Praveen Pavithran, Author at CloudxLab Blog

REVA University partners with CloudxLab for setting up Center of Excellence in AI and Deep Technologies

REVA University signs an MoU with CloudxLab to set up a Center of Excellence in AI and Deep Technologies: In the picture, Dr. K.M Babu, Vice Chancellor, Dr. Dhanamjaya, Pro-Vice Chancellor, Sandeep Giri, Founder, CloudX Labs.

REVA University’s REVA Academy for Corporate Excellence (RACE) has inked a memorandum of understanding with CloudxLab for setting up a Center of Excellence in AI and Deep Technologies and providing a platform for promoting research and innovation

REVA University and Cloudxlab research collaboration intends to work on technologies involving, deep learning, reinforced learning, curiosity-based machines, distributed computing, and launching specialized courses in these advanced technologies.

This collaboration will be aimed at providing and launching some highly sought-after courses in deep technologies involving experts from Academia as well as the industry. These courses will be delivered in hybrid mode – a combination of physical classroom, online instructor-led, self-paced, and project-based modes.

Dr. P. Shyama Raju, honorable Chancellor, REVA University said “This one-of-a-kind collaboration is aimed at being a launchpad for those who are planning to step into the world of AI, Deep Learning and other advanced technologies. It affirms REVA University’s commitment to make high-end technical education available to everyone in the world.”

“With Artificial Intelligence, machine learning, and other high-end technologies influencing every aspect of our lives, we are optimistic that this collaboration will help professionals in shaping their career”, says Sandeep Giri, CEO, and Founder at CloudxLab.

About REVA University

REVA University is one of the top-ranked private Universities in Bangalore, India, offering a wide range of UG, PG and PhD programs. REVA Academy for Corporate Excellence (RACE) is one of the initiatives of REVA University focused on corporate training to develop visionary enterprise leaders through progressive and integrated learning capabilities. RACE offers best-in-class, specialized, techno-functional, and interdisciplinary programs that are designed to suit the needs of working professionals.

What is GPT3 and will it take over the World

GPT-3 is the largest NLP model till date. It has 175 billion parameters and has been trained with 45TB of data. The applications of this model are immense.

GPT3 is out in private beta and has been buzzing in social media lately. GPT3 has been made by Open AI, which was founded by Elon Musk, Sam Altman and others in 2015. Generative Pre-trained Transformer 3 (GPT3) is a gigantic model with 175 billion parameters. In comparison the previous version GPT2 had 1.5 billion parameters. The larger more complex model enables GPT3 to do things that weren’t previously possible.

Video processing with YOLOv4 and TensorFlow

In this blog we will show how to process video with YOLOv4 and tensorflow. YOLOv4 is out and it’s hot. YOLOv4 is significantly better than YOLOv3 as can be seen in the pic below.

Source

How to label custom images for YOLO – YOLO 3

In this blog we will show how to label custom images for making your own YOLO detector. We have other blogs that cover how to setup Yolo with Darknet, running object detection on images, videos and live CCTV streams. If you want to detect items not covered by the general model, you need custom training.

In our case we will build a truck type detector. There are 4 types of trucks we will try to identify

Object Detection with Yolo Python and OpenCV- Yolo 2

we will see how to setup object detection with Yolo and Python on images and video. We will also use Pydarknet a wrapper for Darknet in this blog. The impact of different configurations GPU on speed and accuracy will also be analysed.

This blog is part of series, where we examine practical applications of Yolo. In this blog, we will see how to setup object detection with Yolo and Python on images and video. We will also use Pydarknet a wrapper for Darknet in this blog. The impact of different configurations GPU on speed and accuracy will also be analysed.

Setup Yolo with Darknet- Yolo 1

We will explore YOLO for image recognition in a series of blogs. This is the first one. In this blog, we will see how to setup YOLO with darknet and run it. We will also demonstrate the various choices you have with YOLO in terms of accuracy, speed and cost, enabling you to make a more informed choice of how you would want to run your models.

Setup Yolo with Darknet

The content in the blog is not unique. However if you are starting with YOLO, this is the first thing you need to do.

How to build a Number Plate Reader – Part 2

In the previous blog of this series, we trained a model to identify a numberplate in a picture. Here we will learn how to use OpenCV and PyTesseract to get the final number from the plate.

We will start from where we ended in the last session. We had trained an ssd_inception model and used it Tensorflow Object Detection API to detect number plates.

How to make a custom number plate reader – Part 1

In this duology of blogs, we will explore how to create a custom number plate reader.

In this duology of blogs, we will explore how to create a custom number plate reader. We will use a few machine learning tools to build the detector. An automatic number plate detector has multiple applications in traffic control, traffic violation detection, parking management etc. We will use the number plate detector as an exercise to try features in OpenCV, tensorflow object detection API, OCR, pytesseract

How to run object detection on CCTV feed

In this blog we explore how to run a very popular computer vision algorithm YOLO on a CCTV live feed.

In this blog we explore how to run a very popular computer vision algorithm YOLO on a CCTV live feed. YOLO (You Only Look Once) is a very popular object detection, remarkably fast and efficient. There is a lot of documentation on running YOLO on video from files, USB or raspberry pi cameras. This series of blogs, describes in details how to setup a generic CCTV camera and run YOLO object detection on the live feed. In case you are interested in finding more about YOLO, I have listed out a few articles for your perusal at the end of this blog.

Setup a CCTV with RTSP

This blog lists out in details methods to setup a generic CCTV camera with a live RTSP feed. Note the RTSP url, as we will need it in the later stages. The RTSP (