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Everything You Need To Know About ML Kit For Firebase

Post by|Mobile Apps31 December,2020
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In this day and age of automation, Machine learning is a promising candidate. Not only can you use it for automation, but its application stretches out to the mobile application arena as well. Machine Learning is the future of apps that a mobile app developer can not deny. It customizes apps to users’ needs and makes them more effective, efficient, fast, and safe. Many businesses are investing heavily in machine learning to make the best of it.

There are multiple ways to implement Machine Learning in Android, but Firebase introduces the most potent & most comfortable way to implement machine learning in Android apps. Many developers are facing difficulties while implementing even low-level models in Machine learning. Also, the learning process is time consuming & expensive.

With Firebase ML Kit, you can implement machine learning AI-based models in Android and iOS apps irrespective of your machine learning expertise.

Features Of Firebase ML Kit:

Text Recognition

This is a prevalent feature of ML Kitnowadays. ML Kit is providing text recognition APIs using which you can detect any of the Latin based languages. Text recognition will simplify the time-consuming entry of credit cards, receipts, business cards, etc. Using this feature, we can get the text from any media file like an image or documents. Read world objects can also be detected, i.e., by reading the numbers on the train.

Face Detection

ML Kit’s face detection API lets you detect faces in the form of images, it can detect contours of the faces and also recognize vital facial features. With face detection, users can separate faces from images and edit them using different filters. We can also create the avatars from the detected faces. This feature helps mobile apps for video chats or games in which operation should be performed using facial expressions. Face detection APIs can also get the coordinates of the eyes, ears, cheeks, nose, and mouth of every face detected.

Barcode Scanning

With the barcode scanning API of ML Package, you can read encoded data using most standard barcode formats. The most important thing for this feature is that without having an internet connection, the encoded data will be obtained by extracting the barcode. The specialty of Barcodes is that they can be recognized and scanned in any way possible – upside up or sideways.

Image Labeling

With ML Kit’s image labeling APIs, you can identify entities in an image without the need for additional contextual metadata, using either an on-the-fly API or a cloud-based API. All the objects from the image can be recognized: animals, people, buildings, places, plants & so on. This feature is also available in offline mode & on the cloud.

Object Detection & Tracking

You can locate and monitor the most prominent objects in an image or live camera feed in real-time with the on-screen object detection and tracking API from ML Kit. Optionally, observed objects may also be categorized under one of several general groups. This is very useful to construct live visual search experiences. Mobile devices simplify the object detection and tracking paradigm for use even on low-end devices in real-time applications. You can move them on to a cloud backend, such as cloud vision product search, or to a custom model, such as a model trained by AutoML Vision Edge after you have identified and filtered objects.

Landmark Recognition

Using this API of firebase, well-known from the images will be recognized. To get the results / list of landmarks from the image, developers must pass an image in the API. With the landmark details, geo coordinates of all the detected landmarks will also be defined. You can automatedly produce image metadata by using this information, create individual experiences for users based on their content and more. One limitation is that this feature will only be used if you are having internet connection.

Language Identification

The API helps you to recognize the language from texts. This function can be beneficial for interpreters or researchers who need to know which language is written in a picture or text.

Translation

This API is used for translating the text into different available languages. Users can easily change the languages to translate it from one language to another. In this API, the same models are used which are used in the Google Translator app. In other terms, different combinations of translations can be picked with 59 available languages.

Smart Reply

This feature is used for showing suggestions for chat apps or providing replies in any casual conversations. These APIs are very useful to build any small chatbot app or add chatbot functionality in any app. One limitation of this API is that this API currently supports only the English language to provide suggestions.

AutoML Model Inference

This feature is used to train image labeling models to distinguish & identify different types of images. These API models are developed for general use and are prepared to identify the most common concepts found in images. You can train a model with your images and categories using Firebase ML and AutoML Vision Edge. The individual model is introduced by Google Cloud and is wholly utilized on the computer until the model is ready.

Custom Model Inference

This functionality is available to create our custom models using MLKit. They are used primarily by skilled developers who didn’t find any appropriate model from ready-made models. To create custom models, developers may use the TensorFlow Light model.

Note: This is a beta release of Firebase ML. This API might be changed in backward-incompatible ways and is not subject to any SLA or deprecation policy.

Advantages:

There are numerous benefits of ML Kitin the world of Machine learning. Let’s discuss a few of them:

  • The main advantage is firebase ML Kit provides a friendly & comfortable environment to implement machine learning with no cost.
  • Implementing machine learning features in android & iOS apps is not an easy task for the developers. A high skill set & a large amount of time is required to develop machine learning. But with the use of firebase MLKit, developers can quickly achieve machine learning goals with less effort. Developers need to do only several lines of code & pass the data in ML KitAPIs.
  • The hunt for super-precise, well-trained machine learning models, can be challenging and challenging for the platform to choose which ones. ML Kit provides lots of readymade models to use according to the requirements of Machine learning. It is also providing options to generate custom models. This is a very excellent option for skilled developers. If there is no existing ML KitAPIs, developers can construct their custom ML Models.
  • Some of the APIs can also be used in offline mode without having any internet connection. This is a significant advantage of ML Kit support machine learning features without any internet connection.
  • ML Kit SDK is an API for iOS and Android apps. It is an API framework. Therefore a new ML Kit will contend and even one day beat CoreML from Apple. Today, however, CoreML provides more benefits than the ML Kit. CoreML, for example, uses TensorFlow and accepts Apache MXNet, ONYX, Python Tools.
  • A lot of items to make your application work are suggested by Firebase. You can pick two databases (real-time and Firestore databases), store cloud media, and even create serverless applications using integrated Cloud functions.

Drawbacks:

With the bunch of benefits, there are few drawbacks of ML Kit which should be taken into consideration at the time of development:

  • As we can create custom models, but after creating the custom model, the application may become very heavy & bulky. The file size is therefore considerably more significant than a standard app size. And for developers or users, it won’t be healthy.
  • The other issue of ML Kitis, as we discussed above, still firebase ML Kitis in the beta version, so developers have to face some of the problems while implementing it. There is no confirmation for a new release in the future.

Wrapping Up

Machine learning is just the beginning of automation. Businesses have leveraged ML for the past couple of years to their advantage, and ML Kit for Firebase does the same thing. It is a mobile SDK created to bring machine learning expertise in Andriod and iOS within a few simple coding lines. We have listed all the essential details about the Machine Learning kit in firebase for your Andriod and iOS device through this blog. Using this, you can leverage this marvel for your gain!

FAQ

Using Face Detection APIs, can we get the accurate coordinates of the face with the movements of the face?

No. As it is still in the beta version, we can get the exact coordinates of the steady face, but with the movements of the face, the accuracy level may be decreased. 

Which are the machine learning libraries to construct custom models?

Well known libraries are: TensorFlow, Torch, PyBrain, Azure.


Speak Your Mind

  1. Bhavik says:

    Interesting article. Nicely summarizes the features of Firebase. Can you use the Face Detection for authorization purpose?

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    • Manali Shah says:

      No using Face Detection feature of MLKit, we can detect the faces only but we can not recognize / compare the faces for the authorization process.

      One possible way is to create custom model for recognizing faces & train that model using some of the default images of face. But it’s very tricky & lengthy process.

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  2. Paarth says:

    isnt ml kit replace by fireface ml in android studio?

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    • Manali Shah says:

      Yes. In this blog, we have considered firebase MLKit only. We are also going to publish new article on how to implement Firebase MLKit in Android Studio soon. You will get better idea from that article on this.

      Stay Tuned !!

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