mindspore/model_zoo

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

• Models and Implementations
  • Computer Vision
    • Image Classification
      • GoogleNet
      • ResNet50[benchmark]
      • ResNet101
      • VGG16
      • AlexNet
      • LeNet
    • Object Detection and Segmentation
      • YoloV3
      • MobileNetV2
      • MobileNetV3
      • SSD
  • Natural Language Processing
    • BERT
    • MASS
    • Transformer

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/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/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/Transformer

License

Apache License 2.0