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fix README link

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zhaoting 4 years ago
parent 5e5489d59f
commit 289f856955
10 changed files with 10 additions and 10 deletions
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2
model_zoo/official/cv/centerface/README.md
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@ -84,7 +84,7 @@ other datasets need to use the same format as WiderFace.
- Hardware（Ascend）
- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Framework
- [MindSpore](https://cmc-szv.clouddragon.huawei.com/cmcversion/index/search?searchKey=Do-MindSpore%20V100R001C00B622)
- [MindSpore](https://www.mindspore.cn/install/en)
- For more information, please check the resources below：
- [MindSpore tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)

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model_zoo/official/cv/deeptext/README.md
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@ -188,7 +188,7 @@ class 1 precision is 88.01%, recall is 82.77%
| Loss | ~0.008 |
| Accuracy (8p) | precision=0.8854, recall=0.8024 |
| Total time (8p) | 4h |
| Scripts | [deeptext script](https://gitee.com/mindspore/mindspore/tree/r1.1/mindspore/official/cv/deeptext) |
| Scripts | [deeptext script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeptext) |
#### Inference Performance

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model_zoo/official/cv/psenet/README.md
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@ -197,7 +197,7 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
| Total time | 1pc: 75.48 h; 8pcs: 10.01 h |
| Parameters (M) | 27.36 |
| Checkpoint for Fine tuning | 109.44M (.ckpt file) |
| Scripts | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/psenet> |
| Scripts | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet> |
### Inference Performance

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model_zoo/official/cv/psenet/README_CN.md
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@ -195,7 +195,7 @@ Calculated!{"precision": 0.8147966668299853，"recall"：0.8006740491092923，"h
| 总时间 | 1卡：75.48小时；4卡：18.87小时|
| 参数(M) | 27.36 |
| 微调检查点 | 109.44M （.ckpt file） |
| 脚本 | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/psenet> |
| 脚本 | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet> |
### 推理性能

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model_zoo/official/nlp/fasttext/README.md
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@ -1,4 +1,4 @@
![](https://www.mindspore.cn/static/img/logo.a3e472c9.png)
![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png)
<!-- TOC -->

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model_zoo/official/nlp/gnmt_v2/README.md
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@ -1,4 +1,4 @@
![](https://www.mindspore.cn/static/img/logo.a3e472c9.png)
![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png)
<!-- TOC -->

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model_zoo/official/nlp/mass/README_CN.md
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@ -47,7 +47,7 @@ BERT（Devlin等人，2018年）采用有屏蔽的语料丰富文本预训练Tra
受BERT、GPT及其他语言模型的启发，微软致力于在此基础上研究[掩式序列到序列（MASS）预训练语言生成](https://www.microsoft.com/en-us/research/uploads/prod/2019/06/MASS-paper-updated-002.pdf)。MASS的参数k很重要，用来控制屏蔽后的分片长度。BERT和GPT属于特例，k等于1或者句长。
[MASS介绍 — 序列对序列语言生成任务中性能优于BERT和GPT的预训练方法](https://www.microsoft.com/en-us/research/blog/introduction-mass-a-pre-training-method-thing-forts-bert-and-gpt-in-sequence-to-sequence-language-generate-tasks/)
[MASS介绍 — 序列对序列语言生成任务中性能优于BERT和GPT的预训练方法](https://www.microsoft.com/en-us/research/blog/introducing-mass-a-pre-training-method-that-outperforms-bert-and-gpt-in-sequence-to-sequence-language-generation-tasks/)
[论文](https://www.microsoft.com/en-us/research/uploads/prod/2019/06/MASS-paper-updated-002.pdf): Song, Kaitao, Xu Tan, Tao Qin, Jianfeng Lu and Tie-Yan Liu.“MASS: Masked Sequence to Sequence Pre-training for Language Generation.”ICML (2019).

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model_zoo/official/nlp/prophetnet/README.md
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@ -655,4 +655,4 @@ The model has been validated on Ascend environment, not validated on CPU and GPU
# ModelZoo Homepage
[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
[Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)

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model_zoo/research/audio/fcn-4/README.md
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@ -192,7 +192,7 @@ Parameters for both training and evaluation can be set in config.py
| Speed | 1pc: 160 samples/sec; |
| Total time | 1pc: 20 mins; |
| Checkpoint for Fine tuning | 198.73M(.ckpt file) |
| Scripts | [music_auto_tagging script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/audio/fcn-4) |
| Scripts | [music_auto_tagging script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/audio/fcn-4) |
## [ModelZoo Homepage](#contents)

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model_zoo/research/cv/centernet/README.md
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@ -79,7 +79,7 @@ Dataset used: [COCO2017](https://cocodataset.org/)
- Hardware（Ascend）
- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Framework
- [MindSpore](https://cmc-szv.clouddragon.huawei.com/cmcversion/index/search?searchKey=Do-MindSpore%20V100R001C00B622)
- [MindSpore](https://www.mindspore.cn/install/en)
- For more information, please check the resources below：
- [MindSpore tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)

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