mindspore-lab · Ash-Lee233 · Mar 4, 2025 · Feb 28, 2025 · Feb 28, 2025 · Mar 4, 2025
diff --git a/README.md b/README.md
@@ -46,6 +46,7 @@ mindspore versions.
 | mindocr |  mindspore  |
 |:-------:|:-----------:|
 |  main   |   master    |
+|   0.5   |    2.5.0    |
 |   0.4   | 2.3.0/2.3.1 |
 |   0.3   |   2.2.10    |
 |   0.1   |     1.8     |

diff --git a/README_CN.md b/README_CN.md
@@ -46,6 +46,7 @@ MindOCR是一个基于[MindSpore](https://www.mindspore.cn/) 框架开发的OCR
 | mindocr |  mindspore  |
 |:-------:|:-----------:|
 |  main   |   master    |
+|   0.5   |   2.5.0    |
 |   0.4   | 2.3.0/2.3.1 |
 |   0.3   |   2.2.10    |
 |   0.1   |     1.8     |

diff --git a/configs/cls/mobilenetv3/README.md b/configs/cls/mobilenetv3/README.md
@@ -31,25 +31,11 @@ Currently we support the 0 and 180 degree classification. You can update the par
 
 </div>
 
+## Requirements
 
-## Results
-
-| mindspore |  ascend driver  |   firmware   | cann toolkit/kernel |
-|:---------:|:---------------:|:------------:|:-------------------:|
-|   2.3.1   |    24.1.RC2     | 7.3.0.1.231  |    8.0.RC2.beta1    |
-
-MobileNetV3 is pretrained on ImageNet. For text direction classification task, we further train MobileNetV3 on RCTW17, MTWI and LSVT datasets.
-
-Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
-<div align="center">
-
-| **model name** | **cards** | **batch size** | **img/s** | **accuracy** | **config**  | **weight**                                                                            |
-|----------------|-----------|----------------|-----------|--------------|-----------------------------------------------------|------------------------------------------------|
-| MobileNetV3    | 4         | 256            | 5923.5    | 94.59%       | [yaml](cls_mv3.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/cls/cls_mobilenetv3-92db9c58.ckpt) |
-</div>
-
-
-
+| mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
+|:----------:|:--------------:|:--------------:|:-------------------:|
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 ## Quick Start
 
@@ -128,6 +114,18 @@ Please set the checkpoint path to the arg `ckpt_load_path` in the `eval` section
 python tools/eval.py -c configs/cls/mobilenetv3/cls_mv3.yaml
 ```
 
+## Performance
+
+MobileNetV3 is pretrained on ImageNet. For text direction classification task, we further train MobileNetV3 on RCTW17, MTWI and LSVT datasets.
+
+Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode
+<div align="center">
+
+| **model name** | **cards** | **batch size** | **img/s** | **accuracy** | **config**  | **weight**                                                                            |
+|----------------|-----------|----------------|-----------|--------------|-----------------------------------------------------|------------------------------------------------|
+| MobileNetV3    | 4         | 256            | 5923.5    | 94.59%       | [yaml](cls_mv3.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/cls/cls_mobilenetv3-92db9c58.ckpt) |
+</div>
+
 ## References
 
 <!--- Guideline: Citation format GB/T 7714 is suggested. -->

diff --git a/configs/cls/mobilenetv3/README_CN.md b/configs/cls/mobilenetv3/README_CN.md
@@ -31,22 +31,11 @@ MobileNetV3[[1](#参考文献)]于2019年发布，这个版本结合了V1的deep
 
 </div>
 
+### 配套版本
 
-## 实验结果
-
-| mindspore |  ascend driver  |   firmware   | cann toolkit/kernel |
-|:---------:|:---------------:|:------------:|:-------------------:|
-|   2.3.1   |    24.1.RC2     | 7.3.0.1.231  |    8.0.RC2.beta1    |
-
-MobileNetV3在ImageNet上预训练。另外，我们进一步在RCTW17、MTWI和LSVT数据集上进行了文字方向分类任务的训练。
-
-在采用图模式的ascend 910*上实验结果，mindspore版本为2.3.1
-<div align="center">
-
-| **模型名称**    | **卡数** | **单卡批量大小** | **img/s** | **准确率** | **配置**               | **权重**                                                                                   |
-|-------------|--------|------------|-----------|---------|----------------------|------------------------------------------------------------------------------------------|
-| MobileNetV3 | 4      | 256        | 5923.5    | 94.59%  | [yaml](cls_mv3.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/cls/cls_mobilenetv3-92db9c58.ckpt) |
-</div>
+| mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
+|:----------:|:--------------:|:--------------:|:-------------------:|
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 
 ## 快速上手
@@ -128,6 +117,18 @@ model:
 python tools/eval.py -c configs/cls/mobilenetv3/cls_mv3.yaml
 ```
 
+## 性能表现
+
+MobileNetV3在ImageNet上预训练。另外，我们进一步在RCTW17、MTWI和LSVT数据集上进行了文字方向分类任务的训练。
+
+在采用图模式的ascend 910*上实验结果，mindspore版本为2.5.0
+<div align="center">
+
+| **模型名称**    | **卡数** | **单卡批量大小** | **img/s** | **准确率** | **配置**               | **权重**                                                                                   |
+|-------------|--------|------------|-----------|---------|----------------------|------------------------------------------------------------------------------------------|
+| MobileNetV3 | 4      | 256        | 5923.5    | 94.59%  | [yaml](cls_mv3.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/cls/cls_mobilenetv3-92db9c58.ckpt) |
+</div>
+
 ## 参考文献
 
 <!--- Guideline: Citation format GB/T 7714 is suggested. -->

diff --git a/configs/det/dbnet/README.md b/configs/det/dbnet/README.md
@@ -56,13 +56,11 @@ combination of these two modules leads to scale-robust feature fusion.
 DBNet++ performs better in detecting text instances of diverse scales, especially for large-scale text instances where
 DBNet may generate inaccurate or discrete bounding boxes.
 
-
 ## Requirements
 
 | mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
 |:----------:|:--------------:|:--------------:|:-------------------:|
-|   2.3.1    |    24.1.RC2    |  7.3.0.1.231   |    8.0.RC2.beta1    |
-
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 ## Quick Start
 
@@ -290,7 +288,7 @@ msrun --worker_num=2 --local_worker_num=2 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.
 

diff --git a/configs/det/dbnet/README_CN.md b/configs/det/dbnet/README_CN.md
@@ -45,8 +45,7 @@ DBNet++在检测不同尺寸的文本方面表现更好，尤其是对于尺寸
 
 | mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
 |:----------:|:--------------:|:--------------:|:-------------------:|
-|   2.3.1    |    24.1.RC2    |  7.3.0.1.231   |    8.0.RC2.beta1    |
-
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 ## 快速上手
 
@@ -271,7 +270,7 @@ msrun --worker_num=2 --local_worker_num=2 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

diff --git a/configs/det/dbnet/README_CN_PP-OCRv3.md b/configs/det/dbnet/README_CN_PP-OCRv3.md
@@ -338,7 +338,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
 
 
 * 单卡训练

diff --git a/configs/det/east/README.md b/configs/det/east/README.md
@@ -29,6 +29,14 @@ EAST uses regression for the position and rotation angle of the text box, enabli
 4. **Text detection branch**:
 After determining the location and size of the text region, EAST further classifies these regions as text or non-text areas. For this purpose, a fully convolutional text branch is employed for binary classification of the text areas.
 
+
+## Requirements
+
+| mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
+|:----------:|:--------------:|:--------------:|:-------------------:|
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
+
+
 ## Quick Start
 
 ### Installation
@@ -128,7 +136,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.
 

diff --git a/configs/det/east/README_CN.md b/configs/det/east/README_CN.md
@@ -33,7 +33,7 @@ EAST的整体架构图如图1所示，包含以下阶段:
 
 | mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
 |:----------:|:--------------:|:--------------:|:-------------------:|
-|   2.3.1    |    24.1.RC2    |  7.3.0.1.231   |    8.0.RC2.beta1    |
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 ## 快速上手
 
@@ -132,7 +132,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

diff --git a/configs/det/psenet/README.md b/configs/det/psenet/README.md
@@ -25,7 +25,7 @@ The overall architecture of PSENet is presented in Figure 1. It consists of mult
 
 | mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
 |:----------:|:--------------:|:--------------:|:-------------------:|
-|   2.3.1    |    24.1.RC2    |  7.3.0.1.231   |    8.0.RC2.beta1    |
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 ## Quick Start
 
@@ -156,7 +156,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/tutorials/experts/en/r2.3.1/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
 
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.

diff --git a/configs/det/psenet/README_CN.md b/configs/det/psenet/README_CN.md
@@ -25,7 +25,7 @@ PSENet的整体架构图如图1所示，包含以下阶段:
 
 | mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
 |:----------:|:--------------:|:--------------:|:-------------------:|
-|   2.3.1    |    24.1.RC2    |  7.3.0.1.231   |    8.0.RC2.beta1    |
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 ## 快速上手
 
@@ -155,7 +155,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/tutorials/experts/zh-CN/r2.3.1/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

diff --git a/configs/kie/vi_layoutxlm/README.md b/configs/kie/vi_layoutxlm/README.md
@@ -40,43 +40,20 @@ After obtaining αij from the original self-attention layer, considering the lar
   <em> Figure 1. LayoutXLM(LayoutLMv2) architecture [<a href="#References">1</a>] </em>
 </p>
 
-## Results
-<!--- Guideline:
-Table Format:
-- Model: model name in lower case with _ seperator.
-- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
-- Top-1 and Top-5: Keep 2 digits after the decimal point.
-- Params (M): # of model parameters in millions (10^6). Keep 2 digits after the decimal point
-- Recipe: Training recipe/configuration linked to a yaml config file. Use absolute url path.
-- Download: url of the pretrained model weights. Use absolute url path.
--->
-
-### Accuracy
-
-| mindspore |  ascend driver  |   firmware   | cann toolkit/kernel |
-|:---------:|:---------------:|:------------:|:-------------------:|
-|   2.3.1   |    24.1.RC2     | 7.3.0.1.231  |    8.0.RC2.beta1    |
-
-According to our experiments, the performance and accuracy evaluation（[Model Evaluation](#33-Model-Evaluation)） results of training ([Model Training](#32-Model-Training)) on the XFUND Chinese dataset are as follows:
-
-
-Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
-<div align="center">
-
-| **model name** | **cards** | **batch size** | **img/s** | **hmean** | **config**  | **weight**                                                                            |
-|----------------|-----------|----------------|-----------|-----------|-----------------------------------------------------|------------------------------------------------|
-| LayoutXLM    | 1         | 8              | 73.26     | 90.34%    | [yaml](../layoutxlm/ser_layoutxlm_xfund_zh.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/layoutxlm/ser_layoutxlm_base-a4ea148e.ckpt)  |
-| VI-LayoutXLM    | 1         | 8              | 110.6     | 93.31%    | [yaml](../layoutxlm/ser_layoutxlm_xfund_zh.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/layoutxlm/ser_layoutxlm_base-a4ea148e.ckpt)  |
-</div>
-
+## Requirements
 
+| mindspore  | ascend driver  |    firmware    | cann toolkit/kernel |
+|:----------:|:--------------:|:--------------:|:-------------------:|
+|   2.5.0    |    24.1.0      |   7.5.0.3.220  |     8.0.0.beta1     |
 
 ## Quick Start
-### Preparation
 
-#### Installation
+### Installation
+
 Please refer to the [installation instruction](https://github.com/mindspore-lab/mindocr#installation) in MindOCR.
 
+### Dataset preparation
+
 #### Dataset Download
 
 [The XFUND dataset](https://github.com/doc-analysis/XFUND) is used as the experimental dataset. The XFUND dataset is a multilingual dataset proposed by Microsoft for the Knowledge-Intensive Extraction (KIE) task. It consists of seven datasets, each containing 149 training samples and 50 validation samples.
@@ -168,7 +145,18 @@ Recognition results are as shown in the image, and the image is saved as`inferen
   <em> example_ser.jpg </em>
 </p>
 
+## Performance
+
+According to our experiments, the performance of evaluation on the XFUND Chinese dataset are as follows:
+
+Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode
+<div align="center">
 
+| **model name** | **cards** | **batch size** | **img/s** | **hmean** | **config**  | **weight**                                                                            |
+|----------------|-----------|----------------|-----------|-----------|-----------------------------------------------------|------------------------------------------------|
+| LayoutXLM    | 1         | 8              | 73.26     | 90.34%    | [yaml](../layoutxlm/ser_layoutxlm_xfund_zh.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/layoutxlm/ser_layoutxlm_base-a4ea148e.ckpt)  |
+| VI-LayoutXLM    | 1         | 8              | 110.6     | 93.31%    | [yaml](../layoutxlm/ser_layoutxlm_xfund_zh.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/layoutxlm/ser_layoutxlm_base-a4ea148e.ckpt)  |
+</div>
 
 ## References
 <!--- Guideline: Citation format GB/T 7714 is suggested. -->