The new Luxonis DepthAI SDK Roboflow Integration gives users the option to deploy Roboflow models to OAK devices with more functionality and out-of-the-box options for customization of inferences.
To follow along with this tutorial, you will need a Raspberry Pi 4 or 400. You will need to run the 64-bit Ubuntu operating system.
Roboflow supports deploying custom computer
Team Roboflow decided to end 2022 strong and kick off 2023 with a bang by shipping 12 new features in 12 days. Updates were made to model-assisted labeling, model training, annotation tools, the REST API, Python SDK, and more.
Pre-trained YOLOv8 models are available for testing and deployment on Roboflow Universe. Test and Deploy YOLOv8 Object Detection models in the app through the Models page.
As Ph.D. students in the Active Robotics Sensing Lab (ARoS) at NC State under the supervision of Dr. Edgar Lobaton, we developed in conjunction with The Engineering Place at NC State a set of activities to walk high school students through the entire computer vision pipeline.
Polygons have traditionally been used for training image segmentation models, but they can also improve the training of object detection models. Object detection models are typically much faster and more widely supported, so they're still the best choice for solving many problems.
Constrained bandwith? Left without the option to extend production data beyond your local network or corporate firewall? In need of real-time inference speeds on the edge? The Roboflow Mobile iOS SDK is a great option if you are developing an iOS application.
Regardless of whether your project is a new product line, a new industrial production system, a research project, or a personal one to help you learn what computer vision is all about, you'll want to add "pip install roboflow" to your code - and here's why.
Happy Cinco de Mayo! In honor of the holiday, we created and trained a
multi-label classification
[https://universe.roboflow.com/holidays/cinco-de-mayo-beqrv/1] dataset to detect
some favorite edibles used
Michael Shamash is a Master’s student in the Maurice Lab at Canada's McGill University. Michael used Roboflow to streamline the production of a model and iOS app for use in microbiology.
We've seen tremendous interest in Roboflow from the research community. Faculty and students from institutions ranging from California to Malta to Taiwan have been using Roboflow to accelerate their computer vision work.
