How can I get help or ask questions?
I’m confused: with alwaysAI, do I code on my PC or Mac, on my edge device, or online?The alwaysAI CLI helps you write your code anywhere, and execute it on your local machine or edge device. Most of our developers write code on their laptops and then execute the applications on edge devices. But you can also write the code on the edge device itself, or run the whole thing on your laptop (if you’re using Linux or Windows — we don’t currently support execution on Mac).
What is an edge device?
When we refer to an edge device, we’re thinking of a single board computer (SBC) such as a Raspberry Pi or Jetson Nano. This type of system is usually deployed for embedded use cases where it runs a specific application in a remote or standalone situation, and is often low power with limited resources compared to a more general purpose computer.
What edge devices can I use?
Your edge device needs to be compatible with Docker, so ARM-32, ARM-64 and x86 devices running Linux (including the Raspberry Pi 3B+, NVIDIA Jetson, ASUS Tinker Board, and systems based on Qualcomm’s Snapdragon processors) can all be used with alwaysAI. If you want to run an application that uses a real-time video feed, your device will also need to be camera-enabled.
Do I need an edge device to use the alwaysAI platform?
Not at all — you can execute your application on your laptop as long as it's running Linux or Windows.
I’m struggling to get my Raspberry Pi configured. Can you help?Yes! Simply download our modified Raspbian Buster OS image for the Raspberry Pi 3B+ (and later), which includes everything you need to start using alwaysAI.
What is a model?
Deep learning models, sometimes called “networks,” are the heart of a modern computer vision system. They take in image/video information, perform an inference and then output results. Each alwaysAI application uses a specific model to return information about the contents of a video source.
How do I change the computer vision models in my application?
Just follow these instructions to change the model(s) used in your application.
How do I use my own custom model?
You can use a custom model by uploading it on alwaysAI, where it will be stored as a private model, meaning that it will only be available to your account. Once a model has been successfully added, it is immediately available for use just like any other model in the alwaysAI catalog.
How do I choose a model for my project?
Every model in the alwaysAI model catalog is categorized according to its purpose: image classification, object detection, pose estimation, etc. Once you’ve chosen a specific category, you can read the list of labels associated with each model to see what a certain model has been trained to recognize: e.g., dogs, potted plants, bikes, etc. These can be general (e.g., "dog") or specific (e.g., "golden retriever") depending on how the model was trained.
Our model catalog provides additional information about each model, including performance and model size. Performance is a relative measure of how quickly a model returns predictions. It is hardware-specific, so use it as a guide rather than an absolute. Model size is the amount of memory in MB that a model requires, which can be useful for edge environments where storage is limited. Changing models in an application is quick and easy, so after narrowing down a few models that look good for your specific application, you can experiment and find the best choice.
I’m not seeing a model that will work for my application. Can you help?
Yes — we are testing new model re-training tools with a select group of users; please let us know if you would like to be considered for this.
What is the difference between image classification and object detection?
Image classification returns the dominant object or objects in an image (it can tell you whether a specific thing is present), whereas object detection identifies things in an image or video stream and also locates them in the frame.
Do you have a facial detection model?
Yes, we do. However, this differs from facial recognition. Facial detection can tell you whether a human face is detected in a video or image, while facial recognition can identify a particular person.