update readme

README.md

@@ -1,6 +1,6 @@
 <div align="center" markdown>
 
-# MindCV
+# MindSpore CV
 
 [![CI](https://github.com/mindspore-lab/mindcv/actions/workflows/ci.yml/badge.svg)](https://github.com/mindspore-lab/mindcv/actions/workflows/ci.yml)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mindcv)](https://pypi.org/project/mindcv)
@@ -25,7 +25,7 @@ English | [中文](README_CN.md)
 
 ## Introduction
 
-MindCV is an open-source toolbox for computer vision research and development based on [MindSpore](https://www.mindspore.cn/en). It collects a series of classic and SoTA vision models, such as ResNet and SwinTransformer, along with their pre-trained weights and training strategies. SoTA methods such as auto augmentation are also provided for performance improvement. With the decoupled module design, it is easy to apply or adapt MindCV to your own CV tasks.
+MindSpore CV is an open-source toolbox for computer vision research and development based on [MindSpore](https://www.mindspore.cn/en). It collects a series of classic and SoTA vision models, such as ResNet and SwinTransformer, along with their pre-trained weights and training strategies. SoTA methods such as auto augmentation are also provided for performance improvement. With the decoupled module design, it is easy to apply or adapt MindSpore CV to your own CV tasks.
 
 The following is the corresponding `mindcv` versions and supported `mindspore` versions.
 
@@ -40,7 +40,7 @@ The following is the corresponding `mindcv` versions and supported `mindspore` v
 
 ### Major Features
 
-- **Easy-to-Use.** MindCV decomposes the vision framework into various configurable components. It is easy to customize your data pipeline, models, and learning pipeline with MindCV:
+- **Easy-to-Use.** MindSpore CV decomposes the vision framework into various configurable components. It is easy to customize your data pipeline, models, and learning pipeline with MindSpore CV:
 
   ```pycon
   >>> import mindcv
@@ -57,13 +57,13 @@ The following is the corresponding `mindcv` versions and supported `mindspore` v
   python train.py --model=swin_tiny --pretrained --opt=adamw --lr=0.001 --data_dir=/path/to/data
   ```
 
-- **State-of-The-Art.** MindCV provides various CNN-based and Transformer-based vision models including SwinTransformer. Their pretrained weights and performance reports are provided to help users select and reuse the right model:
+- **State-of-The-Art.** MindSpore CV provides various CNN-based and Transformer-based vision models including SwinTransformer. Their pretrained weights and performance reports are provided to help users select and reuse the right model:
 
-- **Flexibility and efficiency.** MindCV is built on MindSpore which is an efficient DL framework that can be run on different hardware platforms (GPU/CPU/Ascend). It supports both graph mode for high efficiency and pynative mode for flexibility.
+- **Flexibility and efficiency.** MindSpore CV is built on MindSpore which is an efficient DL framework that can be run on different hardware platforms (GPU/CPU/Ascend). It supports both graph mode for high efficiency and pynative mode for flexibility.
 
 ## Model Zoo
 
-The performance of the models trained with MindCV is summarized in [here](https://mindspore-lab.github.io/mindcv/modelzoo/), where the training recipes and weights are both available.
+The performance of the models trained with MindSpore CV is summarized in [here](https://mindspore-lab.github.io/mindcv/modelzoo/), where the training recipes and weights are both available.
 
 Model introduction and training details can be viewed in each sub-folder under [configs](configs).
 
@@ -75,7 +75,7 @@ See [Installation](https://mindspore-lab.github.io/mindcv/installation/) for det
 
 ### Hands-on Tutorial
 
-To get started with MindCV, please see the [Quick Start](docs/en/tutorials/quick_start.md), which will give you a quick tour on each key component and the train/validate/predict pipelines.
+To get started with MindSpore CV, please see the [Quick Start](docs/en/tutorials/quick_start.md), which will give you a quick tour on each key component and the train/validate/predict pipelines.
 
 Below are a few code snippets for your taste.
 
@@ -212,7 +212,7 @@ More examples about training and validation can be seen in [examples](examples/s
 
 ## Tutorials
 
-We provide the following jupyter notebook tutorials to help users learn to use MindCV.
+We provide the following jupyter notebook tutorials to help users learn to use MindSpore CV.
 
 - [Learn about configs](docs/en/tutorials/configuration.md)
 - [Inference with a pretrained model](docs/en/tutorials/inference.md)
@@ -222,7 +222,7 @@ We provide the following jupyter notebook tutorials to help users learn to use M
 
 ## Model List
 
-Currently, MindCV supports the model families listed below. More models with pre-trained weights are under development and will be released soon.
+Currently, MindSpore CV supports the model families listed below. More models with pre-trained weights are under development and will be released soon.
 
 <details open markdown>
 <summary> Supported models </summary>
@@ -351,7 +351,7 @@ See [RELEASE](RELEASE.md) for detailed history.
 
 ## How to Contribute
 
-We appreciate all kinds of contributions including issues and PRs to make MindCV better.
+We appreciate all kinds of contributions including issues and PRs to make MindSpore CV better.
 
 Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
 Please follow the [Model Template and Guideline](docs/en/how_to_guides/write_a_new_model.md) for contributing a model that fits the overall interface :)
@@ -362,7 +362,7 @@ This project follows the [Apache License 2.0](LICENSE.md) open-source license.
 
 ## Acknowledgement
 
-MindCV is an open-source project jointly developed by the MindSpore team, Xidian University, and Xi'an Jiaotong University.
+MindSpore CV is an open-source project jointly developed by the MindSpore team, Xidian University, and Xi'an Jiaotong University.
 Sincere thanks to all participating researchers and developers for their hard work on this project.
 We also acknowledge the computing resources provided by [OpenI](https://openi.pcl.ac.cn/).
 

README_CN.md

@@ -1,6 +1,6 @@
 <div align="center" markdown>
 
-# MindCV
+# MindSpore CV
 
 [![CI](https://github.com/mindspore-lab/mindcv/actions/workflows/ci.yml/badge.svg)](https://github.com/mindspore-lab/mindcv/actions/workflows/ci.yml)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mindcv)](https://pypi.org/project/mindcv)
@@ -25,13 +25,13 @@
 
 ## 简介
 
-MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力于计算机视觉相关技术研发的开源工具箱。它提供大量的计算机视觉领域的经典模型和SoTA模型以及它们的预训练权重和训练策略。同时，还提供了自动增强等SoTA算法来提高模型性能。通过解耦的模块设计，您可以轻松地将MindCV应用到您自己的CV任务中。
+MindSpore CV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力于计算机视觉相关技术研发的开源工具箱。它提供大量的计算机视觉领域的经典模型和SoTA模型以及它们的预训练权重和训练策略。同时，还提供了自动增强等SoTA算法来提高模型性能。通过解耦的模块设计，您可以轻松地将MindSpore CV应用到您自己的CV任务中。
 
 主分支代码目前支持 **MindSpore 1.8+** 以上的版本，包含 **MindSpore 2.0🔥** 版本。
 
 ### 主要特性
 
-- **高易用性** MindCV将视觉任务分解为各种可配置的组件，用户可以轻松地构建自己的数据处理和模型训练流程。
+- **高易用性** MindSpore CV将视觉任务分解为各种可配置的组件，用户可以轻松地构建自己的数据处理和模型训练流程。
 
   ```pycon
   >>> import mindcv
@@ -48,13 +48,13 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
   python train.py --model swin_tiny --pretrained --opt=adamw --lr=0.001 --data_dir=/path/to/dataset
   ```
 
-- **高性能** MindCV集成了大量基于CNN和Transformer的高性能模型, 如SwinTransformer，并提供预训练权重、训练策略和性能报告，帮助用户快速选型并将其应用于视觉模型。
+- **高性能** MindSpore CV集成了大量基于CNN和Transformer的高性能模型, 如SwinTransformer，并提供预训练权重、训练策略和性能报告，帮助用户快速选型并将其应用于视觉模型。
 
-- **灵活高效** MindCV基于高效的深度学习框架MindSpore开发，具有自动并行和自动微分等特性，支持不同硬件平台上（CPU/GPU/Ascend），同时支持效率优化的静态图模式和调试灵活的动态图模式。
+- **灵活高效** MindSpore CV基于高效的深度学习框架MindSpore开发，具有自动并行和自动微分等特性，支持不同硬件平台上（CPU/GPU/Ascend），同时支持效率优化的静态图模式和调试灵活的动态图模式。
 
 ## 模型支持
 
-基于MindCV进行模型实现和重训练的汇总结果详见[模型仓库](https://mindspore-lab.github.io/mindcv/zh/modelzoo/), 所用到的训练策略和训练后的模型权重均可通过表中链接获取。
+基于MindSpore CV进行模型实现和重训练的汇总结果详见[模型仓库](https://mindspore-lab.github.io/mindcv/zh/modelzoo/), 所用到的训练策略和训练后的模型权重均可通过表中链接获取。
 
 各模型讲解和训练说明详见[configs](configs)目录。
 
@@ -66,7 +66,7 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
 
 ### 上手教程
 
-在开始上手MindCV前，可以阅读MindCV的[快速开始](docs/en/tutorials/quick_start.md)，该教程可以帮助用户快速了解MindCV的各个重要组件以及训练、验证、测试流程。
+在开始上手MindSpore CV前，可以阅读MindSpore CV的[快速开始](docs/en/tutorials/quick_start.md)，该教程可以帮助用户快速了解MindSpore CV的各个重要组件以及训练、验证、测试流程。
 
 以下是一些供您快速体验的代码样例。
 
@@ -149,7 +149,7 @@ python infer.py --model=swin_tiny --image_path='./dog.jpg'
   ```
 
   **预定义的训练策略**
-  MindCV目前提供了超过20种模型训练策略，在ImageNet取得SoTA性能。
+  MindSpore CV目前提供了超过20种模型训练策略，在ImageNet取得SoTA性能。
   具体的参数配置和详细精度性能汇总请见[`configs`](configs)文件夹。
   您可以便捷地将这些训练策略用于您的模型训练中以提高性能（复用或修改相应的yaml文件即可）。
 
@@ -203,7 +203,7 @@ python train.py --model=resnet50 --dataset=cifar10 \
 
 ## 教程
 
-我们提供了系列教程，帮助用户学习如何使用MindCV.
+我们提供了系列教程，帮助用户学习如何使用MindSpore CV.
 
 - [了解模型配置](docs/zh/tutorials/configuration.md)
 - [模型推理](docs/zh/tutorials/inference.md)
@@ -214,7 +214,7 @@ python train.py --model=resnet50 --dataset=cifar10 \
 
 ## 模型列表
 
-目前，MindCV支持以下模型。
+目前，MindSpore CV支持以下模型。
 
 <details open markdown>
 <summary> 支持模型 </summary>
@@ -425,7 +425,7 @@ python train.py --model=resnet50 --dataset=cifar10 \
 
 - 2023/01/10
 
-1. MindCV v0.1发布! 支持通过PyPI安装 (`pip install mindcv`)
+1. MindSpore CV v0.1发布! 支持通过PyPI安装 (`pip install mindcv`)
 2. 新增4个模型的预训练权重及其策略： googlenet, inception_v3, inception_v4, xception
 
 - 2022/12/09
@@ -457,7 +457,7 @@ python train.py --model=resnet50 --dataset=cifar10 \
 
 ## 贡献方式
 
-欢迎开发者用户提issue或提交代码PR，或贡献更多的算法和模型，一起让MindCV变得更好。
+欢迎开发者用户提issue或提交代码PR，或贡献更多的算法和模型，一起让MindSpore CV变得更好。
 
 有关贡献指南，请参阅[CONTRIBUTING.md](CONTRIBUTING.md)。
 请遵循[模型编写指南](docs/zh/how_to_guides/write_a_new_model.md)所规定的规则来贡献模型接口：)
@@ -468,13 +468,13 @@ python train.py --model=resnet50 --dataset=cifar10 \
 
 ## 致谢
 
-MindCV是由MindSpore团队、西安电子科技大学、西安交通大学联合开发的开源项目。
+MindSpore CV是由MindSpore团队、西安电子科技大学、西安交通大学联合开发的开源项目。
 衷心感谢所有参与的研究人员和开发人员为这个项目所付出的努力。
 十分感谢 [OpenI](https://openi.pcl.ac.cn/) 平台所提供的算力资源。
 
 ## 引用
 
-如果你觉得MindCV对你的项目有帮助，请考虑引用：
+如果你觉得MindSpore CV对你的项目有帮助，请考虑引用：
 
 ```latex
 @misc{MindSpore Computer Vision 2022,