Announcing Xtreme1 v0.5.5 with New Ontology Center and 3D Data Visualization Features
Xtreme1 version 0.5.5 is releasing this year before Christmas!
In today’s Data-centric AI world, improving data quality, finding data annotation mistakes, and quickly fixing errors are the most effective ways to improve model performance.
Better data quality is the answer to efficient model performance.
With v0.5.5, image data can be visualized in vectorial distribution, which enables algorithm engineers a clear view of data deficiencies.
Curating camera-LiDAR fusion data has always been challenging for AI researchers and engineers.
To analyze the LiDAR point cloud dataset, the AI team may start by loading LiDAR with various formats (.bin, .pcd, las, npz, etc.), configuring camera-LiDAR fusion data camera parameters, visualizing original data and with annotation results, comparing results of the same dataset among different human annotation teams and model’s results, and finding and fixing annotation mistakes.
Go to the Ontology section, and filter out annotations across datasets by clicking on ‘Scenario Search’.
Here are some of the new features that have been added:
Add Ontology Center
Now users can set up Ontology to add an entity’s class/attribute and manage template libraries. On the dataset’s Ontology page, all class/classification and settings can be synchronized with the global Ontology Center.
The improvements in user interface make the configuration simpler and more convenient than the previous version.
For example, in an autonomous driving dataset case, there are more than 10 classes, but their attributes are the same or similar. You can use this function to do the following:
Create a new class name and add attributes by choosing “Car” and then clicking on ‘Push’. All other classes can get the same attributes.
Add Data Visualization Feature
Scenario Search in Ontology Center
Visually display data and annotation results on the Dataset page
Search by class name to find the scene you are interested in
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