Counting moving objects is one of the most popular use cases in computer vision. It is used, among other things, in traffic analysis and as part of the automation of manufacturing processes. That is why understanding how to do it well is crucial for any CV engineer.
The full code for this article is on GitHub
Today, we will explore the use of PyTorch's distributed collective communication feature. When working with multiple GPUs, it is necessary to
The field of computer vision advances with the newest release of YOLOv8, setting a new state of the art for object detection and instance segmentation.
Roboflow joined the Intel Disruptor Initiative to push the limits of innovation in real-world computer vision applications. Roboflow and Intel are working together to democratize access to computer vision by
When you are training machine learning models, it is essential to pick hardware that optimizes your models performance relative to cost. In training, the name of the game is speed per epoch – how fast can your hardware run the calculations it needs to train your model on your data.
In this guide, learn how to use public models on Roboflow Universe to assist you with labeling and to speed up the process of building an accurate model.
Historically, GPUs have been the go-to for computer vision training, providing
excellent performance for training different model types. But, GPU-optimized
computing is not your only option for running computer vision
Ultralytics, the creators of YOLOv5, and Roboflow now support an integration making it easier to import YOLOv5 models from HUB to Roboflow, export datasets to Ultralytics HUB from Roboflow, and
