CIFAR10 Dataset
Contents
CIFAR10 Dataset¶
The CIFAR10 dataset is an image recognition dataset of low resolution images with 10 object classes, treated as a tabular dataset. Since each image is of a fixed size (32-by-32 with 3 color channels), we treat each pixel as if it is a tabular attribute. While an image model would likely work best, the idea is that the same tabular architecture is evaluated on a variety of datasets, including this one, and not evaluating image models. Another special feature of this dataset is that we use cleaned test labels from cleanlab. As we do not clean the training labels, this dataset is a noisy training data task, and this also makes our test set results more generalizable.
You can find out more about the original dataset from the original website. You can explore the cleaned labels from the labelerrors.com website (select the CIFAR-10 dataset) and look into confident learning from their paper.
Data Preprocessing¶
We don’t do much preprocessing to the data itself. The main change is the cleaned test labels (see above). We use the same official train-test split.