Follow along as alwaysAI CEO, Marty Beard, describes how Computer Vision is enabling enterprises across all industries to gain real-time customer analytics through deep learning computer vision applications.
We're excited to provide our first update of 2021! First, our mission has not changed: we work every day to make Computer Vision (CV) easy and affordable for all developers. It has been an amazing journey to watch devs use our platform to build and deploy a wide variety of CV apps onto an equally diverse set of IoT devices - new value is created every day on the alwaysAI platform.
Raising money and growing a startup during a global pandemic has certainly been a challenging and novel experience. But making real progress during this unprecedented time is somehow more rewarding – there was no playbook, no ‘best practice’ to follow for 2020.
Hacky Hour TriviaThis special holiday hacky hour is a chance for you to test your knowledge about alwaysAI and computer vision! If you missed it, no worries, you can test your knowledge with the Hacky Hour quiz below. The answers are located at the bottom of the blog. For explanations, watch the Hacky Hour Trivia video below. 1. Where is the origin for the Numpy axis 0,0?A. top left corner B. right bottom cornerC. MiddleD. Bottom left corner2. What tool does alwaysAI’s Model Training Toolkit...
We are proud to introduce an all new desktop version of the alwaysAI Model Training Toolkit. The new robust interface and simple workflow of the desktop app will allow alwaysAI users of all levels to train their own Computer Vision models. The model training desktop application is the first of a suite of applications that will support your Computer Vision model operations, from data-collection and organization to model deployment and maintenance. The model training module is the backbone of...
This blog post is a follow-up to the Hacky Hour presented on 12/3/2020, Build a Virtual Green Screen with Semantic Segmentation.
What is Semantic Segmentation?
More often than not, the most difficult part of a task is simply getting started. alwaysAI’s Model Training tool is now integrated with a Jupyter Notebook interface, which not only makes kicking off your Computer Vision project a breeze, but also keeps things simple from end-to-end. The alwaysAI Model Training toolkit - Jupyter Lab interface allows you to upload your dataset, dial in your training configuration, and start training, all in just a few clicks.
What is Balena?
As always, we are proud of our users and what they are accomplishing by using alwaysAI to integrate Computer Vision into their projects. This week we would like to highlight our user Abhijeet Bhatikar who is using alwaysAI as part of his project for the OpenCV Spatial AI Competition. Read on to find out more about how he is combining alwaysAI, computer vision and depth cameras to help solve logistics...
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.
We recently caught up with one of our talented users, Leonardo Cohen. He is the founder and Managing Partner of Editek. Editek is a consulting company that does IT & Technology projects and Information and Knowledge Management for corporate and sports events. Editek was founded in Portugal in 1993 and moved its operations to the United States in 2003. Editek has an extensive portfolio of projects delivered in more than 10 countries across the creative, corporate, and sports markets.
Recent trends in computation and automation highlight a massive and sustained growth for computer vision based applications on the edge. Market research also points to tremendous interest in these applications amongst entrepreneurs and developers alike. However, developing commercial-grade Computer Vision applications for the edge is hard (link to the new article here). There are two primary reasons for this.
Developing a Computer Vision (CV) application and bringing it to production requires integrating several pieces of hardware and software.
How can we ensure the pieces work seamlessly together? With the right methodologies, we can expedite development and deployment of Computer Vision applications. It is essential to find a platform with the goal of helping developers create computer vision applications from scratch quickly and...
At alwaysAI, we love to engage with our growing developer community and highlight their cool projects. Most recently, two of our users created an alwaysAI Discord bot. This bot, developed by Nathan Wise and Valentine W. allows you to do two main things right from our Discord community:
At alwaysAI we have the singular mission of making the process of building and deploying computer vision apps to edge devices as easy as possible. That includes training your model, building your app, and deploying your app to edge devices such as the Raspberry Pi, Jetson Nano, and many others. alwaysAI apps are built in Python and can run natively on Mac and Windows, and in our containerized edge runtime environment optimized...
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:
We recently connected with an awesome blogger in the computer vision space. We wanted to highlight his blog to our community as he publishes several relevant and interesting articles about Computer Vision and Robotics. It is called the Serious Computer Vision Blog, and is definitely worthwhile checking out.
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.
Many of us spend most of our days hunched over a desk, leaning forward looking at a computer screen, or slumped down in our chair. If you’re like me, you’re only reminded of your bad posture when your neck or shoulders hurt hours later, or you have a splitting migraine. Wouldn’t it be great if someone could remind you to sit up straight? The good news is, you can remind yourself! In this tutorial, we’ll build a posture app using a pose estimation model available from alwaysAI.
Separating certain variables from the main application into a configuration file can improve your development time by reducing the need to recompile apps for minor changes. In this tutorial, we’ll cover how to setup a very basic method for leveraging a JSON file for runtime configuration options.
Many models, including those for pose estimation, may have much better performance when run on a GPU rather than a CPU. In this tutorial, we’ll cover how to run pose estimation on the Jetson Nano B01 and cover some nuances of running starter apps on this edge device.
Building and running your app on alwaysAI can be done a few different ways, depending on the platform you want to develop on and the device you want to deploy on. We’ve concentrated these options in one place for your convenience and we’ll update this document as the platform evolves!
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.
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...
The Jetson TX2 is part of NVIDIA’s line of embedded AI modules enabling super fast computation on the edge. The TX2 is a leg up compared to the Nano and will give you faster inferencing times in your AI applications. In fact, the Jetson TX2 is the fastest, most power-efficient embedded AI computing device. This 7.5 watt supercomputer on a module brings true AI computing at the edge.
Please note: This setup guide can only be followed if you have a Linux computer. VM support is un-verified.
The process of developing computer vision applications has been greatly simplified by alwaysAI, which now includes native support for Mac OSX (Mojave and Catalina), and enables developers to get started prototyping applications right away with very little setup required. By the way, you can also learn how to install alwaysAI on Ubuntu and Windows.
Many people are now working or learning from home, introducing the new issue of enforcing professionalism and academic honesty by remote. Whether you are trying to prevent students from cheating on tests, want to double check that your kids aren’t just playing on their phones, or maybe you want to stop yourself from checking social media while you’re supposed to be working, computer vision can help. In this tutorial, I’ll show how you can create your own contraband object alert system using...
alwaysAI provides a platform to deploy computer-vision applications onto edge devices.Learn More