  Compared with the domain-specific model, the vision-language pre-training
models (VLPMs) have shown superior performance on downstream tasks with fast
fine-tuning process. For example, ERNIE-ViL, Oscar and UNIMO trained VLPMs with
a uniform transformers stack architecture and large amounts of image-text
paired data, achieving remarkable results on downstream tasks such as
image-text reference(IR and TR), vision question answering (VQA) and image
captioning (IC) etc. During the training phase, VLPMs are always fed with a
combination of multiple public datasets to meet the demand of large-scare
training data. However, due to the unevenness of data distribution including
size, task type and quality, using the mixture of multiple datasets for model
training can be problematic. In this work, we introduce a large-scale
multi-modal corpora named WuDaoMM, totally containing more than 650M image-text
pairs. Specifically, about 600 million pairs of data are collected from
multiple webpages in which image and caption present weak correlation, and the
other 50 million strong-related image-text pairs are collected from some
high-quality graphic websites. We also release a base version of WuDaoMM with 5
million strong-correlated image-text pairs, which is sufficient to support the
common cross-modal model pre-training. Besides, we trained both an
understanding and a generation vision-language (VL) model to test the dataset
effectiveness. The results show that WuDaoMM can be applied as an efficient
dataset for VLPMs, especially for the model in text-to-image generation task.
The data is released at https://data.wudaoai.cn
