I want to know the feasibility of an android app which I am going to build for my College project.
The App, which I am trying to build is for attendance on the class through voice recognition or face detection.
For this, I suppose to first collect the data set for all the student of the class and then train it.
so, Is it feasible to build such an app and how to approach this?
I am new in Tensor flow and ML and also searched about this on the internet but unsuccessful to find anything so please help me come out from this. Your help is appreciated.
You will have to train and use a custom model for this.
ML Kit offers face detection but does not offer face recognition or voice recognition at the moment. So you will have to collect data and train a model yourself. You can look at the quickstart samples for iOS and Android on GitHub and learn about using mobile-optimized custom models in your app.
Related
I am creating a app using
google_ml_kit
to create face recognition. I have successfully implemented the face recognition using flutter(front end),node js(back end) and mongo database. now I have to create Workout Movement Counting example(dumbbell count). can anyone please let me know it is possible with google_ml_kit package. If yes, please share some tips which will helps me a lot.
Thanks in advance!!!
The ML Kit's Android vision quickstart app provides an example to do rep counting with Pose Detection. https://github.com/googlesamples/mlkit/tree/master/android/vision-quickstart
Please search for "Pose Detection" on the page linked above and see instruction about how to enable classification and counting in the demo app.
Here is the most related code: https://github.com/googlesamples/mlkit/tree/master/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/java/posedetector
The implementation is in Java, but it is just algorithm, so you should be able to convert it to Dart I guess (I am not familiar with Dart personally).
#all I have implemented the dumbbell count using tflite plugin. If you need source code or support comment below.
I am new to android app development. I am currently developing an app using speech recognition module provided by android.
My app gets output from the speech recognition module and process the output to classify them into groups. This classification does not rely on machine learning, but just a rule/dictionary base classification/grouping.
I would like to know if there is a way to test the app scalable manner. For example, there is a certain size of dataset of text to synthesise its pronunciation and put them into the module and check the classification result.
I googled it for a few days but no luck so far.. If you know the test method for an app including speech recognition module, please advise me!
Thank you so much for your help in advance!
I need to detect the faces of any animal say Cat and match that to stored photo from database in Android app.
Is this possible to implement through Firebase Face detection APIs OR should I use TensorFlow lite's object detection ?
I do not think that you can detect an animal's face through the Firebase Face Detection API, because I think it is only suitable for human faces since it allows you to detect features such as facial expression. However, if you want to detect whether there are cats in the frame of your camera, your best bet would be to use the Object Detection API. Additionally, if you train your model well it will be able to detect whether there is a cat's face present in the frame. Once you have your model it is rather straightforward on how to integrate it within your app by using Firebase's ML Kit - for reference you can take a look at their quick start example.
The ML Kit Image Labeling API should get you what you are looking for. It's available for on-device or cloud processing. The on-device API can distinguish between 400+ labels, including identifying whether there's a cat or a dog in an image. If you are looking for something more specific you can build your own custom model using AutoML Vision Edge in ML Kit, or using TensorFlow Lite directly.
I'm trying to detect the text on the digital led displays of some device like below and need help on doing that. I tried the sample for text detection that Google provides with firebase ML kit but does not perform well on device(not cloud).
Help me out on how to optimise the accuracy for on device model.
I'm looking for suggestion for doing it the right way with ML kit or are there any other alternatives that are easy than this like OpenCV, etc.
how to optimise the accuracy for on device model?
Unfortunately, you cannot optimize the accuracy of the out-of-box API models in ML Kit. We will update the model to better recognize these images.
are there any other alternatives?
Unless there is a library that performs at the level you are satisfied with, you will have to train your own model. You could potentially start with an open source model and do transfer learning for your own use case without too much data. You can look at Tensorflow-for-poets-2 codelab for a quick tutorial on how to do this. Another option is to look at TF Hub for reusing existing models easily.
Then to deploy your model onto the device for inference, please take a look at using your own custom model in ML Kit.
Known that I'm very new in Machine learning.
I was thinking about a real world example of using Machine Learning
and Neural network in an application and I want to try it with a
mobile application who can handle image recognition with the front
camera after make an image of something(A cat for exemple).
I really need advice of tools to use to rapidly make a prototype of this application with a python backend that I will call via rest.
Thanks in advance.
I suggest if you are new to the machine learning algorithms, that you use an API from Google or Microsoft and get in touch with the flow and how it works .. Once you understand what are the inputs and outputs, you can try to replace the API for you own neural net, try to train it properly and collect results ..
Machine learning is not an easy concept and if you start big, there is a good chance that you'll get discouraged before you finish building it ... The API will provide you with a functional prototype very quickly and thus help you stay motivated to pursue it more ..
But to answer your question more directly, TensorFlow by Google is probably the most sophisticated tool for machine learning in general right now..
There is an excellent course for deep learning with TensorFlow made by Google on Udacity ..
You can follow PyImageSearch. It has lot of stuff related image processiong like face recognition and license Plate Recognition system. It also use neural networks.
Use an image recognition API, like google vision.
It is easy and fast to put in an application, and a lot more effective if you do not have experience and ressources in ML
I have done something similar for our company website. It is based on caffe though.
You can go through the source code here
However, it is a segmentation demo. You need to modify it a little.