TensorFlow Lite (TFLite) is a suite of tools designed to convert and optimize TensorFlow models for deployment on mobile and edge devices. Initially developed by Google for internal use, TensorFlow was later open-sourced. Today, TFLite has a vast reach, running on over 4 billion devices globally!
As an implementation of Edge AI, TensorFlow Lite plays a crucial role in breaking down barriers to widespread adoption of computer vision with on-device machine learning. This advancement enables the integration of machine learning capabilities across various applications, making real-time deep learning scalable and accessible everywhere.
Deploying high-performance deep learning models on embedded devices to address real-world challenges is no easy feat with current AI technology. Privacy concerns, data limitations, network connectivity issues, and the necessity for optimized, resource-efficient models pose significant challenges for many edge applications.
In this article, we will delve into the following topics:
– TensorFlow vs. TensorFlow Lite
– Selecting the best TF Lite Model
– Pre-trained Models for TensorFlow Lite
– How to use TensorFlow Lite
About us: At viso.ai, we offer the most comprehensive computer vision platform, Viso Suite. Our enterprise solution empowers teams to swiftly build, deploy, and scale custom computer vision systems using a build-once, deploy-anywhere approach. We support TensorFlow, PyTorch, and various other frameworks for computer vision tasks.
### What is TensorFlow Lite?
TensorFlow Lite is an open-source deep learning framework tailored for on-device inference, also known as Edge Computing. This framework equips developers with tools to run their trained models on mobile, embedded, IoT devices, and computers. It supports platforms like embedded Linux, Android, iOS, and MCU.
Designed specifically for on-device machine learning (Edge ML), TensorFlow Lite is optimized for deployment on resource-constrained edge devices. Edge intelligence, the ability to shift deep learning tasks from the cloud to the data source, is essential for scaling computer vision in practical scenarios.
### What is TensorFlow?
TensorFlow is an open-source software library for AI and machine learning featuring deep neural networks. Initially developed by Google Brain for internal use, TensorFlow was open-sourced in 2015. Today, it is extensively utilized for research and production purposes at Google.
### What is Edge Machine Learning?
Edge Machine Learning (Edge ML), or on-device machine learning, is essential for overcoming the limitations of purely cloud-based solutions. The key advantages of Edge AI include real-time latency, privacy, robustness, connectivity, smaller model size, and efficiency in computation and energy consumption.
To explore how Edge AI merges Cloud with Edge Computing for local machine learning, we recommend reading our article on Edge AI – Driving Next-Gen AI Applications.
### Computer Vision on Edge Devices
Object detection, among other tasks, holds significant importance in numerous computer vision applications. Traditional object detection implementations struggle to run on resource-constrained edge devices. To address this challenge, Edge ML-optimized models and lightweight variants have been developed to achieve accurate real-time object detection on edge devices.
### Difference between TensorFlow Lite and TensorFlow
TensorFlow Lite is a lightweight version of the original TensorFlow framework. TF Lite is specifically tailored for mobile computing platforms, embedded devices, edge computers, video game consoles, and digital cameras. While TensorFlow aids in building and training ML models, TensorFlow Lite is more suited for inference tasks and edge devices. TensorFlow Lite optimizes trained models using quantization techniques, reducing memory usage and computational costs while maintaining model efficiency.
### TensorFlow Lite Advantages
– Model Conversion: Efficiently convert TensorFlow models into TensorFlow Lite models for mobile-friendly deployment, optimizing existing models to be less memory and cost-intensive.
– Minimal Latency: Decreases inference time, making it ideal for real-time performance-dependent tasks.
– User-friendly: Offers a simple approach for mobile developers to build applications on iOS and Android devices using TensorFlow machine learning models.
– Offline inference: Edge inference operates independently of an internet connection, enabling deployment in remote or internet-scarce locations. This capability is crucial for mission-critical computer vision applications that must function even with intermittent internet connectivity.
### Selecting the best TensorFlow Lite Model
To choose suitable models for TensorFlow Lite deployment, consider factors like model size, data input requirements, inference speed, and accuracy. Prioritize your primary constraint, whether it’s model size, data size, inference speed, or accuracy. Opt for the smallest model for wider device compatibility and faster inferences, unless accuracy is a critical factor.
### Pre-trained Models for TensorFlow Lite
Leverage pre-trained, open-source TensorFlow Lite models to seamlessly integrate machine learning capabilities into real-time mobile and edge device applications. A variety of TF Lite example apps with pre-trained models are available for tasks like image classification, object detection, pose estimation, speech recognition, gesture recognition, and more.
### How to Use TensorFlow Lite
Utilize TensorFlow Lite by generating the TensorFlow Lite model and running inference. The official development workflow documentation provides detailed guidance on using TensorFlow Lite. Data curation is essential for generating a TensorFlow Lite model, represented by the .tflite file extension. FlatBuffers serialization library ensures efficient cross-platform serialization and access to serialized data without parsing.
### Ways to Generate TensorFlow Lite Model
– Use an Existing TensorFlow Lite Model: Deploy pre-built models for specific tasks from the TensorFlow Lite example apps website with minimal modifications.
– Create a TensorFlow Lite Model: Utilize the TensorFlow Lite Model Maker for image classification, object detection, text classification, and more, simplifying the training process with transfer learning.
– Convert a TensorFlow Model into a TensorFlow Lite Model: Employ the TensorFlow Lite Converter to convert TensorFlow models into optimized TensorFlow Lite models, reducing model size and latency.
### The Fastest Way to Use TensorFlow Lite
For a streamlined approach to Edge ML model deployment, consider utilizing a computer vision platform like Viso Suite. This end-to-end solution leverages TensorFlow Lite to build, deploy, and scale real-world applications swiftly and seamlessly.
### What’s Next With TensorFlow Lite?
Lightweight AI model versions like TensorFlow Lite are poised to revolutionize the implementation of scalable computer vision solutions by enabling image recognition capabilities on edge devices. TensorFlow Lite’s streamlined deployment capabilities empower developers to categorize and deploy models across a wide range of devices and platforms, ensuring optimal performance and user experience.
Looking ahead, the lightweight Edge ML model variant of TensorFlow Lite will continue to be a popular choice for on-device inference. Stay updated on the latest releases, news, and articles about TensorFlow Lite by following the TensorFlow blog.
[Original Article Source](https://viso.ai/wp-content/uploads/2021/08/computer-vision-in-retail-applications.jpg)
