Datature Blog

Asset Group Management provides control over dataset usage throughout the whole computer vision pipeline, from asset upload to training dataset versioning.

We are excited to showcase how students from SMU used Datature's Nexus platform to create innovative computer vision projects without coding.

Streamlit empowers users to visualise inference results using custom trained computer vision models while staying within the ease of a Python environment.

We are excited to introduce Datature Edge, a simple way for users to deploy their trained deep learning model on edge devices like the Raspberry Pi.

Our TFLite model export option gives users access to an on-device mobile model that is extensively supported across many devices and platforms.

We continue to strive for democratization of low code implementations for computer vision with Node-RED and TensorFlow.js.

Gain greater insight into your dataset's quality with Aggregation Statistics which will help you improve your dataset for more effective ML model training.

Learn how you can train a YOLOX model to recognize your custom data (without code) for your next computer vision project.

Our Intelligent AI Mask Refinement gives users the option to correct imprecise masks with just a click of the button for new or pre-existing annotations.