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mindspore/model_zoo
Wei Luning 8f56528f8c add flags on function 5 years ago
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Transformer add flags on function 5 years ago
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bert add flags on function 5 years ago
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gcn gcn_rectification 5 years ago
googlenet Move googlenet into ModelZoo and add superlink in README 5 years ago
lenet dataset sink is false when run in CPU 5 years ago
lenet_quant change some comment name in the whole project 5 years ago
lstm !2418 add pretrain for lstm & vgg16 and remove lstm/vgg16/googlenet from directory 'mindspore/model_zoo' 5 years ago
mass Implements of masked seq2seq pre-training for language generation. 5 years ago
mobilenetv2 change tensor dtype and shape from function to attr 5 years ago
mobilenetv3 change tensor dtype and shape from function to attr 5 years ago
resnet101 change tensor dtype and shape from function to attr 5 years ago
ssd change tensor dtype and shape from function to attr 5 years ago
vgg16 add pretrain for lstm & vgg16 and remove lstm/vgg16/googlenet from directory 'mindspore/model_zoo' 5 years ago
wide_and_deep !2279 add model zoo script of wide and deep for gpu 5 years ago
yolov3 clear pylint for yolov3 5 years ago
README.md Move googlenet into ModelZoo and add superlink in README 5 years ago
__init__.py Implements of masked seq2seq pre-training for language generation. 5 years ago

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

LeNet

Parameters LeNet
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

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

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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