Configure and Launch a Training Workflow on Datature Nexus

From Annotated Data to a Running Model

After labeling your dataset, the next step is training. Datature Nexus uses a visual workflow builder that lets you configure your entire training pipeline without writing code. You pick the model architecture, set hyperparameters, choose augmentation strategies, and launch the run from a single interface.

What This Tutorial Covers

Datature walks through the workflow builder step by step:

• Opening the workflow editor and connecting your annotated dataset
• Selecting a model architecture (YOLOv8, D-FINE, DeepLabV3, and others)
• Setting training parameters: learning rate, batch size, epochs, image resolution
• Adding augmentation blocks to improve generalization
• Launching the training run and monitoring progress

The tutorial covers the full setup in about four minutes.

Getting Better Results

The workflow builder handles common pitfalls for you. It validates that your dataset has enough annotations per class, warns about resolution mismatches, and suggests default hyperparameters tuned for each architecture. For teams new to model training, this removes the guesswork that usually comes with writing training scripts from scratch.

To understand what happens during training, see How to Interpret Training Graphs to Understand and Improve Model Performance.