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 CV 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 CV applications. It is essential to find a platform with the goal of helping developers create computer vision applications from scratch quickly and easily with...
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.
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 corrector app using a pose estimation model available from alwaysAI.
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.
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.
productOps is a boutique software development and consulting firm located in Santa Cruz, California. They serve a broad range of clients across a number of industries, creating custom applications and answering general business challenges.
Imagine how the world would change if we could easily extend the functions of our visual cortex to machines. Computer Vision (CV) has provided us with immense opportunities to build systems and machines that can change the world. We took our CV platform, alwaysAI, to hackathons at top universities in California: University of San Diego, University of Southern California, and Stanford University. With less than 48 hours to complete their projects, students delivered phenomenal projects using...
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.
The developer platform at alwaysAI fast-tracks the creation and deployment of computer vision applications on edge devices, making it easy for you to get started building applications in the field of computer vision.
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...
At the AWS re:Invent conference, we deepened our collaboration with Qualcomm® Technologies by demonstrating real-time object detection and pose estimation on the Qualcomm® Robotics RB3 platform. Based on the Qualcomm® SDA845 system-on-a-chip (SoC), the RB3 platform enables the creation of high-performing computer vision applications on robots and other IoT devices. We built an application on a demo robot that showcased the powerful combination of the Qualcomm® Robotics RB3 platform, our deep...
The AI & Big Data Expo was a great success for alwaysAI. We were a sponsor at the event, which took place at the Santa Clara Convention Center on November 13th & 14th. Our booth was met with an overwhelmingly positive response as we demonstrated how to easily create and deploy computer vision apps on the edge with the alwaysAI platform, and attendees got to witness these applications performing in real time.
CEO Marty Beard gave a compelling talk at the Convergent Technology Stage,...
alwaysAI offers a number of starter apps that make it easy to quickly deploy computer vision (CV) based applications. In this demo, I'm going to show you how to extend one of these starter apps and, hopefully, provide some insight about how you can create your own custom CV apps. The app we're going to end up with is meant to be used on a conference booth, to track the number of attendees who stop by and provide some basic metrics on how much time they spend at the booth.
alwaysAI, a developer platform that fast-tracks the creation and deployment of computer vision apps on edge devices, today announced significant momentum following the close of its Seed funding earlier this year. In addition to adding more than 500 developers to its beta program and expanding its corporate team, alwaysAI also formed two new partnerships for rapid computer vision prototype development and deepened its relationships with NVIDIA and Qualcomm Technologies, Inc.
The process of developing computer vision applications has been greatly simplified by alwaysAI, which includes native support for both Windows and Linux, and enables developers to get started prototyping applications right away with very little setup required.
In this tutorial, we will show you the steps needed to change the computer vision model in the alwaysAI application. Your development computer and edge device (if you're using one) should be set up based on the instructions on our dashboard. You should also have an app running like this object detector starter app. 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.
In this tutorial, we will show you the steps needed to boost the performance of your edge device. You will need a hardware accelerator that is supported by alwaysAI – such as Intel’s Neural Compute Stick 2. We have more information on hardware accelerators in our documentation, which is available now.
Computer vision-based deep learning projects might seem far beyond the kind of what you and your development team have tackled in the past. However, though it is an emerging technology, computer vision application development cycles remain relatively similar to that of many projects you may already be familiar with.
Developers have several libraries available to them to make the process of building and deploying computer vision simpler and more effective. Most of these libraries are written in C/C++, ensuring a fast execution. In most cases, however, there is a Python API that wraps the C++ implementation. This is because Python has become the go-to language for prototyping and developing deep neural networks. An extremely popular and versatile language, Python enables interactive development, and its...
Recent advances in technology have greatly broadened the scope of object detection and related computer vision (CV) services. Hardware with advanced features paired with smarter neural networks has attracted developers and data scientists from numerous industries to start leveraging computer vision to solve complex business challenges. Combined with the rising popularity of embedded devices capturing data on the edge, computer vision on a grand scale has been exploding with seemingly endless...
Although alwaysAI is focused on computer vision on the edge, you can easily install the platform on your local PC and do prototyping before going to the edge. In this article, I will show how to install the alwaysAI platform on your PC using either a virtual machine (macOS, Windows) or native installation (Linux). If you are already running a Linux desktop you can skip down to the Installing alwaysAI Platform section.
Want to speak with us about opportunities for your business to accelerate Deep Learning Computer Vision on the edge? Interested in a demo of the alwaysAI platform? Our team will be at AI events in September, October and November 2019 in California, and we're open for conversations.
Something big is happening. The real world is being radically transformed by tech that has never had as much intelligence and capability as it does today. Sea changes are on the horizon and forward-thinking businesses are shifting their development focus to arguably the most significant change since the invention of the printed circuit board.
Whenever any collection of objects enter your field of vision, your brain instinctively begins the processes of recognition and localization. One of the core challenges of computer vision is to replicate this intelligence in a computer. This is impossible without a proper understanding of human vision and visual perception. The study of biological vision has revealed that the human eyes and brain are connected to an intricate level of functioning. While the eyes receive the visual data, the...
It's time: software and hardware technologies are advancing to a place where you can easily equip low-power, resource-constrained edge devices with AI computer vision capabilities. Here's a brief overview of how CV started, what CV is now, where it might be going, and how you can use it to empower your existing equipment or new project. Ultimately, developers everywhere are now able to easily build and deploy deep learning computer vision applications to make your business more functional,...
alwaysAI provides a platform to deploy computer-vision applications onto edge devices.Learn More