I can see there are 3 pre-trained models included in the assets folder of the ML-Kit sample app.
Please help me understand
Which of these models correspond to face detection?
What is the data set used to train these models?
Those are custom models provided to highlight the ML Kit functionality that allows you to bring your own TFLite models.
The pre-trained APIs, also known as Base APIs (text recognition, image labeling, etc) get added by including the appropriate libraries in your app's gradle file. No need to include anything in your assets folder. See an example here:
https://firebase.google.com/docs/ml-kit/android/recognize-text
Related
Hi I trained the model through Firebase MLKit. I have selected the option to train him for 13 hours. And despite this, when I get into the details, Firebase only shows me 1 hour during training hours. And after uploading to Android device, CameraX is detecting images incorrectly. The same when I test the model in Firebase also incorrectly detects. I use photos from here:
https://www.kaggle.com/grassknoted/asl-alphabet/data
How can I improve the detection and classification of photos?
I will try to help with my version, what you need to know is that MLkit runs on TensorFlow.
MLKit can use the cloud model or ondevice, when you use your own model you must first upload it to the cloud (if you use MLKit), but if you use TensorFlowLite then you can put the model and label directly on the device.
Errors may occur when you make a model, you can use this tool to make your own model easily https://teachablemachine.withgoogle.com/train .
After that, upload your model on the MLKit Firebase, and apply it to the Android / iOS application using this sample method https://firebase.google.com/docs/ml-kit/use-custom-models
i have sample project using MLKit & its work for more detail check my article here https://medium.com/#rakaadinugroho/face-image-cropper-with-machine-learning-c448f9d19858
There is so few material about Android application examples.
Could someone answer is it possible to use .mlmodel trained with playground in the Android project?
Official sources refers to ML Kit, TensorFlow Lite and AutoML.
Moreover, there is detailed example of use for Android SDK level 16.
But:
(usually ending in .tflite or .lite)
Could you give me any constructive advice or an knowledge I should have to complete the Android project trained with Machine Learning model?
I believe, this information would be useful for every beginner interested in Android development also.
From Can I convert mlmodel files back to tflite? the answer appears to be no.
From what I can tell, the .mlmodel format is a client-end inference model similar to .tflite where .tflite is a minimized format for deployment on device.
I suspect that in the process of conversion from the original full machine learning model, trade-offs are made which may or may not have equivalents between the two formats.
I want to create a simple neural network based on the example https://github.com/googlesamples/android-ndk/tree/master/nn_sample. Is it possible to create this with the help on Tensorflow only with Android tools on Java
Take a look at this folder https://github.com/googlesamples/android-ndk/tree/master/nn_sample/app/src/main/cpp
simple_model.h is the model trained in Tensorflow before creating the Android project. Now the model likes black-box, get input and predict output only, if you want to build your own model, try this tutorial (All steps from training, evaluating, prediction to deploy onto Android):
https://medium.com/#elye.project/applying-tensorflow-in-android-in-4-steps-to-recognize-superhero-f224597eb055
Affirmative. You can use TensorFlow Lite on Android, it's an open source deep learning framework which helps to compress and deploy models to a mobile or embedded application. It basically can take models as input and then deploys and interpret and perform resource-conserving optimizations for mobile applications. The NNAPI of Android NDK can interface with TFLite easily too. This link contains gesture, image, object & speech detection and classification example implementations on Android with Java using TFLite.
I'd like to swap out the multibox_model.pb being used by TensorFlowMultiBoxDetector.java in Google's Tensorflow Detect Sample App with the mobilenet frozen_inference_graph.pb included in the object detection API's model zoo.
I've ran the optimize_for_inference script on it but the tensorflowInferenceInterface can't parse the optimized model. It can, however, parse the original frozen_inference_graph.pb. I still think I need to modify this graph somehow to take in a square-sized input image, such as the 224x224 one that multibox_model.pb does.
I'm one of the developers --- just FYI, we'll be releasing an update to the Android Detection demo in the next few weeks to make it compatible with the Tensorflow Object Detection API, so please stay tuned.
Google has made the TENSORFLOW open source for developers..
Is there any way out for using this on android?
The link is here TensorFlow.
I would love to have some directions to work on this API.
The TensorFlow source repository includes an Android example application, with some documentation.
The Android example includes a pre-trained model for image classification, and uses this to classify images captured by the camera. Typically you would build and train a model using the Python API; generate a serialised version of the model as a GraphDef protocol buffer (and possibly a checkpoint of the model parameters); and then load that and run inference steps using the C++ API.