diff --git a/mindspore/lite/README_CN.md b/mindspore/lite/README_CN.md index ff3abadb6e..8cf9900d9e 100644 --- a/mindspore/lite/README_CN.md +++ b/mindspore/lite/README_CN.md @@ -9,7 +9,7 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推 MindSpore Lite Architecture -欲了解更多详情，请查看我们的[MindSpore Lite 总体架构](https://www.mindspore.cn/lite/doc/note/zh-CN/master/design/mindspore/architecture_lite.html)。 +欲了解更多详情，请查看我们的[MindSpore Lite 总体架构](https://www.mindspore.cn/doc/note/zh-CN/master/design/mindspore/architecture_lite.html)。 ## MindSpore Lite技术特点 diff --git a/model_zoo/official/cv/resnet50_quant/Readme.md b/model_zoo/official/cv/resnet50_quant/Readme.md index 9cbed1c3a9..3edc9e5ad7 100644 --- a/model_zoo/official/cv/resnet50_quant/Readme.md +++ b/model_zoo/official/cv/resnet50_quant/Readme.md @@ -46,7 +46,7 @@ Dataset used: [imagenet](http://www.image-net.org/) ## [Mixed Precision](#contents) -The [mixed precision](https://www.mindspore.cn/tutorial/training/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. +The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’. # [Environment Requirements](#contents) diff --git a/model_zoo/official/cv/resnext50/README.md b/model_zoo/official/cv/resnext50/README.md index 00fbe4503b..b1ec1fbc6e 100644 --- a/model_zoo/official/cv/resnext50/README.md +++ b/model_zoo/official/cv/resnext50/README.md @@ -47,7 +47,7 @@ Dataset used: [imagenet](http://www.image-net.org/) ## [Mixed Precision](#contents) -The [mixed precision](https://www.mindspore.cn/tutorial/training/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. +The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’. diff --git a/model_zoo/official/cv/ssd/README.md b/model_zoo/official/cv/ssd/README.md index df3ffb064a..7c0084c00b 100644 --- a/model_zoo/official/cv/ssd/README.md +++ b/model_zoo/official/cv/ssd/README.md @@ -147,7 +147,7 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] ### Training on Ascend -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_datasets.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** - Distribute mode diff --git a/model_zoo/official/cv/unet/README.md b/model_zoo/official/cv/unet/README.md index 802d7c9c08..3a99d509ed 100644 --- a/model_zoo/official/cv/unet/README.md +++ b/model_zoo/official/cv/unet/README.md @@ -236,7 +236,7 @@ step: 300, loss is 0.18949677, fps is 57.63118508760329 ## [How to use](#contents) ### Inference -If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example: +If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example: - Running on Ascend diff --git a/model_zoo/official/cv/yolov3_resnet18/README.md b/model_zoo/official/cv/yolov3_resnet18/README.md index c1c234b871..7afb4beecd 100644 --- a/model_zoo/official/cv/yolov3_resnet18/README.md +++ b/model_zoo/official/cv/yolov3_resnet18/README.md @@ -135,7 +135,7 @@ After installing MindSpore via the official website, you can start training and ## [Training Process](#contents) ### Training on Ascend -To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_datasets.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.** +To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_dataset.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.** - Stand alone mode diff --git a/model_zoo/research/cv/ghostnet_quant/Readme.md b/model_zoo/research/cv/ghostnet_quant/Readme.md index 3343aa666e..2629c347b6 100644 --- a/model_zoo/research/cv/ghostnet_quant/Readme.md +++ b/model_zoo/research/cv/ghostnet_quant/Readme.md @@ -50,8 +50,8 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/) - Framework - [MindSpore](https://www.mindspore.cn/install/en) - For more information, please check the resources below： - - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) + - [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) # [Script description](#contents) diff --git a/model_zoo/research/cv/ssd_ghostnet/README.md b/model_zoo/research/cv/ssd_ghostnet/README.md index 2663ae469c..c4aa26ef2b 100644 --- a/model_zoo/research/cv/ssd_ghostnet/README.md +++ b/model_zoo/research/cv/ssd_ghostnet/README.md @@ -134,7 +134,7 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c ### Training on Ascend -To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_datasets.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** +To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.** - Distribute mode