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Orchard ML

Type-safe deep learning framework for reproducible computer vision research.

Orchard ML provides a complete pipeline from data loading to production deployment, with Pydantic v2 validated configuration, Optuna hyperparameter optimization, and ONNX export with quantization.

Key Features

  • Type-safe configuration -- Pydantic v2 frozen models with cross-domain validation
  • 6 built-in architectures -- MiniCNN, ResNet-18, EfficientNet-B0, ConvNeXt-Tiny, ViT-Tiny, plus 1000+ via timm
  • 14 datasets -- MedMNIST, CIFAR-10/100, Galaxy10
  • Optuna integration -- Hyperparameter search with pruning and model search
  • ONNX export -- Production-ready export with INT8/INT4 quantization and benchmarking
  • MLflow tracking -- Local experiment tracking with SQLite backend
  • Full reproducibility -- Deterministic seeding, config snapshots, artifact management

Quick Start

pip install orchard-ml
orchard init
orchard run recipe.yaml

Documentation