ML Kit for Pretrained Models
Explore integrating ML Kit with TensorFlow Lite to deploy pretrained and custom machine learning models into Android apps. Understand how to use ML Kit's APIs for tasks like object detection, face detection, and image labeling. Learn about model installation options and steps to add on-device models to enhance app functionality efficiently.
Google’s mobile software development kit (SDK), ML Kit, provides a set of prebuilt ML models and APIs for mobile and web applications. It simplifies the integration of ML capabilities and functionalities into mobile apps and web applications. It also provides a range of prebuilt models for tasks such as text recognition, face detection, and object tracking.
The ML Kit also provides APIs for custom model integration, allowing us to train our models and incorporate them into our apps. ML Kit uses TF Lite to provide powerful ML capabilities with minimal configuration required. Let’s cover the ready-to-use APIs offered by the ML Kit.
ML Kit APIs
ML Kit APIs can work on-device and on the cloud. The former doesn’t require any internet connections; however, the latter needs an active connection to use the Google Cloud Platform (GCP). We can use ML Kit in conjunction with TF Lite, NNAPI, or Google Cloud APIs.
ML Kit utilizes TF Lite as a backend for running TF Lite models and benefits from its optimizations for mobile deployment.
ML Kit can integrate with Google Cloud APIs of the GCP to leverage their advanced ML capabilities and perform tasks that require cloud-based processing.
ML Kit can leverage ...