  In the realm of fashion object detection and segmentation for online shopping
images, existing state-of-the-art fashion parsing models encounter limitations,
particularly when exposed to non-model-worn apparel and close-up shots. To
address these failures, we introduce FashionFail; a new fashion dataset with
e-commerce images for object detection and segmentation. The dataset is
efficiently curated using our novel annotation tool that leverages recent
foundation models. The primary objective of FashionFail is to serve as a test
bed for evaluating the robustness of models. Our analysis reveals the
shortcomings of leading models, such as Attribute-Mask R-CNN and Fashionformer.
Additionally, we propose a baseline approach using naive data augmentation to
mitigate common failure cases and improve model robustness. Through this work,
we aim to inspire and support further research in fashion item detection and
segmentation for industrial applications. The dataset, annotation tool, code,
and models are available at \url{https://rizavelioglu.github.io/fashionfail/}.
