Introducing Google ML kit:
Google I/O 2018 released a kit to its developers known as ML kit. It is a software development kit that brings the Google machine learning tool for both iOS and Android applications.
It has provided enormous powers in the hand of developers like artificial intelligence. Whether you are experienced or not you can easily learn machine learning functionality and comes with ready to use API’s. You can do a lot of machine learning tasks with very little coding.
A developer now has the option to use core ML kit which allows them to create their own model or ML kit which allowers to use google’s inbuilt models and if models are large you can simply put it in Firebase.
Developing an application with machine complexities and sourcing data can be core technical, expensive and, time-consuming. But by using ML kit developers now can use machine learning to build captivating features irregardless of machine learning expertise.
How does it work?
Cloud Vision API allows the developers to easily understand the content about the images through a machine learning model with easy to use API means it makes easy for the developers to integrate vision detection feature.
Mobile Vision API’s has a detector that allows you to detect objects in photos and videos. The cool feature is that it works without cloud you just need to install API in the device. When it comes to Android API it can detect text, barcode, face, and in iOS API you can detect only face and barcode.
Tenserflow Lite A lightweight solution for mobile and other devices. It has machine learning inference with low latency, high performance, and small binary size.
It allows bringing google together cloud vision API, Mobile vision, Tenserflow LIte in Single SDK.
ML kit Vs Core ML kit:
Google ML kit propound machine learning tools to the developers and differ from core ML kit. Google kit makes it easy for the developers to implement two models i.e iOS and Android with some drawbacks too.
ML kit allows you to build your model and if you don’t want to create a custom model you can use google model. It allows you to update your model without recompiling your application. Creating custom model requires Tenserflow Lite and the knowledge of Python.
The drawback of MI is that it supports tenser flow which iOS have no GPU support which can affect the performance of the model.
Core ML supports GPU and is an apple framework. In this, you can create the only custom model. It does not support Android devices.
Features of ML kit:
Barcode feature enables you to scan the encrypted data. A convenient feature to scan the real world information like small text, web address, contact information etc. It can be implemented in an app through Barcode ViewController. It has various key features like:
It read standard formats which include Codabar, Code 39, Code 93, Code 128, EAN-8, EAN-13, ITF, UPC-A, UPC-E, Data Matrix, QR Code, PDF417.
It has an automatic format detection feature which scans the whole image at once without specifying any format.
It automatically parsed structured data which includes URL, text information, email address, contact details, name, Wifi connection information, and geolocation.
It has the advantage to work with any orientation. It can be any left side, right side, up or downside.
Face detection feature detects the human face in a digital image in which image is matched bit by bit. With this, you can get the information to perform a task like detecting a smile on the face, facial motion capture etc. ML kit can perform it live so you can use it in an image app, and game app. It includes features like:
To detect and recognize facial features.
It detects facial expression whether someone is smiling or sad, eyes are open or close.
It extends face detection to video sequences in which each and every face is tracked.
It also finds the landmarks like the left eye, right eye, nose etc.
Image labeling is much easier than face detection. It detects and recognizes the entities in an image either with cloud API or with on-device API. When you this feature you will get entities of an image like text, people, things and so on.
It is easier to have a bigger size model in cloud API than in device API. The label includes People( selfie, smile, crowd), Animals(dog, cat, rabbit), Activities( Signing, dancing) and so on.
Landmark recognition allows you to recognize landmarks in an image along with all the geographical coordinates. To implement this you have to shift your project to the Google cloud version as it is not available on device version.
It includes features like:
Recognize the landmark geographical coordinate as well as the region in which image is found.
By using Google’s knowledge graph entity ID’s you can identify landmark with the first 1000 uses free per month.
It allows you to recognize text in any language. Through this features, you can recognize text in any document, business card, receipt to translate in any language. It works well with the cloud device API.
Custom Model Inference
Machine learning makes it easy for developers to create devices which are easy to use and has a lot of new features. Google has provided out of box solution to the complex problems. Developers just have to learn about machine learning models. Google MI kit is also coming with new features to give more options to developers like a smart reply, which allows the phone to reply internally.