With millions of trainable parameters, neural networks have long been considered black boxes. They can produce stunning results, and we often accept the output with very little understanding as to why ...
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 ...
Semantic Segmentation using KerasCV DeepLabv3+
The KerasCV series continues with this second article. Continuing from the previous post, where we discussed Object Detection using KerasCV YOLOv8, this article discusses solving a semantic ...
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 ...
Object Keypoint Similarity in Keypoint Detection
In the constantly evolving field of computer vision, understanding the precise structure and pose of objects is essential. Whether it's detecting a specific object in a cluttered scene or analyzing ...
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 ...