Sub-dataset selection method

To train this contrast classifier, I have access to a vast dataset sourced from NeuroPoly servers and OpenNeuro. To maximize the utility of this data, I aim to create a balanced and diverse dataset to develop a robust model. I particularly want the model to learn the relationship between image content and contrast, rather than the specific characteristics of my sub-dataset and the contrast (such as resolution, orientation, framing).

- Balance among contrasts will be ensured by assigning weights relative to their representation in the dataset (upsampling).

- Data augmentation will simulate variations in framing, orientation, and resolution through random crops, rotations, and downscalings.

- I will estimate the dataset's bias based on different characteristics by evaluating the performance of basic classifiers trained exclusively with these data. The worse these classifiers, the better the dataset.

No files, tests, or entry points are named. Start by locating the existing contrast-classifier and dataset-selection workflow, then clarify how balancing, augmentation, and bias evaluation should be integrated and what measurable criteria define a successful sub-dataset.

Written by the indexing model from the issue text.