This article applies to these versions of LandingLens:

            LandingLens            LandingLens on Snowflake
(see exceptions below)

offers these developer tools to accelerate your deployment process and heighten your creativity

The APIs and libraries include practical examples of how to run inference using models you’ve developed in LandingLens. The libraries are available in GitHub.

We plan to add a C# library in the future.

​
REST APIs

Use the REST APIs to perform many tasks, including:

  • Upload images to LandingLens.
  • Create projects.
  • Create classes.
  • Assign split keys (Dev, Train, Test) to images.
  • Train models.
  • Deploy models.
The REST APIs don’t support .

​
Python Library

Use the Python library to:

  • Upload labeled and unlabeled images to LandingLens.
  • Capture images from various sources (video files, webcams, RTSP streams, etc.).
  • Assign metadata values and split keys (Dev, Train, Test) to images.
  • Get prediction results from your deployed model.
  • Post-process your prediction results into other formats.
  • Visualize your prediction results.
  • Chain multiple model inference and post-processing operations together.

To learn more, check out these resources:

​
Using the Python Library with

The Python library offers limited support for . The Python library can be used to run inference and perform image operations like cropping and resizing images. However, it doesn’t support other functions for interacting with data on , like uploading images and assigning splits.

​
JavaScript Library

Use the JavaScript library to:

  • Get prediction results from your deployed model.
  • Visualize your prediction results.
  • Upload unlabeled images from your app.

To learn more, check out the JavaScript repository.

The JavaScript library doesn’t support .