Classify on the Clock (CloCk) - An Entry Level Image Data Set for Neural Networks

General information This data set contains synthetic images of an analog clock. Each time point from 00:00:00 - 11:59:59 is included as a separate image. We provide three different versions of each image with an increasing number of additional information: Sparse: The image includes just the hour, minute and second hand Reduced: Additional markings around the clock Full: Additional written numbers The hands differ in size and color: Hour: Black, short, wide Minute: Blue, long, medium Second: Red, long, slim We provide the following files in this data repository: RGB images with a size of 512x512 for all three versions A suggested training, validation and test split The creation script as Python file The script can easily be modified to create images with a different size and color. Clock System The movements of the hands follow a linear relationship. Their angles can be calculated by the forward system: \( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\) One can also introduce rotated versions of the images. The system becomes non-linear in this case: \(\operatorname{mod}\left( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} + \omega, \, 360^\circ \right) = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\) Use Cases This data set was originally designed for basic research on Capsule Networks. Use cases are: Evalutation of classification and regression performance Influence of image transformations, e.g. rotations Detection of the hierarchy & relationship of image parts Concealment of objects in the image Interpretability of the learned model Solving a discrete inverse problem (sparse version) ...