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In Machine Learning, we always want to get insights into data: like getting familiar with the training samples or better understanding the label distribution. To do that, we visualize the
Imagine you trained a deep learning model on some dataset. A few days later, you want to reproduce the same experiment, but if you were not careful, you may never
In today’s post, we are covering the topic of Gaze Estimation and Tracking. I was invited to give a talk on the subject at a workshop on Eye Tracking for
1. Image Classification vs. Object Detection Image Classification is a problem where we assign a class label to an input image. For example, given an input image of a cat,
The world is evolving and so is the technology serving it. It’s crucial for everyone to keep up with the rapid changes in technology. One of the domains witnessing the
The blog post provides code and shows how to do inference using a Pytorch model with ONNX and Caffe2.
Overview OpenCV.org, in partnership with Big Vision LLC (owner of LearnOpenCV.com) has launched a Kickstarter campaign to create 3 Computer Vision courses. There are many benefits of buying these courses
Face Recognition is a computer vision technique which enables a computer to predict the identity of a person from an image. This is a multi-part series on face recognition. This
In this tutorial, we will discuss an interesting application of Deep Learning applied to faces. We will estimate the age and figure out the gender of the person from a
Last year, Google released a publicly available dataset called Open Images V4 which contains 15.4M annotated bounding boxes for over 600 object categories. It has 1.9M images and is largest
YOLOv3 is one of the most popular real-time object detectors in Computer Vision. In our previous post, we shared how to use YOLOv3 in an OpenCV application. It was very
In this post, we will learn how to squeeze the maximum performance out of OpenCV’s Deep Neural Network (DNN) module using Intel’s OpenVINO toolkitpost, we compared the performance of OpenCV
