At alwaysAI, we want you to create unique and powerful models that help accomplish your computer vision goals. With the Model Training Toolkit you can create a custom object detection model with little experience and no coding. This video outlines the end-to-end process of doing exactly that - and in a way that is easy to follow. It is meant to be interactive, so you can pause it after each step to take action and then come back. 

Here at alwaysAI, we love to engage with our community of over 10K developers. We have been listening closely to the feedback and are proud to announce the launch of the most requested feature: Model Training.

In this tutorial, we will cover how to create an app that will read license plates using the license plate detection model from alwasyAI and the optical recognition software EasyOCR. 

We're happy to announce the addition of the alwaysAI discord bot developed by Nathan W. and Valentine W. , and implemented by Chris Chu. This bot allows you to do 2 main things right from our Discord community:

In this tutorial, we’ll cover how to create your own license plate tracker using the new license plate detection model, which was created using alwaysAI’s model training tool.

In this tutorial, we’ll walk through the steps and decision points involved in the creation of the ‘alwaysai/vehicle_license_mobilenet_ssd’ model, an object detection model for identifying vehicles and license plates. 

If you haven’t already started thinking about building computer vision into your products, now is the time.

alwaysAI’s Co-Founder & CEO, Marty Beard, recently shared in a quarterly report (click here or see video below) key updates around the company’s product, developer, partner and corporate progress - as well as exciting news for the remaining months of 2020.

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alwaysAI set out to create a platform for developers to easily and affordably build and deploy computer vision (CV) applications on edge devices. Early this year, alwaysAI came out of beta and officially released a...

Training a computer vision model is one component of a complex and iterative undertaking, which can often seem daunting. At alwaysAI we want to make the process simple and approachable. To get you started, we have compiled a general overview of the training process of Deep Neural Networks (DNNs) for use in computer vision applications. We will focus on supervised learning in this overview, which uses labeled training data to teach the model what the desired output is. This article provides...

Computer vision (CV) is a huge part of Industry 4.0 and the changing technological landscape as we know it. Computer vision will allow deeper, more impactful insights into businesses in all sectors. Healthcare providers will be able to more quickly and safely diagnose and treat patients. Manufacturing operations will have enhanced security and productivity. Companies looking for more security while operating virtually, can use computer vision to keep track of their assets, and assure the...

Christian Piper is a 16-year-old high school student from Pennsylvania, who equipped his First Robotics Competition robot with machine learning sight. He did this with the alwaysAI platform.

In this article I will demonstrate how to easily modify existing apps offered with alwaysAI to use two object detection models simultaneously, and to display the output in side-by-side frames.

In this guide, we’ll be focusing on image classification. What is image classification? It is a technique used in computer vision to identify and categorize the main content in a photo or video.

The ability to recognize human activity with computer vision allows us to create applications that can interact with and respond to a user in real time. For instance, we can make an application that gives feedback to a user in the moment so that they can learn how to recreate the perfect golf swing, or that sends an immediate alert for help when someone has fallen, or that generates an immersive augmented reality experience based on the user's position.

At the dawn of a new decade, mobile devices already dominate our personal and professional lives. During the 2020s, computer vision (CV) will come more into focus.

Detecting people can be an important part of applications across many industries. Common use cases include security applications that track who’s coming and going, as well as safety systems designed to keep people out of harm’s way.

With the new alwaysAI model catalog, you can now search for a computer vision model by specific criteria; for instance, you can search by label to ensure that the model includes a label for the type of entity that your application needs to be able to recognize. You can also search for a model that is compatible with the kind of service that you want your application to provide (object detection, image classification, or pose estimation), and then further refine your search with filters for...

In this tutorial, we will show you the steps needed to change the computer vision model in the alwaysAI application. First, set up your development computer and edge device (if you're using one). You should also have an app running like this object detector starter app. You can see more about setting up projects and the alwaysAI workflow here. Finally, you should have a Terminal window open.

If you don't have an alwaysAI account yet, you can sign-up here.

In this tutorial, we will show you the steps needed to get a real-time object detector starter app up and running quickly and easily on an edge device. You should have already set up your development computer and installed the alwaysAI CLI. For more information on system requirements and supported boards, check out our Docs.