In this guide we'll be pairing Gradient Notebooks with Roboflow datasets to run a training benchmark and compare training costs for YOLO object detection models across multiple GPU types.
In this tutorial, we examine the new YOLOv7 & its new features, learn how to prepare custom datasets for the model, and then build a YOLOv7 demo from scratch using NBA footage to detect and discern the ball handler from players on the court.
In this new tutorial, we will examine YOLOR object detection with PyTorch in detail to see how it combines implicit and explicit information with a unified representation. We then demonstrate how to use YOLOR with Gradient Notebooks.
In this blog, we will show an example of how to train and generalize Scaled-YOLOv4 on your custom dataset to detect custom objects.
YOLO has been a very popular and fast object detection algorithm, but unfortunately not the best-performing. In this article I will highlight simple training heuristics and small architectural changes that can make YOLOv3 perform better than models like Faster R-CNN and Mask R-CNN.
