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Welcome Flutter geeks! With the rapidly evolving field of machine learning, the capacity of mobile and desktop platforms to process complex models continues to amplify. A revolutionary step in this regard is the launch of "Flutter TensorFlow lite", a phenomenal tool that broadens the scope of efficiently deploying machine learning models within your Flutter applications.
TensorFlow Lite is a framework provided by Google for the purpose of running machine learning models on resource-constrained devices. It is a compact yet powerful tool tailored for mobile and IoT devices, enabling developers to turn insights gained from machine learning models into a tangible user experience.
Imagine being able to access TensorFlow Lite interpreter directly from your Flutter app. This is made possible with the TensorFlow Lite Flutter plugin. With this tool, you can bring the absolute best of TensorFlow Lite into your Flutter applications, using machine learning to offer detailed image and text classification and object detection capabilities. In other words, this plugin brings the power of advanced machine-learning algorithms right into the palm of users' hands.
Paired together, TensorFlow with Flutter offers acceleration support by binding directly to TensorFlow Lite C Api making it wildly efficient. This approach guarantees low latency by eliminating the need for a language bridge (like JNI in Android). So, the Flutter TensorFlow Lite works almost as swiftly as the TensorFlow Lite Java API would in native Android Apps.
The operation of TensorFlow Lite in Flutter is seamless and lets you create an interpreter from a simple TensorFlow Lite model file. Here's a lowdown of the exciting features offered by the TensorFlow Lite Flutter plugin:
Being Flutter-based, TensorFlow Lite inherently offers cross-platform support. Therefore, your TensorFlow Lite-powered Flutter app can run on both Android and iOS, which is a significant advantage when trying to reach a wider audience with your machine learning-powered application.
The plugin supports all standard TensorFlow Lite models. Whether you own an image classification model you've worked on or an already trained model available on the TensorFlow website, you can plug it in and start performing inference tasks right away.
With support for multithreading, the TensorFlow Lite plugin for Flutter ensures that your app makes the best use of system resources. This feature offloads computing responsibilities from the main thread preventing jank in the UI thread, leading to smoother animations and interactions within your app.
This is a boon to developers who have previously worked with the TensorFlow Lite Java API for Android. The Flutter plugin adopts a similar structure, making it easier to grasp and work with.
Owing to its direct connection to the TensorFlow Lite C API, the TensorFlow Lite Flutter plugin offers fast inference speeds close to those of native Android apps built using the Java API.
Before we can start running a TensorFlow Lite model in our Flutter project, some initial setup is required. For TensorFlow to function, we need to add dynamic libraries to your app. I will show you how to add these dynamic libraries and the package to your project in the next continuation of the blog.
And remember, always feel free to reach out to TensorFlow with queries about the Flutter TensorFlow Lite in the platform's issue discussion area.
The TensorFlow Lite plugin requires some dynamic libraries to be added to your Flutter application. This allows the plugin to directly interact with the C library of TensorFlow Lite, which enables high-performance and low-latency operations.
Here's a code snippet showing how you can add these libraries to your Flutter project:
1 // Add this configuration to your pubspec.yaml file 2 3 flutter: 4 assets: 5 - assets/ 6 fonts: 7 - family: 8 fonts: 9 - asset: 10 style: 11
In the case of Android and iOS, the dynamic libraries can be automatically downloaded and added to your project with just a few commands.
1 // Example: running your project with dynamic libraries on Android 2 3 flutter build android & flutter install android 4
However, in the case of iOS, there's an important note to remember; TensorFlow Lite may not work with the iOS simulator. It is recommended to test with a physical device, primarily because the underlying TensorFlow Lite code is written to interface with specific hardware features of iOS devices, which are not available in the simulator.
With the TensorFlow Lite plugin installed and your environment properly configured, you're cleared to incorporate machine learning models into your Flutter application.
At the outset of using TensorFlow Lite in Flutter, you'll need to create an Interpreter. The Interpreter forms a vital core of the process as it ingests your machine-learning model and helps execute it on your device.
You may have your TensorFlow Lite model as a .tflite file lodged within your project's assets directory. Refer to the following snippet to create an Interpreter from such a model file:
1 // Importing the tflite_flutter plugin 2 import 'package:tflite_flutter/tflite_flutter.dart'; 3 4 // Creating the interpreter using a helper function 5 final interpreter = await tfl.Interpreter.fromAsset('assets/your_model.tflite'); 6
In this snippet, 'assets/your_model.tflite' represents the pathway to your model's .tflite file within your 'assets' folder.
The advent of TensorFlow Lite provides an efficient canvas on which to perform inference, which is essentially the operation of running your TensorFlow Lite model with selective input data and interpreting the output data provided by the model. Here's how you can conduct inference for a simple, single input and observe the output it produces.
If, for instance, your input tensor shape is [1, 5] and the type is float32, you can define it as follows:
1 // For ex: if input tensor shape [1,5] and type is float32 2 var input = [[1.23, 6.54, 7.81, 3.21, 2.22]]; 3
If the output tensor also has a shape of [1, 2] and is also of the type float32, you can define it as follows:
1 // If the output tensor shape is [1,2] and type is float32 2 var output = List.filled(1*2, 0).reshape([1,2]); 3
With the input and output tensors defined, you can now run the inference:
1 // Perform inference 2 interpreter.run(input, output); 3 4 // Print the outputs 5 print(output); 6
Once the inference is performed, your model provides its interpretation of the input data in the output variable, which you can simply print out to examine.
This was an example of performing inference with a single input and output. However, TensorFlow Lite can handle a more complex scenario where you have multiple inputs and outputs.
You might be dealing with more complex models that need multiple inputs and outputs. Here's how you can perform inferences in such scenarios:
1 // Let's say we have two inputs and two outputs 2 3 var input0 = [1.23]; 4 var input1 = [2.43]; 5 6 // Three sets of inputs: List<Object> 7 var inputs = [input0, input1, input0, input1]; 8 9 var output0 = List<double>.filled(1, 0); 10 var output1 = List<double>.filled(1, 0); 11 12 // Outputs: Map<int, Object> 13 var outputs = {0: output0, 1: output1}; 14 15 // Perform inference 16 interpreter.runForMultipleInputs(inputs, outputs); 17 18 // Print outputs 19 print(outputs) 20
Finally, once you're done with performing inferences, it's good practice to close the interpreter. This allows to free up the resources occupied by the interpreter.
1 // Closing the interpreter 2 3 interpreter.close(); 4
With that, you've successfully run your TensorFlow Lite model in your Flutter application.
Before the TensorFlow Lite interpreter can be initialized and run in Flutter, we need to add the TensorFlow Lite Flutter Plugin to our project.
1 // To include tflite_flutter in your project, you need to add it to your pubspec.yaml 2 3 dependencies: 4 tflite_flutter: ^latest_version // replace latest_version with the current version 5 6 // Then, you can install it using the Flutter command 7 flutter pub get 8
With a sharp rise in mobile app development, the need for intelligent apps has grown multifold. Integrating Machine Learning capabilities in apps has now become simpler, thanks to platforms like TensorFlow Lite, and specifically, the TensorFlow Lite Flutter Plugin. This not only makes your apps smarter but also opens up an immense scope for user engagement.
Remember, your TensorFlow Lite model has the power to transform the user experience of your app. So, dive right in, and infuse your applications with the power of machine learning using TensorFlow Lite with Flutter. Happy Coding!
For further information and updates, you can check out the official plugin on pub.dev here and the TensorFlow official announcement blog link here .
Through this guide, we have provided you with the fundamentals to incorporate TensorFlow Lite into your Flutter applications, making it an excellent resource for both beginners in the field of Machine Learning and experienced developers looking to explore the intersection of Machine Learning and Application Development.
By integrating Flutter, TensorFlow Lite, and the power of machine learning, you can effectively create advanced, user-friendly applications that provide impressive features and functionality.
Remember, in the realm of machine learning, the sky's the limit. So, keep exploring, keep learning, and keep pushing your limits. We look forward to seeing the marvelous applications you create using TensorFlow Lite with Flutter.
As we journey into this exciting era of embedding machine learning in mobile applications, remember that each of your experiences matters. Share your experiences in the community. Every insight, every challenge, and every victory you share helps us all grow together in our understanding. After all, in the world of coding, every experience counts in our shared quest for knowledge!
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