TensorFlow TFLite Debugger vs Vision Detector Usage & Stats

The TFLite Debugger app is an essential tool for iOS developers and machine learning enthusiasts who want to streamline their TensorFlow Lite model debugging and testing processes on iOS devices. This powerful and intuitive app empowers you to effortlessly evaluate, validate, and optimize TensorFlow Lite models, ensuring their seamless integration into your iOS applications. Key Features: 1. Model Evaluation: With the TensorFlow Lite Debug and Test App, you can easily load and evaluate your TensorFlow Lite models directly on your iOS device. This allows you to quickly assess the performance of your models in a real-world environment. 2. Model Testing: Debugging TensorFlow Lite models becomes effortless with the app's interactive testing capabilities. Seamlessly import models from local storage or the Files app, simplifying integration and allowing you to identify and resolve issues effectively. 3. Performance Analysis: Assess the performance of your TensorFlow Lite models using detailed metrics provided by the app. Measure parameters like inference time, disk and memory usage to optimize your models and ensure optimal performance on iOS devices. 4. User-Friendly Interface: The app provides an intuitive and user-friendly interface, making it easy for both beginners and experienced developers to navigate and utilize its powerful features. 5. Offline Capability: Enjoy the convenience of using the app even in offline environments. No constant internet connection is required, ensuring uninterrupted development and testing. Whether you're a professional iOS developer working on machine learning projects or a hobbyist exploring the possibilities of TensorFlow Lite, the TFLite Debugger App is an indispensable companion. Streamline your debugging and testing process, enhance model performance, and deliver cutting-edge machine learning experiences on iOS devices. Download the app now and unlock the full potential of TFLite Debugger on iOS!

Apple App Store
Paid
Developer Tools

Store Rank

- -

Vision Detector

Vision Detector performs image processing using a CoreML model on iPhones and iPads. Typically, CoreML models must be previewed in Xcode, or an app must be built with Xcode to run on an iPhone. However, Vision Detector allows you to easily run CoreML models on your iPhone. To use the app, first prepare a machine learning model in CoreML format using CreateML or coreml tools. Then, copy this model into the iPhone/iPad file system, which is accessible through the iPhone's 'Files' app. This includes local storage and various cloud services (iCloud Drive, One Drive, Google Drive, Dropbox, etc.). You can also use AirDrop to store the CoreML model in the 'Files' app. After launching the app, select and load your machine learning model. You can choose the input source image from: - Video captured by the iPhone/iPad's built-in camera - Still images from the built-in camera - The photo library - The file system For video inputs, continuous inference is performed on the camera feed. However, the frame rate and other parameters depend on the device. The supported types of machine learning models include: - Image classification - Object detection - Style transfer Models lacking a non-maximum suppression layer, or those that use MultiArray for input/output data, are not supported. In the local 'Vision Detector' documents folder, you'll find an empty tab-separated values (TSV) file named 'customMessage.tsv'. This file is for defining custom messages to be displayed. The data should be organized into a table with two columns as follows: (Label output by YOLO, etc.) (tab) (Message) (return) (Label output by YOLO, etc.) (tab) (Message) (return) Note: This application does not include a machine learning model. On the iPhone, you can use the LED torch feature. When the screen is in landscape orientation, touching the screen will hide the UI and switch to full-screen mode.