graph LR
BaseDataset["BaseDataset"]
SpecificDatasetImplementations["SpecificDatasetImplementations"]
BasicTransformations["BasicTransformations"]
AutoAugmentation["AutoAugmentation"]
SpecificDatasetImplementations -- "inherits from" --> BaseDataset
SpecificDatasetImplementations -- "composes and utilizes" --> BasicTransformations
SpecificDatasetImplementations -- "composes and utilizes" --> AutoAugmentation
The Data Management & Preprocessing subsystem is responsible for loading, transforming, and augmenting datasets, preparing them for model input. Its boundaries are primarily defined by the encoding.datasets and encoding.transforms packages.
Serves as the abstract base class for all datasets, providing a standardized interface and common functionalities for data loading and initial transformations. This aligns with the "Data Loaders/Pre-processing" pattern by establishing a clear contract for data handling.
Related Classes/Methods:
Concrete implementations of BaseDataset for various datasets (e.g., ADE20K, Cityscapes, COCO). Each handles dataset-specific file parsing, data loading, and initial preprocessing steps, embodying the "Data Loaders/Pre-processing" pattern.
Related Classes/Methods:
A collection of fundamental image manipulation utilities (e.g., resizing, cropping, normalization) that act as building blocks for data preprocessing pipelines. This component directly supports the "Data Loaders/Pre-processing" pattern by providing essential data transformation capabilities.
Related Classes/Methods:
Implements advanced, automated data augmentation policies (e.g., Cutout, RandAugment) to enhance data variability and improve model robustness during training. This component extends the "Data Loaders/Pre-processing" capabilities with sophisticated augmentation strategies.
Related Classes/Methods: