In Machine Learning, we always want to get insights into data: like get familiar with the training samples or better understand the label distribution. To do that, we visualize the data in many ...
CNN Receptive Field Computation Using Backprop
In the previous post, we learned how to classify arbitrarily sized images and visualized the response map of the network. In Figure 1, notice that the head of the camel is almost not ...
Augmented Reality using ArUco Markers in OpenCV (C++ / Python)
In this post, we will explain what ArUco markers are and how to use them for simple augmented reality tasks using OpenCV. ArUco markers have been used for a while in augmented reality, camera pose ...
Fully Convolutional Image Classification on Arbitrary Sized Image
In this post, we will learn how to perform image classification on arbitrary sized images without using the computationally expensive sliding window approach. This post is written for people who are ...
Ensuring Training Reproducibility in PyTorch
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 be able to reproduce the same ...
Applications of Foreground-Background separation with Semantic Segmentation
In our previous post, we learned what is semantic segmentation and how to use DeepLab v3 in PyTorch to get an RGB mask of the detected labels within an image. Recall that semantic segmentation is ...
