Datature Blog

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

Dataset exploration is the practice of continuously inspecting, filtering, and understanding your training data throughout the MLOps loop - because in real projects the dataset keeps changing as you collect new samples, annotate, retrain, and redeploy. This article breaks down why classic “tabular” analysis doesn’t map cleanly to images and video, and why modern tools rely on two complementary search approaches: metadata query and image similarity search.

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.

Leverage Segment Anything with IntelliBrush and our Label Everything tool to efficiently label your data on Nexus' Annotator.

We are excited to explore the latest developments in generative AI and how it can drive ML applications through image augmentation and dataset population.

Our Active Learning tool informs users to annotate images that best help to train the model based on metrics calculated from model predictions on images.

We are excited to introduce one of our Nexus models: DeepLabV3, a state-of-the-art multi-scale semantic segmentation model that can support any use case.

Our Performance dashboard offers insights into useful metrics to monitor your team's labelling performance for better quality control and task management.