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 ...
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 ...
Running OpenVINO Models on Intel Integrated GPU
Traditionally, Deep-Learning models are trained on high-end GPUs. But for inference, Intel CPUs and edge devices like NVidia’s Jetson and Intel-Movidius VPUs are preferred. Most of these Intel CPUs ...
Post Training Quantization with OpenVINO Toolkit
Deep Learning models inferencing on video stream inputs in computer vision applications are mostly used for object detection, image segmentation, and image classification. In many cases, we fail to ...
