Train & Deploy TFLite Object Detection for Android (2025)

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.

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.

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 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.

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 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.

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.

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.

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 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.