As AI engineers, we're always building cool machine learning and deep learning models, right? But then we hit the big question: "Where do we deploy these models so that end-users can actually use ...
YOLOv9: Advancing the YOLO Legacy
Advancing object detection technology, YOLOv9 stands out as a significant development in Object Detection, created by Chien-Yao Wang and his team. This new version introduces innovative methods such ...
YOLO Loss Function Part 2: GFL and VFL Loss
In the preceding article, YOLO Loss Functions Part 1, we focused exclusively on SIoU and Focal Loss as the primary loss functions used in the YOLO series of models. In this article, we will dive ...
YOLO Loss Function Part 1: SIoU and Focal Loss
The YOLO (You Only Look Once) series of models, renowned for its real-time object detection capabilities, owes much of its effectiveness to its specialized loss functions. In this article, we delve ...
Moving Object Detection with OpenCV using Contour Detection and Background Subtraction
Moving object detection is used extensively for applications ranging from security surveillance to traffic monitoring. It is a crucial challenge in the ever-evolving field of computer vision. The ...
Real Time Deep SORT with Torchvision Detectors
Tracking is one of the most important components in object detection when it comes to real-world applications. Applications like real-time surveillance and autonomous driving systems cannot reach ...