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mindspore/model_zoo
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README.md

Welcome to the Model Zoo for MindSpore

In order to facilitate developers to enjoy the benefits of MindSpore framework and Huawei chips, we will continue to add typical networks and 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 and Huawei chips

  • Officially maintained and supported

Table of Contents

Announcements

Date News
May 31, 2020 Support MindSpore v0.3.0-alpha

Models and Implementations

Computer Vision

Image Classification

GoogleNet

Parameters GoogleNet
Published Year 2014
Paper Going Deeper with Convolutions
Resource Ascend 910
Features • Mixed Precision • Multi-GPU training support with Ascend
MindSpore Version 0.3.0-alpha
Dataset CIFAR-10
Training Parameters epoch=125, batch_size = 128, lr=0.1
Optimizer Momentum
Loss Function Softmax Cross Entropy
Accuracy 1pc: 93.4%; 8pcs: 92.17%
Speed 79 ms/Step
Loss 0.0016
Params (M) 6.8
Checkpoint for Fine tuning 43.07M (.ckpt file)
Model for inference 21.50M (.onnx file), 21.60M(.geir file)
Scripts https://gitee.com/mindspore/mindspore/tree/master/model_zoo/googlenet

ResNet50

Parameters ResNet50
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
Accuracy
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

ResNet101

Parameters ResNet101
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
Accuracy
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

VGG16

Parameters VGG16
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
Accuracy
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

AlexNet

Parameters AlexNet
Published Year 2012
Paper ImageNet Classification with Deep Convolutional Neural Networks
Resource Ascend 910
Features support with Ascend, GPU
MindSpore Version 0.5.0-beta
Dataset CIFAR10
Training Parameters epoch=30, batch_size=32
Optimizer Momentum
Loss Function SoftmaxCrossEntropyWithLogits
Accuracy 88.23%
Speed 1481fps
Loss 0.108
Params (M) 61.10
Checkpoint for Fine tuning 445MB(.ckpt file)
Scripts https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet

LeNet

Parameters LeNet
Published Year 1998
Paper Gradient-Based Learning Applied to Document Recognition
Resource Ascend 910
Features support with Ascend, GPU, CPU
MindSpore Version 0.5.0-beta
Dataset MNIST
Training Parameters epoch=10, batch_size=32
Optimizer Momentum
Loss Function SoftmaxCrossEntropyWithLogits
Accuracy 98.52%
Speed 18680fps
Loss 0.004
Params (M) 0.06
Checkpoint for Fine tuning 483KB(.ckpt file)
Scripts https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet

Object Detection and Segmentation

YoloV3

Parameters YoLoV3
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
Mean Average Precision (mAP@0.5)
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

MobileNetV2

Parameters MobileNetV2
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
Mean Average Precision (mAP@0.5)
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

MobileNetV3

Parameters MobileNetV3
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
Mean Average Precision (mAP@0.5)
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

SSD

Parameters SSD
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
Mean Average Precision (mAP@0.5)
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

Natural Language Processing

BERT

Parameters BERT
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
GLUE Score
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

MASS

Parameters MASS
Published Year
Paper
Resource
Features
MindSpore Version
Dataset
Training Parameters
Optimizer
Loss Function
ROUGE Score
Speed
Loss
Params (M)
Checkpoint for Fine tuning
Model for inference
Scripts

Transformer

Parameters Transformer
Published Year 2017
Paper Attention Is All You Need
Resource Ascend 910
Features • Multi-GPU training support with Ascend
MindSpore Version 0.5.0-beta
Dataset WMT Englis-German
Training Parameters epoch=52, batch_size=96
Optimizer Adam
Loss Function Softmax Cross Entropy
BLEU Score 28.7
Speed 410ms/step (8pcs)
Loss 2.8
Params (M) 213.7
Checkpoint for inference 2.4G (.ckpt file)
Scripts https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/transformer

License

Apache License 2.0

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MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.

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