5 posts tagged with "edge computing"

High-speed performance is a must for today's computer vision applications, but it comes with many challenges. These include processing a high volume of frames per second (FPS), which requires not only fast algorithms, but also efficient data storage to handle the large quantities of images being processed in real time.

Traditional annotation methods are often time-consuming and labor-intensive for training machine learning models. In other words, they create bottlenecks that slow down projects from getting done.

At the same time, Roboflow was designed to address the challenges associated with annotating data, but manually labeling all images is often tedious and unrealistic. In this case, ReductStore can provide the tools to query, filter, and replicate specific images for further annotation and training.

In this article, we'll explain how Roboflow can help reduce the time and effort required to annotate images, and how ReductStore can be used to store and filter important images.

Deploying a vision model like YOLOv10 at the edge has become a game-changer for real-time object detection. Developed by researchers at Tsinghua University, YOLOv10 introduces architectural innovations that optimizes speed and accuracy, making it ideal for vision tasks that require low inference latency.

This article provides resources for training a YOLOv10 model and managing data storage for real-time performance on edge devices. We will look at a combination of tools, including Roboflow for dataset preparation, Ultralytics for model training, and ReductStore for efficient data storage.

Computer vision is transforming industries by automating decision making based on visual data. From facial recognition to autonomous driving, the need for efficient computer vision solutions is growing rapidly. This article explores how Roboflow combined with ReductStore, a time-series object store optimized for managing continuous data streams, can improve computer vision applications. ReductStore is designed to efficiently handle high-frequency time-series data, such as video streams, making it a perfect fit for storing and retrieving large datasets generated by computer vision tasks.