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Release Notes

November 25, 2024 - LEIP Design 1.0

Updates and Enhancements

  • Introducing LEIP Design, a core LEIP Onsite SDK capability that allows you to orchestrate your entire machine learning workflow, from data ingestion and model development to optimization and deployment, all within a unified environment.

  • Flexible Installation: Install LEIP Design via Docker, conda, or pip.

  • Voxel FiftyOne Integration: Directly ingest any dataset supported by the Voxel FiftyOne API into LEIP Design.

  • Advanced Data Augmentations with MixUp: Implement MixUp augmentation for classifiers to enhance model generalization and performance by blending samples during training.

  • Custom Input Channels for Classifiers: You can now train classifiers from scratch with images that have a different number of channels.

  • Support for New Model Architectures: LEIP Design supports YOLOv10 for advanced object detection and RT-DETR for efficient, real-time object detection tasks.

  • Expanded Classification Dataset Support: Access and train on five new classification datasets:

    • CIFAR-100
    • FGVS Aircraft
    • EuroSAT
    • GTSRB (German Traffic Sign Recognition Benchmark)
    • Stanford Cars

  • Enhanced Classification Metrics: Configure and evaluate models using new classification metrics through the Recipe Designer API:

    • AUROC (Area Under the Receiver Operating Characteristic Curve)
    • Precision
    • Recall
    • F1-Score

  • LEIP Stub Code Generator: Simplify deployment to the edge by generating all necessary code during the recipe design process.

  • Golden Recipe Database (GRDB) API Enhancements: The GRDB now suggests optimized, tested, and validated Golden Recipes, with compiler and stub code generation parameters, to further streamline the machine learning workflow through optimization to deployment.