Will Kwan spent 50 days to create an AI Startup, out of the project out of Y Combinator Startup School.

You can try it out here: https://omnipost.co.

I’m building a machine learning/SaaS startup. In this video, I share the results my first 50 days of full-time work, explaining my business strategy, showing the core features I designed and programmed, and summarizing what I learned from my users. I also give a overview of all the programming frameworks and API’s I used.

Here’s an interesting idea that uses AI to make recycling easier.

Arjun and Vayun realized that separating waste is sometimes confusing and cumbersome—something that can derail people’s good intentions to recycle. Using TensorFlow, they built a “Smart Bin” that can identify types of trash and sort them automatically. The Smart Bin uses a camera to take a picture of the object inserted in the tray, then analyzes the picture with a Convolutional Neural Network, a type of machine learning algorithm designed to recognize visual objects.

Are you interested in Computer Vision, Deep Learning, and OpenCV, but not sure where to start?

Then this step by step guide is for you.

Follow these steps to get OpenCV configured/installed on your system, learn the fundamentals of Computer Vision, and graduate to more advanced topics, including Deep Learning, Face Recognition, Object Detection, and more!

Siraj Raval has designed an image classifier template for you to use as a learning tool.

This is an example of how machine learning can be used in a software-as-a-service context, hopefully it gives you some ideas on how to do something similar. It’s a combination of a few components including a Python web API, Flutter mobile app, and FastAI model training script.

In this episode, he explains the process of building this template and how all the components fit together.

Ten years ago, researchers thought that getting a computer to tell the difference between a cat and a dog would be almost impossible.

Today, computer vision systems do it with greater than 99 percent accuracy.

How?

Joseph Redmon works on the YOLO (You Only Look Once) system, an open-source method of object detection that can identify objects in images and video — from zebras to stop signs — with lightning-quick speed.

Watch this amazing live demo, where Redmon shows off this important step forward for applications like self-driving cars, robotics and even cancer detection.

Siraj Raval has designed a free curriculum to help anyone learn Computer Vision in the most efficient way possible.

My curriculum starts off with low level vision techniques and progressively increases in difficulty until we get to high level analysis techniques i.e deep learning. Don’t worry if you’ve never coded before, i’ve included links to help you learn Python as well. Now is the time to build computer vision solutions, the world needs these menial tasks automated to help liberate humans from drudgery. The tools needed are python, OpenCV, and Tensorflow, all of which have their place and I’ll explain all the details of how it fits together in this video. Enjoy!

Here’s a great article on R-CNN, object detection, and the ins and outs of computer vision.

After exploring CNN for a while, I decided to try another crucial area in Computer Vision, object detection. There are several methods popular in this area, including Faster R-CNN, RetinaNet, YOLOv3, SSD and etc. I tried Faster R-CNN in this article. Here, I want to summarise what I have learned and maybe give you a little inspiration if you are interested in this topic.

The Raspberry Pi 4 could not have come at a better time and now is the moment for new developers to start experimenting with the technology. This powerful, yet tiny, computer can be used for a variety of functions, but our focus today will be on using the Pi 4 for image processing in a small package and low power setting.

The computing power of the Raspberry Pi 4 is higher compared to previous generations. This means that it can perform inference fairly quickly. It can be used for various types of applications. These include a rock-paper-scissors detection machine, home surveillance through motion detection, object detection for authorized entry (pet vs. animal) or even to give vision to a robot.

Here’s an interesting computer vision / IoT project you can make at home.

The JeVois machine vision sensor can recognize a wide variety of objects and symbols. My own project, Hedley the Robotic Skull , uses one to track me as I walk around in his field of view. The sensor communicates with an Arduino microcontroller, which moves the pan servo to […]