Train & Deploy TFLite Object Detection for Android (2025)

Identify ways to collect datasets for object detection: open datasets, web scraping, photographs, and data augmentation; emphasize varied object sizes and 150–500 images per class while avoiding diagonal placements.

Use an existing fruits dataset from Kaggle to collect training data for an Android object detection model, exploring train, test, and validation folders and downloading after signing in.

Unzip and curate a train, test, and validation dataset of four fruits—apple, pineapple, watermelon, kiwi—for Android object detection training. Annotation is covered in the next lecture.

Annotate images by drawing bounding boxes around each object, naming them to train an object detection model, while ensuring pixel-perfect boxes, avoiding diagonal items, and using Roboflow for free annotation.

learn to annotate a fruit dataset for android object detection using roboflow, create a workspace and project, upload images, and manually annotate before training.

Annotate images for an Android object detection model by drawing bounding boxes, creating class labels like pineapple, watermelon, kiwi, and apple, using Roboflow for annotation and Google Colab for training.

Annotate images, export them to the dataset for model training, delete the mistaken class and rename objects as needed, and finally export the updated dataset for training.

Export augmented dataset by creating a version, selecting images, and configuring train, validation, and test splits. Export to Pascal VOC and download the zip from Roboflow to train Android model.

Use Roboflow’s health check to review your annotated Pascal VOC dataset and plan augmentation and data adjustments before training your object detector.

Train an object detection model on your annotated dataset using transfer learning from models such as SSD, MobileNet, or YOLO, via Google Colab, then convert to TensorFlow Lite for Android.

Unzip the dataset, reorganize train, test, and valid folders by placing all xml files in an annotations folder and all images in an images folder, then zip for training.

Master Google Colab, a browser-based Python platform with free GPU, to train your object detection model and visualize data with NumPy and Matplotlib.

Upload the training notebook to Google Colab and run the Python code to train a TensorFlow Lite object detection model with Mediapipe model maker, after uploading the dataset zip.

Learn to set up MediaPipe Model Maker, unzip and prepare train, validation, and test datasets, convert them into dataset objects, and start training an object detection model for Android.

Train a custom object detection model by retraining a MobileNet v2 with Mediapipe model maker, tuning batch size, learning rate, and epochs on a dataset with validation and export.

Test your trained tflite object detection model using average precision and IOU to gauge prediction accuracy, with a 0.5 IOU threshold guiding correct detections.

Explore how the custom object detection model improves across epochs, evaluates on a test set with iou-based metrics and average precision, and exports the model to tflite.

Learn to use TensorFlow Lite for on-device machine learning: convert models to tflite and run them with the interpreter in mobile apps, delivering low latency, privacy, and offline capabilities.

Export a trained object detection model to TensorFlow Lite with the export_model function, naming it fruits.tflite in the exported model folder. Download the tflite file for use in mobile applications.

Learn how to improve custom object detection models for edge devices by tuning hyperparameters (epochs, learning rate, batch size), expanding datasets, and trying MobileNet variants to boost tflite performance.

Learn to create an Android Studio Java project and build an image picker app that selects from the gallery or captures photos for ML model input, with runtime permissions.

Capture and display images in an Android Java app by long-clicking an image view to open the camera, manage permissions, and show the captured image.

Capture images in your Android app, convert them to bitmap for machine learning models, and verify the conversion by displaying the bitmap before selecting images from the gallery.

Tap the image view to open the gallery, select an image, convert it to a bitmap, and display it; later, pass the bitmaps to machine learning models for predictions.

Learn to create a Kotlin Android Studio project that lets users select or capture images, display them in an image view, and handle camera and storage permissions.

Capture images in your Android Kotlin app by long clicking on the image view to open the camera, handle runtime permissions, and display the captured photo in the image view.

Convert captured images to bitmap for machine learning in an Android Kotlin app using a uri to bitmap method and display via bitmap; next lecture covers choosing images from gallery.

Learn to integrate gallery selection and camera capture in an Android app, handling intents, activity results, image URIs, and dynamic permissions for preparing images for a machine learning model.

Learn to integrate a tflite object detection model into an Android app by importing a starter project, selecting or capturing images, and passing them to the model to obtain results.

Discover how to add a trained object detection model to an Android project by placing the model in assets, configuring Gradle, and loading it with the MediaPipe library.

Use an object detector helper to load the model in an android java app, pass input images, set threshold 0.5 and max results 10, and choose cpu or gpu delegates.

Pass input images to the object detector in Android, obtain results via a result bundle, draw bounding rectangles and labels for detected objects, and log the model's inference time.

Learn to extract object detection results from a result bundle, iterate detections to obtain bounding boxes, names, and maximum confidence, and prepare to draw labeled rectangles around detected objects.

Draw rectangles around detected objects on a mutable bitmap in Android using a canvas and a styled paint. Display object names and confidence in the image view.

Draw the detected object's name at the top left of each bounding box with canvas.drawText, using the bounding box's left and top for position and a paint object.

Import a starter app from GitHub, select or capture an image, perform object detection with a tflite model via the object detector helper, and display results with bounding boxes.

Import starter Android Kotlin app code, clone from GitHub, and run in an emulator to pass selected or captured images to the object detection model, drawing bounding boxes and labels.

Integrate an object detection model into an Android Kotlin app by adding the tflite model to assets, configuring Gradle for Mediapipe, adding the library, and syncing with SDK updates.

Load the fruits.tflite object detection model in an Android Kotlin app with the object detector helper, pass input images, and obtain detections through inference.

Convert the input image to a bitmap and pass it to the object detection model via the detect image method, retrieving results from the result bundle and logging inference time.

Iterate detection results to extract each object's name, location, and maximum confidence, then display these details in logcat and prepare for drawing bounding boxes.

Learn to draw bounding boxes around detected objects on a bitmap in Android by converting to a mutable bitmap, obtaining a canvas, and drawing blue stroke rectangles.

Draws the detected object name at the top left of each bounding box on images in an Android Kotlin app, using canvas draw text, paint properties, and dynamic sizing.

Dynamically size detection rectangles and text in an Android Kotlin app by deriving dimensions from the image width and converting to float, with tests on kiwi, watermelon, apple, and pineapple.

Use the rotate bitmap method to remove rotation from captured bitmaps on Android devices with landscape cameras, improving object detection results.

Import and set up the android studio project from GitHub to run a real-time object detection app with the fruits recognition model on live camera footage.

Test a real time object detection model on an android app using a live feed, detecting apples, kiwis, watermelons, and pineapples with high accuracy and speed.

Learn to display live camera footage in an Android app, capture frames, run a real-time object detection model, and draw rectangles on screen.

Capture Android live camera frames with an own image available listener, convert frames to bitmaps, and feed them to an object detection model one frame at a time.

Load the object detection model via the object detector helper and pass live camera frames as bitmaps for detection, extracting names, locations, and confidence.

Draws object detection results on Android by converting camera frames to bitmaps, passing them to the model, and rendering rectangles with names via a Multibox tracker on an overlay view.

Clone the repository from GitHub, import into Android Studio, and run the real-time fruit detection app using live camera footage. Test with fruits.tflite by updating the model in main activity.

Showcases a real-time Android Kotlin object detection app tested on a live feed, detecting apples, kiwis, watermelons, and pineapples with high speed and accuracy.

Learn to display live Android camera footage using the Camera2 API, convert frames to bitmaps, run object detection on continuous frames, and render real-time detection rectangles on screen.

Capture live camera frames, convert each frame to bitmap via image reader and RGB conversion, and feed bitmaps to the object detection model with controlled frame processing.

Learn to run object detection on live Android camera frames by converting frames to bitmaps, loading a MediaPipe tflite model, and rendering real-time detections with names and confidence.

Convert live camera frames to bitmaps, run real-time object detection, and draw rectangles with labels on a tracking overlay in the Android Kotlin app.

Learn to integrate EfficientDet single-shot object detection models into an Android app using image and live camera footage to detect up to 90 objects and draw rectangles around them.

Learn to integrate EfficientDet object detection models in Android apps, handling images and live camera feeds, loading models from assets, and selecting Lite zero to Lite three for testing.

Test the Android implementation of EfficientDet object detection by selecting gallery images to verify detections of buses, cars, trucks, and refrigerators, including multiple items detected simultaneously.

Specify an efficientdet light model name and run the app on your device to compare performance; larger models offer slightly higher accuracy than lighter ones.

Learn to deploy EfficientDet object detection models on live Android camera footage by updating your app with the model name and running on a real device.

Test the EfficientDet lite three model with live Android camera footage for stable predictions. Predict keyboard, TV, laptop, and mouse with over 90% laptop confidence, slower than lite zero.