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 ...
In today’s blog post you will learn how to train your own fast style transfer network in PyTorch and deploy the model to get live style transfer effect on a web meeting on Zoom/Skype/ Microsoft Teams ...
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 ...
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 ...
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 ...
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 ...
All views expressed on this site are my own and do not represent the opinions of OpenCV.org or any entity whatsoever with which I have been, am now, or will be affiliated.
In 2007, right after finishing my Ph.D., I co-founded TAAZ Inc. with my advisor Dr. David Kriegman and Kevin Barnes. The scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products.
