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.
Video processing with YOLOv4 and TensorFlow
Tag: Computer Vision
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
Continue reading “How to label custom images for YOLO – YOLO 3”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.
Continue reading “Object Detection with Yolo Python and OpenCV- Yolo 2”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.
Continue reading “Setup Yolo with Darknet- Yolo 1”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.
Continue reading “How to build a Number Plate Reader – Part 2”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
Continue reading “How to make a custom number plate reader – Part 1”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 (
Understanding Computer Vision with Deep Learning – Free Webinar
CloudxLab conducted a successful webinar on “Introduction to Machine Learning” on the 15th of October, 2019. It was a 2-hour session in which the instructor explained the concepts based on Understanding Computer Vision with Deep Learning.
More than 250 learners around the globe attended the webinar. The participants were from countries namely; United States, Canada, Australia, Indonesia, India, Thailand, Philippines, Malaysia, Macao, Japan, Hong Kong, Singapore, United Kingdom, Saudi Arabia, Nepal, & New Zealand.
Continue reading “Understanding Computer Vision with Deep Learning – Free Webinar”Conference on Computer Vision at Google Asia, Singapore
The deep learning algorithms and frameworks have changed the approach to computer vision entirely. With the recent development in computer vision with Convolutional Neural Networks such as Yolo, a new era has begun. It would open doors to new industries as well as personal applications.
After the successful bootcamps held at IIT Bombay, NUS Singapore, RV College of Engineering, etc, CloudxLab in collaboration with IoTSG and Google Asia conducted a successful conference on Understanding Computer Vision with AI using Tensorflow on May 11, 2019, at Google Asia, Singapore office.
Continue reading “Conference on Computer Vision at Google Asia, Singapore”