Highlights: Object detection is one of the most important tasks in Computer Vision. In this post, we will give an overview of one of the most influential families of object detection algorithms: R-CNN, Fast R-CNN, and Faster R-CNN. We will highlight the main novelties and improvements for each of them.  Finally, we will focus on the Faster R-CNN and explore the code and how it can be used in PyTorch.  Tutorial Overview: 1. Introduction to… Read more The post #027 R-CNN, Fast R-CNN, and Faster R-CNN explained with a demonstration in PyTorch appeared first on Master Data Science ..read more

Highlights. Hello and welcome. In this post, we are going to talk about one of the most popular algorithms for object detection called the Yolo object detection algorithm.  We will cover the basic theory behind YOLO object detection [1], its benefits, and how this algorithm has evolved over the last couple of years. Finally, we will examine some real-life applications of YOLO object detection, more specifically, we will explain how to use YOLO for detecting… Read more The post #004 Advanced Computer Vision – YOLO Object Detection appeared first on Master Data Science ..read more

Highlights: In this post, we are going to learn several hacks that we can use to train stable GAN models. First, we are going to provide a quick recap of the GANs theory, and then, we are going to talk about challenges when training GANs. After that, we will provide solutions for these challenges in Python. So, let’s begin with our post. Tutorial Overview: Challenges when training GANs Heuristics for Training Stable GANs Architecture for guidelines… Read more The post #007 How to implement GAN Hacks to Train Stable Models? appeared first on Master Data Science ..read more

Highlight: In this post, we will briefly review the theory behind Generative Adversarial Networks and then we will learn to implement that knowledge in PyTorch. We will actually build our first GAN from scratch so that all the details are demystified. Initially, we will start with generator modeling or faking a simple 1D function (sine wave). In the later posts, we will build on the fundamental GAN architecture presented in this post.  Tutorial Overview: GAN… Read more The post #006 GANs – How to Develop a 1D GAN from Scratch appeared first on Master Data Science ..read more

Highlights: Semantic segmentation is an important subject in Computer Vision that enables a model to label specific regions of an image according to what’s being shown. DeepLab is a state-of-the-art model by Google with many versions making a family of algorithms used for semantic segmentation. In this tutorial post, we will introduce the DeepLab algorithm and specifically talk about the DeepLab v2 that introduced three famous advancements in the field of semantic segmentation. Towards the… Read more The post #022 PyTorch – DeepLab v2 Semantic Segmentation in PyTorch appeared first on Master D ..read more

Highlights: In this post, we will talk about the importance of visualization and understanding of what our Convolutional Network sees and understands. In the end, we will write code for visualizing different layers and what are the key points or places that the Neural Network uses for prediction. Tutorial Overview: History Introduction Visualization with a Deconvnet Saliency and Occlusion  Visualization and understanding CNNs in PyTorch 1. History A bit of history about CNN’s, back in… Read more The post #028 PyTorch – Visualization of Convolutional Neural Networks in PyTorch appeared fir ..read more

Highlights: Hello and welcome to our new post. Today, we’ll discuss another popular method used to improve the performance of your deep neural network called batch normalization. It is a technique for training deep neural networks that standardizes the inputs to a layer for each mini-batch. After finishing the theoretical part, we will explain how to implement batch normalization in Python using PyTorch. So, let’s begin with our lecture. Tutorial Overview: Data Normalization and Standardization Batch Normalization… Read more The post #017 PyTorch – How to apply Batch Normalization in ..read more

Highlights: Hello and welcome back. In this post, we will see how we can use Neural Networks for the segmentation task. To be more precise, it will be about Semantic Segmentation. The goal of Semantic Segmentation is to label each pixel of an image with a corresponding class. We will cover some of the most popular Deep Learning models for segmentation: Fully Convolutional Neural Network SegNet U-Net Tutorial Overview: What is Semantic Segementation? Types of segmentations Fully Convolutional Neural… Read more The post # 020 Overview of Semantic Segmentation methods appeared ..read more

Highlights: Hi and welcome to our new post. In this post, we are going to talk about very popular deep learning techniques that we can apply to speed up training and improve the performance of our deep learning model. You will learn how you can use transfer learning and some other popular methods like data augmentation and scheduling the learning rate. So, let’s begin. Tutorial Overview: What is Transfer learning? Transfer learning in PyTorch Scheduling… Read more The post #016 PyTorch – Three hacks for improving the performance of Deep Neural Networks: Transfer Learning, Data Augmentation, an ..read more

Highlights: Is your goal to do face recognition in photographs or in videos? This distinguished paper, 2015, Deep Face Recognition proposed a novel solution to this. Although the period was very fruitful with contributions in the Face Recognition area, VGGFace presented novelties that enabled a large number of citations and worldwide recognition. Here, we will present a paper overview and provide a code in PyTorch to implement it.  Overview: Theoretical background VGGFace network architecture VGGFace… Read more The post #026 VGGFace: Deep Face Recognition in PyTorch by Oxford VGG appeared ..read more