June 2025 monthly summary for open-edge-platform/training_extensions: Key feature delivery centered on Anomaly Detection Template Refactor. No major bugs fixed; primary focus on template cleanup and maintainability. Overall impact: streamlined anomaly templates, improved consistency, and readiness for future model integrations.

May 2025 focused on decommissioning the Visual Prompting feature within the Training Extensions project, removing the feature and all related configurations, docs, imports, and templates to eliminate dead code paths and reduce maintenance burden. The cleanup ensured there are no remaining references, minimizing risk of reintroduction and confusion for contributors and downstream users. The work culminated in a focused commit that captures the removal: dd14740cab7d11715aec2a45cc0f1dd8521511e2 with the message “🗑️ Remove traces of visual prompting (#4370)”.

April 2025: Open-edge-platform/training_extensions delivered robustness improvements for multi-label classification, expanded and standardized performance testing coverage for classification and semantic segmentation, and stabilized the benchmarking pipeline. Key outcomes include a crash fix by ensuring labels are LongTensor before one-hot encoding, standardized test configurations and datasets, corrected dataset paths, lint cleanups, and careful management of changes with targeted reverts to preserve stability. These efforts improved data handling reliability, reduced benchmark variability, and accelerated validation for future features and deployments.

November 2024: Stabilized notebook-based deployment and data access flows in open-edge-platform/geti-sdk. Implemented deployment workflow corrections to pass the project object to deploy_project, reverted conflicting changes, and aligned with training/deployment expectations. Updated download flow to pass a project object to download_project_data and clarified docs by renaming parameters from project_name to project. Standardized notebook usage by adopting 'project' in upload_and_predict_image calls, improving consistency across examples. These changes reduce configuration errors, accelerate onboarding for data scientists, and enhance maintainability of notebook-based pipelines.