Hello Android developers, Previously we looked at how to pre-process image inputs for an ONNX model using Kotlin. It’s useful to understand this process because the principles apply to any model that you wish to use. On the other hand, having to write boilerplate code for input processing can be tedious – it also means there’s more...
Hello Android developers, One of the advantages of the ONNX runtime is the ability to run locally on a variety of devices, including mobile devices. This means that your users get fast response times, but also comes with the need to respect mobile device limitations such as app size and the ability to support performance enhancements...
Hello Android developers, Last week we got an ONNX runtime demo running on Android, which classified the subject of images being streamed from the device’s camera. Setup required downloading a pre-trained model and adding it to the sample app on GitHub. This week we’re going to look into the details of preparing inputs for the model...
Hello Android developers, This week we’re going to get started with on-device machine learning using the ONNX Runtime and check out an Android sample that identifies the objects using the camera video stream. What is ONNX? ONNX stands for Open Neural Network eXchange and is an open-source format for AI models. ONNX supports ...
