This allows you to implement any specialized behavior required for your data, such as handling multi-modal inputs, complex labeling schemes, or handling imbalanced datasets. Customizability: By subclassing the Dataset class, you can create your own custom dataset with specific functionality tailored to your task.The DataLoader takes a Dataset instance as input and provides options for batch size, shuffling, and parallelism. Integration with Data Loaders: The Dataset class is designed to work easily with PyTorch’s DataLoader class, which handles efficient and parallel data loading.It provides a unified interface to retrieve samples, which is useful during training or when evaluating the model’s performance. Data Access and Indexing: The Dataset class enables you to access individual data samples by their index.Again, because it’s abstracted away in a class, this process can be hidden away from end-users. This includes tasks such as data augmentation, normalization, scaling, or any other transformations required to prepare your data for training or inference. Data Preprocessing: With the Dataset class, you can define custom preprocessing operations on your data.Furthermore, it allows you to use encapsulation to load your data in a single class, making it easier to manage and reuse.
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