An on-device embedding based search package is been introduced by Tensorflow which could be run on android, ios and web applications. It runs with help of the Edge ML technique. This on-device package could help the user to search images, text or audio in just a snap of time. In this article, we would learn the implementation of on-device text-to-image search with TensorflowLite. Following are the topics to be covered.

In this blog post, we will learn to create a TensorFlow Lite model using the TF Lite Model Maker Library. We will fine-tune a pre-trained image classification model on the custom dataset and further explore different types of model optimization techniques currently supported by the library and export them to the TF Lite model. Detailed performance comparison of the created TF Lite models and the converted one is done, followed by deploying the model on the web app in the end. The TensorFlow Lite Model Maker Library enables us to train a pre-trained or a custom TensorFlow Lite model on a custom dataset. Similar to the previous blog, we will be using Microsoft's Cats and Dogs Dataset.

Learn how to train a TensorFlow Lite model that can recognize custom images using TensorFlow Lite Model Maker. Learn how to integrate the model into an Android app using the new ML Model Binding plugin in Android Studio 4.1 beta Khanh shows you how to train a TensorFlow Lite model that can recognize custom images with your own dataset using TensorFlow Lite Model Maker. Then, Hoi shows you how to integrate the model into an Android app using the new ML Model Binding plugin in Android Studio 4.1 beta. Resources: Codelab which goes through all the steps in this screencast https://goo.gle/TFCodeLab Check out the website https://goo.gle/30FDT8S

Ever since Android first came into existence in 2008, it has become the world's biggest mobile platform in terms of popularity and number of users. Over the years, Android developers have built advances in machine learning, features like on-device speech recognition, real-time video interactiveness, and real-time enhancements when taking a photo/selfie. In addition, image recognition with machine learning can enable users to point their smartphone camera at text and have it live-translated into 88 different languages with the help of Google Translate. Android users can even point your camera at a beautiful flower, use Google Lens to identify what type of flower that is, and then set a reminder to order a bouquet for someone. Google Lens is able to use computer vision models to expand and speed up web search and mobile experience.

Google today announced TensorFlow Lite Model Maker, a tool that adapts state-of-the-art machine learning models to custom data sets using a technique known as transfer learning. It wraps machine learning concepts with an API that enables developers to train models in Google's TensorFlow AI framework with only a few lines of code, and to deploy those models for on-device AI applications. Tools like Model Maker could help companies incorporate AI into their workflows faster than before. According to a study conducted by Algorithmia, 50% of organizations spend between 8 and 90 days deploying a single machine learning model, with most blaming the duration on a failure to scale. Model Maker, which currently only supports image and text classification use cases, works with many of the models in TensorFlow Hub, Google's library for reusable machine learning modules.