with ML Kit, achieving high confidence.

Train and integrate your own image classification model in Flutter with live camera footage by loading tflite models via Google ML Kit and configuring assets, path provider, and model path.

Demonstrate real-time image classification in Flutter using live camera footage, achieving high accuracy on watermelon, apple, banana, and pineapple with confidence scores above 90%.

Build a real-time image classification app in Flutter by capturing live camera frames, processing them with an image labeling model, and displaying the label name with confidence in real time.

Learn to build flutter apps that detect and track objects with ML Kit, using images and live camera frames, and recognize them with default or custom image classifiers.

Learn to build an object detection app in Flutter that lets users pick or capture images, run a detector, and draw bounding boxes with object names.

Enable flutter object detection with Google ML Kit. Detect and track objects in images or camera footage, then classify them with a built-in or custom model; configure Android and iOS.

Learn to create an object detector in Flutter by converting a selected image to input image, configuring a single-image object detector with classification using ML Kit, and processing the results.

Pass an input image to a Flutter object detector via process image to obtain detected objects with bounding boxes and tracking IDs, classify them with labels and confidence if enabled.

Pass an input image to the object detection model to obtain detected objects, then draw rectangles and labels on the image using a Flutter custom paint canvas.

Draws the image on a canvas, overlays red bounding rectangles around detected objects, and shows the first label at the top left using a blue text painter.

Learn how to load a custom image classification model in Flutter alongside the ML Kit object detector, configure asset paths, and integrate loading and labeling to identify detected objects.

Test with pineapple, watermelons, and apples to see bounding boxes and correct predictions, noting numbering comes from image metadata, and switch models by name to classify detected objects.

Set up a real-time object detection Flutter app by cloning the starter project, installing it in an emulator or device, and enabling live camera preview with required permissions.

Set up a flutter app to display a full-screen live camera preview, initialize the camera and object detector, and stream frames for real-time object detection with a busy-flag throttling.

Learn to perform real-time object detection in Flutter by converting live camera frames to input images, processing them with an object detector, and drawing rectangles around detected objects.

Learn to draw bounding rectangles around detected objects in real-time on a live camera feed using Flutter, CustomPaint, and an object detector painter.

Test an object detection app on a device with the Malkit library; detect objects, draw labeled rectangles for food, fashion goods, and label them with a trained image classifier.

Demonstrates using a custom image classification model with a real-time object detection app powered by ML Kit, drawing rectangles and labeling detected objects.

Test the real-time object detection app on a device using custom models to recognize apples, watermelons, bananas, and pineapples.

Understand how the object detector painter scales bounding boxes to the live camera size, draws pink rectangles and blue labels, and overlays them on a transparent canvas above the feed.

Learn to implement real-time object detection in Flutter by streaming live camera frames, processing each frame with an object detection model, and drawing labeled rectangles on the camera preview.