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 ...
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 ...
Introduction to Intel OpenVINO Toolkit
The training of neural-network architectures is what drives most of us who are involved in the field of Deep Learning. We fixate endlessly over the amount of data, its quality, and what neural network ...
Non Maximum Suppression: Theory and Implementation in PyTorch
Non Maximum Suppression (NMS) is a technique used in numerous computer vision tasks. It is a class of algorithms to select one entity (e.g., bounding boxes) out of many overlapping entities. We can ...
