A detailed breakdown of a new paper from Google Research and Johns Hopkins University — DetectoRS: Detecting Objects using Recursive Feature Pyramids and Switchable Atrous Convolutions.

Photo by Markus Spiske on Unsplash

There has been extensive research going on in finding novel techniques, algorithms, and new end-to-end trainable pipelines for Object Detection and Image Segmentation tasks in the field of Computer Vision.

Year by year, different research institutes/organizations come up with some new ideas to tackle the pertaining problems of these tasks…

An understanding of a new paradigm of depthwise convolution operation developed by Google Research Team

Photo by Devon Janse van Rensburg on Unsplash

Convolutional Neural Networks are complex computational models. Deeper the model, higher will be the complexity. Due to this unfortunate property, it becomes very nontrivial to use these models for real-time purposes.

First released in the paper Xception: Deep Learning with Depthwise Separable Convolutions by Google, introduced the concept of Depthwise…

Understanding the core architecture of RegNet from Facebook AI

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Getting to know about the new 2020 version of ResNet /ResNeXtRegNet from Facebook AI.

This article will mainly focus on the architectural design of RegNet mentioned in paper Designing Network Design Spaces¹.

After finishing this blog, you will get to know the core skeleton of RegNet Architecture and its…

Understanding one of the interesting attention mechanisms in convolutional neural networks.

In this article, we will be going through two articles quickly viz. Bottleneck Attention Modules(BAM)¹ and Convolutional Block Attention Modules(CBAM)².

Recently, many different SOTA networks have leveraged these attention mechanisms that have significantly improved and refined real-time results.

Lightweight network and straightforward implementations have made it easier to incorporate directly…

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Vision Wizard

“Life can only be understood backwards; but it must be lived forwards.”
Søren Kierkegaard

Hello, from VisionWizard. We a team of engineers/researchers working in AI started VisionWizard in one of the worst human adversaries in the history of mankind — Covid19 Pandemic, with a basic need to bridge the…

An Introductory Guide on the Fundamentals and Algorithmic Flows of YOLOv4 Object Detector

Source: Photo by Joanna Kosinska on Unsplash

Welcome to the final part of YOLOv4¹ mini-series.

YOLOv4 — Version 0: Introduction

YOLOv4 — Version 1: Bag of Freebies

YOLOv4 — Version 2: Bag of Specials

YOLOv4 — Version 3: Proposed Workflow

YOLOv4 — Version 4: Final Verdict

I hope we were able to do a thorough walk through…

An Introductory Guide on the Fundamentals and Algorithmic Flows of YOLOv4 Object Detector

Source: Photo by Joanna Kosinska on Unsplash

Welcome to the mini-series on YOLOv4. This article will be addressing all the components authors have presented in the part Bag of Specials. So, breathe in, breathe out, and enjoy learning.

YOLOv4 — Version 0: Introduction

YOLOv4 — Version 1: Bag of Freebies

YOLOv4 — Version 2: Bag of Specials

An Introductory Guide on the Fundamentals and Algorithmic Flows of YOLOv4 Object Detector

Source: Photo by Joanna Kosinska on Unsplash

First introduced in 2015, YOLO quickly rose to fame as one of the fastest dense object detectors with its surprisingly fast inference speed and decent results. Till last year, it has remained the king of one stage object detectors.

This year, it has manifested itself as the boss of One-Shot…

Shreejal Trivedi

Deep Learning || Computer Vision || AI || Editor — VisionWizard

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