APK Model: A Guide to Creating and Installing Android Packages
Hi I have trained inceptionV3 classification model with custom dataset and got retrained.pb and retrained_labels.txt. I want to use the model in mobile phone and I converted the model using the following code
You are in need of a fully-featured modeling app on your Android devices, which can easily handle all the common modeling tasks and more? Interested in simple yet powerful tools that can handle your 3D models, objects, arts, and even CGI graphics in mobile environments? Then 3D Modeling App is undoubtedly a great tool for you.
Have no troubles using the convenient mobile app and enjoying its many features, which will allow you to create many 3D objects for different uses. Use the app to work on your 3D graphic designs, 3D builder designs, 3D drawing experiences, and 3D models for many uses. Enjoy the app as game designers and developers, as well as 3D artists to freely work on your many projects.
With 3D Modeling App, Android users will have a convenient and interactive 3D environment for designing, modeling, and drawing on their many 3D objects and models with little trouble. Feel free to use the app to create your cool 3D models on the mobile platform whenever and wherever you want.
Using the convenient touch interactions and gesture controls to freely interact and work on refining your designs. And make uses of the stylus pen to work on detailing and drawing on your 3D objects to have the best arts on your models.
To start with, 3D Modeling App users can enjoy the intuitive app with accessible features to improve their many experiences. Always have access to the intuitive UI with clean layouts and interactive touch controls in 3D Modeling App. Simply navigate through the many features and make uses of your touch gestures to freely interact with the models.
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With the precise edge tools, you can easily draw a cut using your single swipe actions or touch to select between points. Use the app to cut your loops, select loops, or extrude your loops with ease. Feel free to delete your objects, select rings, and create your 3D models using the precise edge tools. All of which will allow you to work on your professional designs.
Like Easy Pose, 3D Modeling App can make use of the face modeling features to work on designing and refining your 3D face models. Here, the app provides users with convenient tools of Extrude, Detach, Clone, Reverse, and Delete, which you can easily use to handle your many designs on the go. Feel free to work with the simple touch to select, double tap, and other useful actions to make the most of the app.
For those of you who are interested, you can now enjoy the convenient object tools in 3D Modeling App to improve your modeling experiences. Have no troubles using the app to combine, clone, mirror, smooth, and enable other interactions with the models.
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And by featuring the useful sculpting tools of move, screen, push, pull, and smooth, 3D Modeling App allows mobile users to freely customize their interactions with the 3d models. And feel free to adjust the brush size and strength to further improve your in-app experiences.
The AnTuTu Benchmark test has a variable duration, which depends a lot on our Android device model and the specific test we want to perform. The performance test, for example, can take up to 5 minutes.
Of these, only two are free and you need to pay if you want to access the rest. Gradually, you can better understand models from simple to complex through face planes. There are many ways to perform the discovery of different models so you can interact with them to the fullest extent.
The 3D models in Head Model Studio are described very realistically with many different shapes. The special thing is that users can easily observe these 3D models from many different angles. Typically you can perform several actions like zoom in, zoom out, tilt, rotate, flip, and more. These actions will help you dig deeper into the content and each aspect of the models on the screen.
Head Model Studio is a design support application, so its features are arranged very logically on the main screen. You will not have too many difficulties in accessing and using this application the first time you use it. Accordingly, all information is displayed visually, giving users a comfortable feeling when accessing. Besides, the dominant black tone also contributes to highlighting 3D models along with useful features on the screen.
Since the creators of Pikashow have spent piles of money buying servers, hosting, and media rights of all kinds of media stuff available inside it, they deserve a payback, which in the business model is known as Subscription Charges.
The game experience is very good, much better than other games of the same model. The graphics and gameplay are also very distinctive. Very good game, can still make friends on it, but the stored valu...
TensorFlow Lite lets you run TensorFlow machine learning (ML) models in yourAndroid apps. The TensorFlow Lite system provides prebuilt and customizableexecution environments for running models on Android quickly and efficiently,including options for hardware acceleration.
TensorFlow Lite uses TensorFlow models that are converted into a smaller,portable, more efficient machine learning model format. You can use pre-builtmodels with TensorFlow Lite on Android, or build your own TensorFlow models andconvert them to TensorFlow Lite format.
This page discusses using already-built machine learning models and does notcover building, training, testing, or converting models. Learn more aboutpicking, modifying, building, and converting machine learning models forTensorFlow Lite in the Models section.
A TensorFlow Lite model running inside an Android app takes in data, processesthe data, and generates a prediction based on the model's logic. A TensorFlowLite model requires a special runtime environment in order to execute, and thedata that is passed into the model must be in a specific data format, called atensor. When a model processes the data, known as runningan inference, it generates prediction results as new tensors, and passes themto the Android app so it can take action, such as showing the result to a useror executing additional business logic.
This section describes the recommended, most common path for implementingTensorFlow Lite in your Android App. You should pay most attention to theruntime environment and developmentlibraries sections. If you have developed a custommodel, make sure to review the Advanced developmentpaths section.
TheInterpreter APIprovides classes and methods for running inferences with existing TensorFlowLite models. The TensorFlow LiteTask APIwraps the Interpreter API and provides a higher-level programming interfacefor performing common machine learning tasks on handling visual, audio, andtext data. You should use the Task API unless you find it does not supportyour specific use case.
Running a model in an Android app requires a TensorFlow Lite-format model. Youcan use prebuilt models or build one with TensorFlow and convert it to the Liteformat. For more information on obtaining models for your Android app, see theTensorFlow Lite Modelssection.
Any data you pass into a ML model must be a tensor with a specific datastructure, often called the shape of the tensor. To process data with a model,your app code must transform data from its native format, such as image, text,or audio data, into a tensor in the required shape for your model.
Processing data through a model to generate a prediction result is known asrunning an inference. Running an inference in an Android app requires aTensorFlow Lite runtime environment, amodel and input data.
The speed at which a model can generate an inference on a particular devicedepends on the size of the data processed, the complexity of the model, and theavailable computing resources such as memory and CPU, or specialized processorscalled accelerators. Machine learning models can run faster on thesespecialized processors such as graphics processing units (GPUs) and tensorprocessing units (TPUs), using TensorFlow Lite hardware drivers calleddelegates. For more information about delegates and hardware acceleration ofmodel processing, see theHardware acceleration overview.
When using more sophisticated and customized TensorFlow Lite models, you mayneed to use more advanced development approaches than what is described above.The following sections describe advanced techniques for executing models anddeveloping them for TensorFlow Lite in Android apps.
In addition to the standard runtime and Google Play services runtimeenvironments for TensorFlow Lite, there are additional runtime environments youcan use with your Android app. The most likely use for these environments is ifyou have a machine learning model that uses ML operations that are not supportedby the standard runtime environment for TensorFlow Lite.
The TensorFlow Lite Flex runtime allows you to includespecific operators required for your model. As an advanced option for runningyour model, you can build TensorFlow Lite for Android to include operators andother functionality required for running your TensorFlow machine learning model.For more information, see Build TensorFlow Lite for Android.
TensorFlow Lite also provides an API for running models using C and C++. If yourapp uses the Android NDK, you shouldconsider using this API. You may also want to consider using this API if youwant to be able to share code between multiple platforms. For more informationabout this development option, see theDevelopment tools page.