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README.md

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What is MindSpore Hub

MindSpore Hub is a pre-trained model application tool of the MindSpore ecosystem, serving as a channel for model developers and application developers.

  • Provide model developers with a convenient and fast channel for model release and submission.
  • Provide application developers with high-quality pre-trained models, and complete the work of model migration to deployment quickly using model loading and fine-tuning APIs.

Current pre-trained models in MindSpore Hub mainly cover four mainstream task scenarios including image classification, object detection, semantic segmentation and recommendation. You can check more models in MindSpore Hub Website.

Features

  • Flexible model loading: Access and experience pre-trained models quickly by searching models of interest on MindSpore Hub Website.
  • Easy-to-use transfer learning: Achieve transfer learning in one step via MindSpore's flexbile interface.

Installation

Binaries

Install MindSpore Hub using pip command. hub depends on the MindSpore version used in current environment.

  1. Please download whl package from MindSpore Hub download page.

    pip install #TODO
  2. Run the following command in a network-enabled environment to verify the installation.

    import mindspore_hub as mshub
    model = mshub.load("mindspore/ascend/0.7/googlenet_v1_cifar10")

Quickstart

See the Quick Start to implement model loading and fine-tuning.

Docs

For more information about installation guide, tutorials and APIs, please check out the User Documentation.

Community

As one part of MindSpore community, the following information in MindSpore Hub including governance, communication and contributing is consistent with the content in MindSpore community.

Governance

Check out how MindSpore Open Governance works.

Communication

Contributing

Welcome contributions. See our Contributor Wiki for more details。

Release Notes

The release notes, see our RELEASE

License

Apache License 2.0

项目简介

MindSpore hub

发行版本

当前项目没有发行版本

贡献者 9

开发语言

  • Python 93.6 %
  • Shell 6.4 %