TinyImageNet statistics and PCA

TinyImageNet mean (mean_reshaped.npy) and standard deviation (std_reshaped.npy) calculated on the training set; TinyImageNet's covariance matrix's eigenvalues (eigenvalues.npy), the ratio of total variance explained by each principal component (eigenvalues_ratio.npy) and TinyImageNet's principal components (pc_matrix.npy) computed using the normalized training dataset. These items were used in [1]. The TinyImageNet dataset was presented in [2]. [1] Alice Bizeul, Thomas M. Sutter, Alain Ryser, Julius Von Kügelgen, Bernhard Schölkopf, Julia E. Vogt. Components Beat Patches: Eigenvector Masking for Visual Representation Learning. Oct, 2024. [2] Le, Ya, and Xuan Yang. "Tiny imagenet visual recognition challenge." CS 231N 7.7 (2015): 3.