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mindspore/model_zoo
zhouyaqiang b1a1e24e07 add resnext50 5 years ago
..
Transformer add flags on function 5 years ago
alexnet modify dataset.py 5 years ago
bert add flags on function 5 years ago
deepfm add DeepFM 5 years ago
deeplabv3 add flags on function 5 years ago
faster_rcnn optimize fastrcnn training process 5 years ago
gat Add gat to model zoo 5 years ago
gcn gcn_rectification 5 years ago
googlenet Move googlenet into ModelZoo and add superlink in README 5 years ago
lenet checkpoint add model_type 5 years ago
lenet_quant checkpoint add model_type 5 years ago
lstm change lstm import pacakge location 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
resnext50 add resnext50 5 years ago
ssd change tensor dtype and shape from function to attr 5 years ago
vgg16 fix accurancy lower then 92 5 years ago
wide_and_deep add wide&deep stanalone training script for gpu in model zoo 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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