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The following topics will be covered: 1. We will first introduce deep learning foundation model library (MMPreTrain). The milestone works of foundation models will be presented, and we then elaborate the design of our codebase and highlight properties. 2. In this part, we will highlight the newly updated features related to multimodality learning. First, we will discuss the recent progress of the community in multimodal learning, followed by sharing practical examples of how to use MMPreTrain for various multimodal tasks. 3. We then will show how to play foundation models based on image classification. We will illustrate how to use MMPretrain for the classic image classification task and introduce the recent progress in vision backbone. 4. Finally, we share more recent works on self-supervised learning. We would like to introduce the concept and pipeline of self-supervised learning at the beginning. Then we will introduce how to learn self-supervised learning with MMPretrain .