DeepLearning Dataclass Guide
Okay, let’s craft a general and uniform guide for building a dataset class for image processing large models, focusing on PyTorch and PyTorch Lightning. This structure is highly adaptable. Core Principles for Your Dataset Class: Uniformity: The interface (__init__, __len__, __getitem__) should be consistent. Flexibility: Easily accommodate different data sources, label types, and transformations. Efficiency: Load data on-the-fly, leverage multi-processing in DataLoader, and handle large datasets without excessive memory usage. Clarity: Code should be well-commented and easy to understand. Reproducibility: Ensure that given the same settings, the dataset behaves identically (especially important for train/val/test splits). We’ll structure this around: ...