  Large-scale datasets for single-label multi-class classification, such as
\emph{ImageNet-1k}, have been instrumental in advancing deep learning and
computer vision. However, a critical and often understudied aspect is the
comprehensive quality assessment of these datasets, especially regarding
potential multi-label annotation errors. In this paper, we introduce a
lightweight, user-friendly, and scalable framework that synergizes human and
machine intelligence for efficient dataset validation and quality enhancement.
We term this novel framework \emph{Multilabelfy}. Central to Multilabelfy is an
adaptable web-based platform that systematically guides annotators through the
re-evaluation process, effectively leveraging human-machine interactions to
enhance dataset quality. By using Multilabelfy on the ImageNetV2 dataset, we
found that approximately $47.88\%$ of the images contained at least two labels,
underscoring the need for more rigorous assessments of such influential
datasets. Furthermore, our analysis showed a negative correlation between the
number of potential labels per image and model top-1 accuracy, illuminating a
crucial factor in model evaluation and selection. Our open-source framework,
Multilabelfy, offers a convenient, lightweight solution for dataset
enhancement, emphasizing multi-label proportions. This study tackles major
challenges in dataset integrity and provides key insights into model
performance evaluation. Moreover, it underscores the advantages of integrating
human expertise with machine capabilities to produce more robust models and
trustworthy data development. The source code for Multilabelfy will be
available at https://github.com/esla/Multilabelfy.
  \keywords{Computer Vision \and Dataset Quality Enhancement \and Dataset
Validation \and Human-Computer Interaction \and Multi-label Annotation.}
