AI lab out of the box

Each project includes a continuously trained labeling model, integrated Jupyter server, and scalable annotation database. Manage your datasets and models using the no-code interface or programmatically with a pythonic API.

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integration

Preconfigured for your project with automated templates to accelerate dataset and model development.

Train inference models, measure performance and generate predictions easily using our jupyter templates and one-click GPU assignment.
Filter your data by pixel characteristics, create new datasets, run experiments, and identify the best data for your use case.
Every new annotation is immediately added to the scalable pixel database and made available through Jupyter.

Jupyter Integration - Training, testing, and inference

Jupyter Integration - Iterative data discovery

Jupyter Integration - Work in parallel on the latest data

Deeply customizable when needed

API-driven approach with pyLucy

Our pythonic API gives you the ability to easily manage your models, datasets, and annotation tasks.

Import annotations. Export models and datasets

Using Jupyter you can import your existing annotations and pick up where you left off. Download your datasets and trained models in one click.

Track performance and deliver success

Lodestar provides you multiple templates to understand the performance of your model so you can track relevant progress indicators and know when you attain your objectives.

Manage data programatically

You can manage your annotations by size, pixel consistency, or distance to other objects. Using our interface, annotators can also add custom metadata to any annotation and data scientists can manipulate annotations based on these attributes.