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 ...
Mastering All YOLO Models from YOLOv1 to YOLO-NAS: Papers Explained (2024)
What is YOLO? You Only Look Once (YOLO): Unified, Real-Time Object Detection is a single-stage object detection model published at CVPR 2016, by Joseph Redmon, famous for having low latency and high ...
Introducing YOLO-NAS Pose: A Leap in Pose Estimation Technology
YOLO-NAS Pose models is the latest contribution to the field of Pose Estimation. Earlier this year, Deci garnered widespread recognition for its groundbreaking object detection foundation model, ...
Comparing KerasCV YOLOv8 Models on the Global Wheat Data 2020
This article is a continuation of our series of articles on KerasCV. The previous article discussed fine-tuning the popular DeeplabV3+ model for semantic segmentation. In this article, we will shift ...
Animal Pose Estimation: Fine-tuning YOLOv8 Pose Models
Animal pose estimation is an area of research within computer vision, a subfield of artificial intelligence, focused on automatically detecting and analyzing the postures and positions of animals in ...
Weighted Boxes Fusion in Object Detection: A Comparison with Non-Maximum Suppression
Weighted box fusion: The post-processing step is a trivial yet important component in object detection. In this article, we will demonstrate the significance of Weighted Boxes Fusion (WBF) as opposed ...