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update model zoo 

Signed-off-by: NTing Wang <kathy.wangting@huawei.com>
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docs/source_en/benchmark.md
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<a href="https://gitee.com/mindspore/docs/blob/master/docs/source_en/benchmark.md" target="_blank"><img src="./_static/logo_source.png"></a>
This document describes the MindSpore benchmarks.
For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo).
For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
## Training Performance
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docs/source_en/network_list.md
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| Domain | Sub Domain | Network | Ascend | GPU | CPU
|:------ |:------| :----------- |:------ |:------ |:-----
|Computer Version (CV) | Image Classification | [AlexNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/alexnet/src/alexnet.py) | Supported | Supported | Doing
| Computer Version (CV) | Image Classification | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/googlenet.py) | Supported | Doing | Doing
| Computer Version (CV) | Image Classification | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/googlenet/src/googlenet.py) | Supported | Doing | Doing
| Computer Version (CV) | Image Classification | [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lenet/src/lenet.py) | Supported | Supported | Supported
| Computer Version (CV) | Image Classification | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported | Doing | Doing
|Computer Version (CV) | Image Classification | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported |Doing | Doing
| Computer Version (CV) | Image Classification | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/vgg.py) | Supported | Doing | Doing
| Computer Version (CV) | Image Classification | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) | Supported | Doing | Doing
|Computer Version (CV) | Image Classification | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) | Supported |Doing | Doing
| Computer Version (CV) | Image Classification | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/vgg16/src/vgg.py) | Supported | Doing | Doing
| Computer Version (CV) | Mobile Image Classification<br>Image Classification<br>Semantic Tegmentation | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv2/src/mobilenetV2.py) | Supported | Doing | Doing
|Computer Version (CV) | Targets Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/ssd/src/ssd.py) | Supported |Doing | Doing
| Computer Version (CV) | Targets Detection | [YoloV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/yolov3/src/yolov3.py) | Supported | Doing | Doing
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docs/source_zh_cn/benchmark.md
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<a href="https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/benchmark.md" target="_blank"><img src="./_static/logo_source.png"></a>
本文介绍MindSpore的基准性能。MindSpore预训练模型可参考[Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)。
本文介绍MindSpore的基准性能。MindSpore预训练模型可参考[Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。
## 训练性能
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docs/source_zh_cn/network_list.md
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| 领域 | 子领域 | 网络 | Ascend | GPU | CPU
|:------ |:------| :----------- |:------ |:------ |:-----
|计算机视觉（CV） | 图像分类（Image Classification） | [AlexNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/alexnet/src/alexnet.py) | Supported | Supported | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/googlenet.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/googlenet/src/googlenet.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lenet/src/lenet.py) | Supported | Supported | Supported
| 计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported | Doing | Doing
|计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported |Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/vgg.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) | Supported | Doing | Doing
|计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) | Supported |Doing | Doing
| 计算机视觉（CV） | 图像分类（Image Classification） | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/vgg16/src/vgg.py) | Supported | Doing | Doing
| 计算机视觉（CV） | 移动端图像分类（Mobile Image Classification）<br>目标检测（Image Classification）<br>语义分割（Semantic Tegmentation） | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv2/src/mobilenetV2.py) | Supported | Doing | Doing
|计算机视觉（CV） | 目标检测（Targets Detection） | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/ssd/src/ssd.py) | Supported |Doing | Doing
| 计算机视觉（CV） | 目标检测（Targets Detection） | [YoloV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/yolov3/src/yolov3.py) | Supported | Doing | Doing
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resource/faq/FAQ_en.md
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Q: What are the available recommendation or text generation networks or models provided by MindSpore?
A: Currently, recommendation models such as Wide & Deep, DeepFM, and NCF are under development. In the natural language processing (NLP) field, Bert\_NEZHA is available and models such as MASS are under development. You can rebuild the network into a text generation network based on the scenario requirements. Please stay tuned for updates on the [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo).
A: Currently, recommendation models such as Wide & Deep, DeepFM, and NCF are under development. In the natural language processing (NLP) field, Bert\_NEZHA is available and models such as MASS are under development. You can rebuild the network into a text generation network based on the scenario requirements. Please stay tuned for updates on the [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
### Backend Support
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resource/faq/FAQ_zh_cn.md
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Q：MindSpore有哪些现成的推荐类或生成类网络或模型可用？
A：目前正在开发Wide & Deep、DeepFM、NCF等推荐类模型，NLP领域已经支持Bert_NEZHA，正在开发MASS等模型，用户可根据场景需要改造为生成类网络，可以关注[MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)。
A：目前正在开发Wide & Deep、DeepFM、NCF等推荐类模型，NLP领域已经支持Bert_NEZHA，正在开发MASS等模型，用户可根据场景需要改造为生成类网络，可以关注[MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。
### 后端支持
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tutorials/source_en/advanced_use/network_migration.md
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num_shards=device_num, shard_id=rank_id)
```
Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see <https://gitee.com/mindspore/mindspore/blob/master/example/resnet50_cifar10/dataset.py>.
Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/dataset.py>.
3. Build a network.
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6. Build the entire network.
The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
7. Define a loss function and an optimizer.
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## Examples
1. [Common network script examples](https://gitee.com/mindspore/mindspore/tree/master/example)
1. [Common dataset examples](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/loading_the_datasets.html)
2. [Common dataset examples](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/loading_the_datasets.html)
3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)
tutorials/source_en/use/multi_platform_inference.md
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1. Inference on the Ascend 910 AI processor
MindSpore provides the `model.eval` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see <https://gitee.com/mindspore/mindspore/blob/master/example/resnet50_cifar10/eval.py>.
MindSpore provides the `model.eval` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lenet/eval.py>.
```python
res = model.eval(dataset)
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tutorials/source_zh_cn/advanced_use/network_migration.md
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num_shards=device_num, shard_id=rank_id)
```
然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见<https://gitee.com/mindspore/mindspore/blob/master/example/resnet50_cifar10/dataset.py>。
然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/dataset.py>。
3. 构建网络。
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6. 构造整网。
将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。
将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。
7. 定义损失函数和优化器。
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## 样例参考
1. [常用网络脚本样例](https://gitee.com/mindspore/mindspore/tree/master/example)
1. [常用数据集读取样例](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html)
2. [常用数据集读取样例](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html)
3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
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2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)
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