Datature Blog

Unlock clarity in unstructured datasets using custom attributes with Ontologies, paving the way for standardized and transparent data annotation.

The article explores the concept of quantization in machine learning, detailing how it reduces the bit representation of data in models, thus enhancing computational efficiency and reducing memory footprint. We are announcing our latest patch that supports this in-platform.

Boost the precision of predictions on small objects with Sliding Window Inference

Action recognition is a computer vision task aimed at identifying human actions in visual data, using machine learning techniques to analyze motion and appearance patterns. This field is distinct from traditional classification, focusing on temporal dynamics in videos and has applications in surveillance, healthcare, sports analysis, and more.

Image classification automates the labeling of images based on content using machine learning models, particularly convolutional neural networks, and has applications in various domains like medical diagnosis, e-commerce, and surveillance; YOLOv8 is a cutting-edge method in this field that offers high accuracy and real-time processing, and the Datature Nexus platform simplifies the process of training custom YOLOv8 classification models.

Explore the power of Eigen-CAM in unraveling the inner workings of your YOLOv8 model and gaining insights into its decision-making process.

Unlocking the potential of computer vision for medical imaging! This is a practical guide to building a customized tumor detection system with Datature Nexus.

We are excited to help you learn about the Confusion Matrix and how it can be used to evaluate and determine steps to improve your Computer Vision models.