What is Tensorflow Lite?
It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
Tensorflow Lite is a tool in the Machine Learning Tools category of a tech stack.
Who uses Tensorflow Lite?
Companies
6 companies reportedly use Tensorflow Lite in their tech stacks, including NeoQuant, Mobile Enterprise, and AGMO.
Developers
66 developers on StackShare have stated that they use Tensorflow Lite.
Tensorflow Lite Integrations
Pros of Tensorflow Lite
1
Tensorflow Lite's Features
- Lightweight solution for mobile and embedded devices
- Enables low-latency inference of on-device machine learning models with a small binary size
- Fast performance
Tensorflow Lite Alternatives & Comparisons
What are some alternatives to Tensorflow Lite?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
ML Kit
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
Caffe2
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
