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 ...
GradCAM – Enhancing Neural Network Interpretability in the Realm of Explainable AI
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 ...
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, ...
Deploying a Deep Learning Model using Hugging Face Spaces and Gradio
In deep learning, training a model is not the final step. Be it image classification or object detection, a deep learning project becomes worthwhile only when it reaches the masses. That's where ...
Train YOLOv8 on Custom Dataset – A Complete Tutorial
Ultralytics recently released the YOLOv8 family of object detection models. These models outperform the previous versions of YOLO models in both speed and accuracy on the COCO dataset. But what about ...