Introduction to TensorFlow Lite

TensorFlow is a free and open-source machine learning library. TensorFlow was created by Google Brain Team developers and engineers as part of Google’s Artificial Intelligence research organization for the purpose of performing machine learning and deep neural network research, but the technology is general enough to be used in a multitude of other domains!

When one uses TensorFlow to implement and train a machine learning algorithm, one typically ends up with a model file that takes up a lot of storage space and needs a GPU to run inference. On most mobile devices, luxuries such as huge disk space and GPUs are not usable. TensorFlow Lite is a solution for running machine learning models on mobile devices. 

The TensorFlow Lite is a special feature and mainly designed for embedded devices like mobile. This uses a custom memory allocator for execution latency and minimum load. It is also explaining the new file format supported Flat Buffers. TensorFlow Lite takes existing models and converts them into an optimized version within the sort of .tflite file. 

Advantages of TensorFlow Lite:

• Convert TensorFlow models to TensorFlow lite models quickly and easily for mobile-friendly models.
• With simplicity, builds machine learning apps for iOS and Android devices.
• In contrast to server-based architectures, a more effective alternative to mobile model enablement.
• On mobile devices, it allows offline inference.
• Tensorflow Lite allows one to execute machine learning models easily on a smartphone, allowing one to perform traditional machine learning tasks without the need for an external API or server. As a result, the models will operate on devices that are not connected to the internet.

Disadvantages of TensorFlow Lite:

• It does not optimize model size. So mobile devices may require larger storage.
• In the TensorFlow Lite process, the expense of reliability and optimization is a trade-off with the model’s accuracy. As a result, TensorFlow Lite models are less accurate than their full-featured counterparts.

The Architecture of TensorFlow Lite:

TensorFlow Mobile is a successor of TensorFlow Lite, it is employed for mobile platforms like Android and iOS (Operating System). It is used to develop TensorFlow model and integrate that model into a mobile environment.

Use Cases of TensorFlow Mobile 

The three main and important Use case of TensorFLow Mobile are as follows:

• Gesture Recognition in TensorFlow: It’s wont to control applications or perform any special task supported by the hands or other gestures, through analyzing sensor data.
• Image Recognition in TensorFlow: It’s used to detect or get a way of the image captured with a mobile. If the users are taking photos to understand some information that or want to use some effect (filters) thereon then Images Recognition play the measure role to recognize the Photo correctly. Example: Camera, Image Editor, etc.
• Speech Recognition in TensorFlow: Various applications related to speech can build with a speech-driven interface using Tensorflow. To Recognize the voice correctly Speech Recognition is Applies here. There are many popular applications from which some that work on the Speech Recognition System are Google Translate, Google Assistant, etc.

TensorFlow Lite Vs. TensorFlow Mobile 

Hence, TensorFlow Lite outperforms its predecessor TensorFlow Mobile in terms of consistency and binary file size.

Applications of TensorFlow Lite:

• Mobile devices(IOS and Android)
• Internet Of Things(IOT)
• Raspberry Pi