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fix docs

This commit is contained in:
zhaojichen 2021-01-21 14:50:38 +08:00
parent 69416d105f
commit 9952da5a90
3 changed files with 6 additions and 6 deletions
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6
model_zoo/official/cv/FCN8s/README.md
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@ -52,13 +52,13 @@ Dataset used:
在通过官方网站安装MindSpore之后，你可以通过如下步骤开始训练以及评估：
- runing on Ascend with default paramaters
- running on Ascend with default parameters
```python
# run training example
python train.py --device_id device_id
# run evaluation example with default paramaters
# run evaluation example with default parameters
python eval.py --device_id device_id
```
@ -202,7 +202,7 @@ Dataset used:
| outputs | probability
| Loss | 0.038
| Speed | 1pc: 564.652 ms/step;
| Scripts | [FCN script](https://gitee.com/mindspore/mindspore/tree/r1.0/model_zoo/official/cv/FCN)
| Scripts | [FCN script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/FCN8s)
### Inference Performance

2
model_zoo/official/cv/openpose/README.md
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@ -41,7 +41,7 @@ In the currently provided training script, the coco2017 data set is used as an e
````bash
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
````
- Create the mask dataset.

4
model_zoo/research/recommend/autodis/README.md
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@ -32,7 +32,7 @@ AutoDis leverages a set of meta-embeddings for each numerical field, which are s
# [Dataset](#contents)
- [1] A dataset [Criteo](https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz) used in Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[J]. 2017.
- [1] A dataset Criteo used in Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[J]. 2017.
# [Environment Requirements](#contents)
@ -48,7 +48,7 @@ AutoDis leverages a set of meta-embeddings for each numerical field, which are s
After installing MindSpore via the official website, you can start training and evaluation as follows:
- runing on Ascend
- running on Ascend
```python
# run training example