YOLO

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

Computer Vision, Focal Loss, Loss Function, Object Detection, SIoU Loss Functions, YOLO

Mastering All YOLO Models from YOLOv1 to YOLOv12: Papers Explained (2025)

In this blog post we explore all the YOLO object detection model from YOLOv1 to YOLO-NAS.

Computer Vision, Object Detection, YOLO

Introducing YOLO-NAS Pose: A Leap in Pose Estimation Technology

Unveiling a significant breakthrough in computer vision, Deci introduces YOLO-NAS Pose, the latest evolution in Pose Estimation technology. Building on the foundations of the acclaimed YOLO-NAS, this advanced model stands

Pose Estimation, YOLO

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

Keras, Object Detection, Tensorflow, YOLO

Animal Pose Estimation: Fine-tuning YOLOv8 Pose Models

In this article, fine-tune the YOLOv8 Pose model for Animal Pose Estimation.

keypoint detection, Keypoint Estimation, Pose Estimation, YOLO

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

Deep Learning, Object Detection, PyTorch, YOLO