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 Model Optimization. We will explore the different ...
TensorFlow Lite Model Maker: Create Models for On-Device Machine Learning
In this blog post, we will learn 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 and ...
TensorFlow Lite: Model Optimization for
On-Device Machine Learning
The recent trend in the development of larger and larger Deep Learning models for a slight increase in accuracy raises the concern about their computational efficiency and wide scaled usability. We ...
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 ...
