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fixed the invalid links

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caojiewen 2021-05-30 22:47:02 +08:00
parent 583857799f
commit b6b8cfe38b
9 changed files with 9 additions and 9 deletions
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2
model_zoo/README.md
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@ -65,7 +65,7 @@ In order to facilitate developers to enjoy the benefits of MindSpore framework,
- [GhostNet_Quant](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/ghostnet_quant/Readme.md)
- [SSD_GhostNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/ssd_ghostnet/README.md)
- [TinyNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/tinynet/README.md)
- [CycleGAN](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/cycle_gan/README.md)
- [CycleGAN](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/research/cv/CycleGAN/README.md)
- [FaceAttribute](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/FaceAttribute/README.md)
- [FaceDetection](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/FaceDetection/README.md)
- [FaceQualityAssessment](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/FaceQualityAssessment/README.md)

2
model_zoo/README_CN.md
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@ -65,7 +65,7 @@
- [GhostNet_Quant](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/ghostnet_quant/Readme.md)
- [SSD_GhostNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/ssd_ghostnet/README.md)
- [TinyNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/tinynet/README.md)
- [CycleGAN](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/cycle_gan/README.md)
- [CycleGAN](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/research/cv/CycleGAN/README.md)
- [人脸属性](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/FaceAttribute/README.md)
- [人脸检测](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/FaceDetection/README.md)
- [人脸图像质量评估](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/FaceQualityAssessment/README.md)

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model_zoo/official/nlp/q8bert/README_CN.md
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@ -55,7 +55,7 @@ Q8BERT模型的主干结构是transformer，一个转换器包含12个编码器
# 环境要求
- 硬件（Ascend或GPU）
- 使用Ascend或GPU处理器准备硬件环境。如需试用昇腾处理器，请发送[申请表](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)到ascend@huawei.com。申请通过后，即可获得资源。
- 使用Ascend或GPU处理器准备硬件环境。
- 框架
- [MindSpore](https://gitee.com/mindspore/mindspore)
- 更多关于Mindspore的信息，请查看以下资源：

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model_zoo/research/cv/Glore_resnet200/README_CN.md
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@ -226,7 +226,7 @@ result:{'top_1 acc':0.7974158653846154}
| 总时长 | 33时45分钟 |
| 参数(M) | 70.6 |
| 微调检查点| 807.57M（.ckpt文件） |
| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/Glore_resnet200) |
| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/Glore_resnet200) |
### 推理性能

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model_zoo/research/cv/glore_res50/README.md
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@ -228,7 +228,7 @@ sh run_eval.sh ~/dataset/imagenet 0 ~/ckpt/glore_res50_120-1251.ckpt
| 总时长 | 10.98小时 |
| 参数(M) | 30.5 |
| 微调检查点| 233.46M（.ckpt文件）|
| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/glore_res50_r1.1/model_zoo/research/cv/glore_res50) |
| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/glore_res50) |
# 随机情况说明

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model_zoo/research/cv/ntsnet/README.md
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@ -213,7 +213,7 @@ python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_
| Total time | 8pcs: 5.93 hours |
| Parameters | 87.6 |
| Checkpoint for Fine tuning | 333.07M(.ckpt file) |
| Scripts | [ntsnet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ntsnet) |
| Scripts | [ntsnet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/ntsnet) |
# [Description of Random Situation](#contents)

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model_zoo/research/cv/retinanet_resnet101/README_CN.md
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@ -291,7 +291,7 @@ mAP: 0.3710347196613514
| 最终损失 | 0.43 |
| 精确度 (8p) | mAP[0.3710] |
| 训练总时间 (8p) | 34h50m20s |
| 脚本 | [Retianet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/Retinanet_resnet101) |
| 脚本 | [Retianet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/retinanet_resnet101) |
#### 推理性能

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model_zoo/research/cv/retinanet_resnet152/README_CN.md
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@ -291,7 +291,7 @@ mAP: 0.3571988469737286
| 最终损失 | 0.69 |
| 精确度 (8p) | mAP[0.3571] |
| 训练总时间 (8p) | 41h32m20s |
| 脚本 | [Retianet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/Retinanet_resnet101) |
| 脚本 | [Retianet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/retinanet_resnet152) |
#### 推理性能

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model_zoo/research/cv/vgg19/README_CN.md
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@ -75,7 +75,7 @@ VGG 19网络主要由几个基本模块（包括卷积层和池化层）和三
# 环境要求
- 硬件（Ascend或GPU）
- 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](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)至ascend@huawei.com，审核通过即可获得资源。
- 准备Ascend或GPU处理器搭建硬件环境。
- 框架
- [MindSpore](https://www.mindspore.cn/install)
- 如需查看详情，请参见如下资源：