mindspore2022/model_zoo

• MindSpore Logo
• • Welcome to the Model Zoo for MindSpore
  • Table of Contents
  • Announcements
  • Related Website
  • Disclaimers
  • License
  • FAQ

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

  • Computer Vision

    • Image Classification
      • DenseNet
      • GoogleNet
      • ResNet50[benchmark]
      • ResNet50_Quant
      • ResNet101
      • ResNext50
      • VGG16
      • AlexNet
      • LeNet
      • LeNet_Quant
      • MobileNetV2
      • MobileNetV2_Quant
      • MobileNetV3
      • InceptionV3
      • InceptionV4
    • Object Detection and Segmentation
      • DeepLabV3
      • FasterRCNN
      • YoloV3-DarkNet53
      • YoloV3-ResNet18
      • MaskRCNN
      • Unet
      • SSD
      • Warp-CTC
      • RetinaFace-ResNet50
    • Keypoint Detection
      • OpenPose

  • Natural Language Processing

    • BERT[benchmark]
    • TinyBERT
    • GNMT V2
    • FastText
    • LSTM
    • MASS
    • Transformer
    • CPM

  • Recommender Systems

    • DeepFM
    • NAML
    • Wide&Deep[benchmark]

  • Graph Neural Networks

    • BGCF
    • GAT
    • GCN

• Research

  • Computer Vision
    • GhostNet_Quant
    • SSD_GhostNet
    • TinyNet
    • CycleGAN
    • FaceAttribute
    • FaceDetection
    • FaceQualityAssessment
    • FaceRecognitionForTracking
    • FaceRecognition
    • CenterNet
  • Natural Language Processing
    • DS-CNN
  • Recommender Systems
    • AutoDis
  • Audio
    • FCN-4
    • DeepSpeech2
    • Wavenet
  • High Performance Computing
    • GOMO
    • Molecular_Dynamics
    • SPONGE
    • PINNs

• Community

Announcements

Date	News
September 25, 2020	Support MindSpore v1.0.0
September 01, 2020	Support MindSpore v0.7.0-beta
July 31, 2020	Support MindSpore v0.6.0-beta

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

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

• Q: How to resolve the lack of memory while using PYNATIVE_MODE with errors such as Failed to alloc memory pool memory?

  A: PYNATIVE_MODE usually requires more memory than GRAPH_MODE, especially in training process which have to deal with back propagation. You could try using smaller batch size.