  The quality of pre-training data plays a critical role in the performance of
foundation models. Popular foundation models often design their own recipe for
data filtering, which makes it hard to analyze and compare different data
filtering approaches. DataComp is a new benchmark dedicated to evaluating
different methods for data filtering. This paper describes our learning and
solution when participating in the DataComp challenge. Our filtering strategy
includes three stages: single-modality filtering, cross-modality filtering, and
data distribution alignment. We integrate existing methods and propose new
solutions, such as computing CLIP score on horizontally flipped images to
mitigate the interference of scene text, using vision and language models to
retrieve training samples for target downstream tasks, rebalancing the data
distribution to improve the efficiency of allocating the computational budget,
etc. We slice and dice our design choices, provide in-depth analysis, and
discuss open questions. Our approach outperforms the best method from the
DataComp paper by over 4% on the average performance of 38 tasks and by over 2%
on ImageNet.
