Welcome to the Model Zoo for MindSpore

In order to facilitate developers to enjoy the benefits of MindSpore framework, we will continue to add typical networks and some of the related pre-trained models. If you have needs for the model zoo, you can file an issue on gitee or MindSpore, We will consider it in time.

• SOTA models using the latest MindSpore APIs

• The best benefits from MindSpore

• Officially maintained and supported

Table of Contents

Official

Research

• Community

Announcements

2021.9.15 Set up repository models

models comes from the directory model_zoo of repository mindspore. This new repository doesn't contain any history of commits about the directory model_zoo in mindspore, you could refer to the repository mindspore for the past commits.

Related Website

Here is the ModelZoo for MindSpore which support different devices including Ascend, GPU, CPU and mobile.

If you are looking for exclusive models only for Ascend using different ML platform, you could refer to Ascend ModelZoo and corresponding gitee repository

If you are looking for some pretrained checkpoint of mindspore, you could refer to MindSpore Hub or Download Website.

Disclaimers

Mindspore only provides scripts that downloads and preprocesses public datasets. We do not own these datasets and are not responsible for their quality or maintenance. Please make sure you have permission to use the dataset under the dataset’s license. The models trained on these dataset are for non-commercial research and educational purpose only.

To dataset owners: we will remove or update all public content upon request if you don’t want your dataset included on Mindspore, or wish to update it in any way. Please contact us through a Github/Gitee issue. Your understanding and contribution to this community is greatly appreciated.

MindSpore is Apache 2.0 licensed. Please see the LICENSE file.

License

Apache License 2.0

FAQ

For more information about MindSpore framework, please refer to FAQ

• Q: How to resolve the lack of memory while using the model directly under "models" with errors such as Failed to alloc memory pool memory?

  A: The typical reason for insufficient memory when directly using models under "models" is due to differences in operating mode (PYNATIVE_MODE), operating environment configuration, and license control (AI-TOKEN).

  • PYNATIVE_MODE usually uses more memory than GRAPH_MODE , especially in the training graph that needs back propagation calculation, there are two ways to try to solve this problem. Method 1: You can try to use some smaller batch size; Method 2: Add context.set_context(mempool_block_size="XXGB"), where the current maximum effective value of "XX" can be set to "31". If method 1 and method 2 are used in combination, the effect will be better.
  • The operating environment will also cause similar problems due to the different configurations of NPU cores, memory, etc.;
  • Different gears of License control (AI-TOKEN ) will cause different memory overhead during execution. You can also try to use some smaller batch sizes.

• Q: How to resolve the error about the interface are not supported in some network operations, such as cann not import?

  A: Please check the version of MindSpore and the branch you fetch the modelzoo scripts. Some model scripits in latest branch will use new interface in the latest version of MindSpore.

• Q: How to run the scripts on Windows system?

  A: Most the start-up scripts are written in bash, but we usually can't run bash directly on Windows. You can try start python directly without bash scripts. If you really need the start-up bash scripts, we suggest you the following method to get a bash environment on Windows:

  1. Use a virtual system or docker container with linux system. Then run the scripts in the virtual system or container.
  2. Use WSL, you could turn on the Windows Subsystem for Linux on Windows to obtain an linux system which could run the bash scripts.
  3. Use some bash tools on Windows, such as cygwin and git bash.