How to Create a YOLOv11 Android App
Blog post from Roboflow
In an informative guide by Aryan Vasudevan, the process of integrating vision AI capabilities into an Android application using a custom YOLOv11 model is outlined, emphasizing object detection for creating features like counting and classification. The guide details the development of a custom coin-counting app, involving training a YOLOv11 model to detect different coin denominations, converting the model to TorchScript for Android compatibility, and implementing it into an app using Android Studio. The app, featuring a user interface that facilitates image selection from a gallery, utilizes the model to predict and display detected coins and their total value. Key components include the YoloModelManager, responsible for model loading and inference, and the ImageProcessor for image preparation, ensuring the app processes images correctly for accurate detection. The project showcases the deployment of advanced computer vision technology on mobile devices, providing a practical application of object detection models in everyday tasks.