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mindspore-ci-bot 716d0eb969
!2894 debug for out of memory when global shuffle level was set for large dataset of Bert 		5 years ago
..
Transformer only save ckpt in rank0 for Transformer 5 years ago
alexnet modify dataset.py 5 years ago
bert debug for machine down because of out of memory when global shuffle level was set for large dataset 5 years ago
deepfm add Yolov3-darknet53 model 5 years ago
deeplabv3 add flags on function 5 years ago
faster_rcnn fix fastrcnn accuracy 5 years ago
gat adjust model zoo utils 5 years ago
gcn !2802 fix gcn import path error 5 years ago
googlenet support multi server muli process 5 years ago
lenet checkpoint add model_type 5 years ago
lenet_quant bug fix in auto create quant graph in master 5 years ago
lstm GPU Lstm network 5 years ago
mass Solve MASS codex warning. 5 years ago
mobilenetv2 !2801 add mobilenet v2 quant and resnet50 quant to model_zoo 5 years ago
mobilenetv2_quant add mobilenetV2 quant export 5 years ago
mobilenetv3 !2801 add mobilenet v2 quant and resnet50 quant to model_zoo 5 years ago
resnet move resnet series from example to model_zoo 5 years ago
resnet50_quant add mobilenet v2 quant and resnet50 quant to model_zoo 5 years ago
resnet_thor move resnet_thor series from example to model_zoo 5 years ago
resnext50 add resnext50 5 years ago
ssd change tensor dtype and shape from function to attr 5 years ago
utils add hccl_tools 5 years ago
vgg16 fix accurancy lower then 92 5 years ago
warpctc add warpctc to modelzoo 5 years ago
wide_and_deep clean codedex warning 5 years ago
yolov3_darknet53 fix data preprocess bug 5 years ago
yolov3_resnet18 add Yolov3-darknet53 model 5 years ago
README.md add transformer in model_zoo/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

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/Transformer

License

Apache License 2.0