github cvat-ai/cvat v2.4.0
v2.4.0: 3D Cuboids interpolation, advanced analytics, and PyTorch dataset adapter in SDK

latest releases: v2.22.0, v2.21.3, v2.21.2...
20 months ago

In this release, we’ve introduced new features based on our vision and suggestions in the CVAT community. Thank you for reaching out and providing us with valuable feedback. Here is the list of features we are shipping in the CVAT v2.4.0

  • Added the grid view and support of the contextual images for 2D and 3D tasks. For more information, see Contextual images.

    3D Cuboids interpolation

  • Added Interpolation for 3D cuboids. Also, annotation can be exported and imported in Datumaro and Supervisely Point Cloud formats.

  • Added Yolo v7 serverless functionally for automated annotation.

  • Collections in server API were redesigned for improved performance.

  • Added simple filters for collection endpoints in API. For example, now you can filter the tasks by username: /tasks?project_id=42&user=myusername

  • Improved analytics and enriched it with the following features: log collection from UI and server, exceptions collection, user activity visualization, aggregation of user and job working time, and log filtering for debugging purposes. Analytics are now based on ClickHouse + Vector + Grafana.

    Advanced analytics

  • Improved the work of the Pytorch adapter also known as the cvat_sdk.pytorch module, which allows you to use a CVAT project or task directly as a PyTorch-compatible dataset, eliminating the need for intermediate representation. For more information, see CVAT SDK Pytorch Adapter.

  • Improved high-level SDK APIs for task data uploading. The default data source is now the local file system. Added an option to wait for data processing to finish after data uploading, the option is turned on by default.

  • Added high-level API for working with organizations. For more information, see High-level API.

New Contributors

Full Changelog: v2.3.0...v2.4.0

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