Introducing the YOLOv8 Web UI
Introducing the new YOLOv8 Web UI - image labeling, training, and inference in a single GUI.
Introducing the new YOLOv8 Web UI - image labeling, training, and inference in a single GUI.
This article reviews the development of the influential StyleGAN model throughout its development history.
In this tutorial, we understand Data2Vec model from Meta AI and show how to train your own model with a ready-to-use codebase on the Gradient Notebook.
Boosting the performance and generalization of models by ensembling multiple neural network models.
In this article, we explored a broad overview of epistemic uncertainty in deep learning classifiers, and develop intuition about how an ensemble of models can be used to detect its presence for a particular image instance.
In this article, we took a brief look at uncertainties in deep learning. Thereafter, we took a more keen look at aleatoric uncertainty and how convolutional autoencoder can help to screen out-of-sample images for classification tasks.
In this article, we examine typical computer vision analysis techniques in comparison with the modern CLIP (Contrastive Language-Image Pre-Training) model.
In this blog post, we examine what's new in Ultralytics awesome new model, YOLOv8, take a peak under the hood at the changes to the architecture compared to YOLOv5, and then demo the new model's Python API functionality by testing it to detect on our Basketball dataset.
When it comes to image synthesis algorithms, we need a method to quantify the differences between generated images and real images in a way that corresponds with human judgment. In this article, we highlight some of these metrics that are commonly used in the field today.