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Overview

In this section, We would like to give a quick review of the open-source library MMSelfSup.

We will first illustrate the basic idea of the self-supervised learning, then we will briefly describe the design of MMSelfSup. After that, we will provide a hands-on roadmap to help the users to play with MMSelfSup

Introduction of Self-supervised Learning

Self-supervised learning(SSL) is a promising learning paradigm, which aims to leverage the potential of the huge amount of unlabeled data. In SSL, we typically use the label generated automatically without human labor, to learn a model to extract the discriminative representation of the data. Equipped with the powerful pre-trained model by SSL, we are able to improve various downstream vision tasks currently.

The community has witnessed rapid development of SSL in the past few years. Our codebase aims to become an easy-to-use and user-friendly library, to help the research and engineering. We will elaborate the properties and design of MMSelfSup in the following sections.

Design of MMSelfSup

MMSelfSup follows the modular designed architecture as other OpenMMLab projects. the overall framework is illustrated below:

  • Datasets provides the support for various datasets, with many useful augmentation strategy.

  • Algorithms consists of many milestone SSL works with easy-to-use interface.

  • Tools includes the training and analysis tools for SSL

  • Benchmarks introduces many examples of how to use SSL for various downstream tasks(e.g., classification, detection, segmentation and etc.).

Hands-on Roadmap of MMSelfSup

To help the user to use the MMSelfSup quickly, we recommend the following roadmap for using our library.

Play with MMSelfSup

Typically, SSL is considered as the pre-training algorithm for various model architectures. Thus, the complete pipeline consists of the pre-training stage and the benchmark stage.

  • For the user who wants to try MMSelfSup with various SSL algorithms. We first refer the user to Get Started for the environment setup.

  • For the pre-training stage, we refer the user to Pre-train for using various SSL algorithms to obtain the pre-trained model.

  • For the benchmark stage, we refer the user to Benchmark for examples and usage of applying the pre-trained models in many downstream tasks.

  • Also, we provide some analysis tools and visualization tools Useful Tools to help diagnose the algorithm.

Learn SSL with MMSelfSup

If you are new to SSL, we recommend using the Model Zoo as a reference to learn the representative SSL algorithms.

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