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mindspore-ci-bot 14868eb2b0 !2465 GCN rectification 
Merge pull request !2465 from chentingting/gcn_rectification
 		2020-06-23 10:40:11 +08:00
..
Transformer fix decoder loop for Transformer model 2020-06-18 14:18:21 +08:00
alexnet !2370 modify alexnet shell def get_lr args 2020-06-20 10:54:57 +08:00
bert Init GraphKernel. 2020-06-20 22:31:54 +08:00
deepfm add DeepFM 2020-05-28 23:31:11 +08:00
deeplabv3 1:modify shell for deeplabv3 2020-06-10 18:55:29 +08:00
faster_rcnn fix fastrcnn eval failed 2020-06-18 10:23:45 +08:00
gat Add gat to model zoo 2020-06-16 08:32:45 +00:00
gcn gcn_rectification 2020-06-23 10:01:59 +08:00
googlenet Move googlenet into ModelZoo and add superlink in README 2020-06-19 20:50:10 +08:00
lenet dataset sink is false when run in CPU 2020-06-20 11:28:57 +08:00
lenet_quant change some comment name in the whole project 2020-06-22 12:59:21 +08:00
lstm !2418 add pretrain for lstm & vgg16 and remove lstm/vgg16/googlenet from directory 'mindspore/model_zoo' 2020-06-22 22:51:19 +08:00
mass Implements of masked seq2seq pre-training for language generation. 2020-06-20 15:48:49 +08:00
mobilenetv2 change tensor dtype and shape from function to attr 2020-06-12 19:03:23 +08:00
mobilenetv3 change tensor dtype and shape from function to attr 2020-06-12 19:03:23 +08:00
resnet101 change tensor dtype and shape from function to attr 2020-06-12 19:03:23 +08:00
ssd change tensor dtype and shape from function to attr 2020-06-12 19:03:23 +08:00
vgg16 add pretrain for lstm & vgg16 and remove lstm/vgg16/googlenet from directory 'mindspore/model_zoo' 2020-06-22 17:17:10 +08:00
wide_and_deep !2279 add model zoo script of wide and deep for gpu 2020-06-18 20:08:47 +08:00
yolov3 clear pylint for yolov3 2020-06-19 10:28:07 +08:00
README.md Move googlenet into ModelZoo and add superlink in README 2020-06-19 20:50:10 +08:00
__init__.py Implements of masked seq2seq pre-training for language generation. 2020-06-20 15:48:49 +08:00

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