  We introduce the initial release of our software Robustar, which aims to
improve the robustness of vision classification machine learning models through
a data-driven perspective. Building upon the recent understanding that the lack
of machine learning model's robustness is the tendency of the model's learning
of spurious features, we aim to solve this problem from its root at the data
perspective by removing the spurious features from the data before training. In
particular, we introduce a software that helps the users to better prepare the
data for training image classification models by allowing the users to annotate
the spurious features at the pixel level of images. To facilitate this process,
our software also leverages recent advances to help identify potential images
and pixels worthy of attention and to continue the training with newly
annotated data. Our software is hosted at the GitHub Repository
https://github.com/HaohanWang/Robustar.
