Semantic Segmentation with DeepLabV3 on Datature
Semantic segmentation assigns a class label to every pixel in an image. Unlike object detection, which draws boxes around objects, segmentation produces a dense mask that outlines exact boundaries. This matters for tasks like autonomous driving (road vs sidewalk vs obstacle), medical imaging (tumor vs healthy tissue), and agricultural monitoring (crop vs weed vs soil).
What This Tutorial Covers
Datature walks through building a semantic segmentation model from scratch on Nexus using DeepLabV3:
- Setting up a project and uploading training images
- Creating polygon annotations for each class in the dataset
- Configuring a training workflow with DeepLabV3 as the backbone
- Launching the training run and reviewing results
The full walkthrough takes about eight minutes and requires no code.
Why DeepLabV3
DeepLabV3 uses atrous (dilated) convolutions to capture multi-scale context without losing resolution. This makes it strong on scenes where objects appear at different sizes in the same frame. On Datature Nexus, DeepLabV3 comes preconfigured with sensible defaults for learning rate, augmentation, and input resolution, so you can start training as soon as your annotations are ready.
For a written guide with architecture details, read A Guide to Using DeepLabV3 for Semantic Segmentation. To understand your training metrics, see How to Interpret Training Graphs.
