diff --git a/model_zoo/official/cv/centerface/README.md b/model_zoo/official/cv/centerface/README.md index 0ad5cd6028..7650f57409 100644 --- a/model_zoo/official/cv/centerface/README.md +++ b/model_zoo/official/cv/centerface/README.md @@ -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) diff --git a/model_zoo/official/cv/deeptext/README.md b/model_zoo/official/cv/deeptext/README.md index 830d018e7a..a412c0defa 100644 --- a/model_zoo/official/cv/deeptext/README.md +++ b/model_zoo/official/cv/deeptext/README.md @@ -187,7 +187,7 @@ class 1 precision is 88.01%, recall is 82.77% | Loss Function | SoftmaxCrossEntropyWithLogits for classification, SmoothL2Loss for bbox regression| | Loss | ~0.008 | | 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 diff --git a/model_zoo/official/cv/psenet/README.md b/model_zoo/official/cv/psenet/README.md index c2a2e17fd5..5b473f888a 100644 --- a/model_zoo/official/cv/psenet/README.md +++ b/model_zoo/official/cv/psenet/README.md @@ -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 | | +| Scripts | | ### Inference Performance diff --git a/model_zoo/official/cv/psenet/README_CN.md b/model_zoo/official/cv/psenet/README_CN.md index 39792a0b0f..c2f40f8f8d 100644 --- a/model_zoo/official/cv/psenet/README_CN.md +++ b/model_zoo/official/cv/psenet/README_CN.md @@ -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 | | +| 脚本 | | ### 推理性能 diff --git a/model_zoo/official/nlp/fasttext/README.md b/model_zoo/official/nlp/fasttext/README.md index 682dedc349..276ac4c99d 100644 --- a/model_zoo/official/nlp/fasttext/README.md +++ b/model_zoo/official/nlp/fasttext/README.md @@ -1,4 +1,4 @@ -![](https://www.mindspore.cn/static/img/logo.a3e472c9.png) +![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png) diff --git a/model_zoo/official/nlp/gnmt_v2/README.md b/model_zoo/official/nlp/gnmt_v2/README.md index 618ab203ba..89a6ef9d2d 100644 --- a/model_zoo/official/nlp/gnmt_v2/README.md +++ b/model_zoo/official/nlp/gnmt_v2/README.md @@ -1,4 +1,4 @@ -![](https://www.mindspore.cn/static/img/logo.a3e472c9.png) +![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png) diff --git a/model_zoo/official/nlp/mass/README_CN.md b/model_zoo/official/nlp/mass/README_CN.md index e4e7e8ffd4..a07f812b44 100644 --- a/model_zoo/official/nlp/mass/README_CN.md +++ b/model_zoo/official/nlp/mass/README_CN.md @@ -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). diff --git a/model_zoo/official/nlp/prophetnet/README.md b/model_zoo/official/nlp/prophetnet/README.md index 141b008584..b4f2a3c742 100644 --- a/model_zoo/official/nlp/prophetnet/README.md +++ b/model_zoo/official/nlp/prophetnet/README.md @@ -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) diff --git a/model_zoo/research/audio/fcn-4/README.md b/model_zoo/research/audio/fcn-4/README.md index dfd0359599..5d0fe4ddff 100644 --- a/model_zoo/research/audio/fcn-4/README.md +++ b/model_zoo/research/audio/fcn-4/README.md @@ -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) diff --git a/model_zoo/research/cv/centernet/README.md b/model_zoo/research/cv/centernet/README.md index ef31c84f0a..5ff91b0b22 100644 --- a/model_zoo/research/cv/centernet/README.md +++ b/model_zoo/research/cv/centernet/README.md @@ -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)