In the previous posts of the TFLite series, we introduced TFLite and the process of creating a model. In this post, we will take a deeper dive into the TensorFlow Model Optimization. We will explore ...
TensorFlow Lite Model Maker: Create Models for On-Device Machine Learning
In this article, we will learn how to create a TensorFlow Lite model using the TF Lite Model Maker Library. We will fine-tune a pre-trained image classification model on the custom dataset, further ...
TensorFlow Lite: TFLite Model Optimization for On-Device Machine Learning
The recent trend in developing larger and larger Deep Learning models for a slight increase in accuracy raises concerns about their computational efficiency and wide scaled usability. We can not use ...
Building Industrial embedded deep learning inference pipelines with TensorRT
You can scarcely find a good article on deploying computer vision systems in industrial scenarios. So, we decided to write a blog post series on the topic. The topics we will cover in this ...
Model Selection and Benchmarking with Modelplace.AI
In this post, we will learn how to select the right model using Modelplace.AI. Selecting the right model will make your application faster, help you scale it to millions of requests, and save a ton ...
Introduction to OpenVINO Deep Learning Workbench
The Intel-OpenVINO Toolkit provides many great functionalities for Deep-Learning model optimization, inference and deployment. Perhaps the most interesting and practical tool among them is the ...
