Who is this for?
This Learning Path is for Android and machine learning developers who want to run an optimized image-segmentation model locally on an Arm-based Android phone.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Prepare the Android command-line tools and connect an Arm-based Android phone
- Download and run the MobileSAM ExecuTorch model from the Arm AI Portal
- Understand how the Android adapter validates, prepares, and renders MobileSAM results
- Optionally inspect an unsupported image model before implementing a model-specific adapter
Prerequisites
Before starting, you will need the following:
- A macOS, Linux, or Windows development computer with Git, Python 3, and Java 17 or later
- An Arm-based Android phone running Android 9 or later
- A Hugging Face account
- A data-capable USB cable
- Basic familiarity with terminal commands and Android applications
- Network access for the first Gradle build and model download
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
adb devices -l. The output should list the device with its serial and show it as authorized. If it reports unauthorized, unlock the phone and accept the debugging prompt. On Windows, you might also need the phone manufacturer’s USB driver.java and javac commands must report JDK 17 or later so that Gradle can start. The project configures Gradle to use a compatible JDK 17 build toolchain and download one automatically when it isn’t already available.mobile_sam_raspberry_executorch_optimized.pte. Copy it into the directory named by the MobileSAM catalog entry under application-private storage, then start Image Analysis. Select Load model, choose an image, and select Run segmentation to generate the mask.