diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 0c9bbc0bb..16463708d 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -18,14 +18,12 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.8"
+        python-version: "3.9"
 
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements/dev.txt
-        pip install "Pillow==9.1.1"
-        pip install "mindspore>=1.8,<=1.10"
         pip install build twine
     - name: Build package
       run: |
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index ccd7526d2..fa2d01522 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -17,9 +17,9 @@ Report bugs at https://github.com/mindspore-lab/mindcv/issues.
 
 If you are reporting a bug, please include:
 
-* Your operating system name and version.
-* Any details about your local setup that might be helpful in troubleshooting.
-* Detailed steps to reproduce the bug.
+- Your operating system name and version.
+- Any details about your local setup that might be helpful in troubleshooting.
+- Detailed steps to reproduce the bug.
 
 ### Fix Bugs
 
@@ -43,9 +43,9 @@ The best way to send feedback is to file an issue at https://github.com/mindspor
 
 If you are proposing a feature:
 
-* Explain in detail how it would work.
-* Keep the scope as narrow as possible, to make it easier to implement.
-* Remember that this is a volunteer-driven project, and that contributions are welcome :)
+- Explain in detail how it would work.
+- Keep the scope as narrow as possible, to make it easier to implement.
+- Remember that this is a volunteer-driven project, and that contributions are welcome :)
 
 ## Getting Started
 
diff --git a/LICENSE.md b/LICENSE.md
index 261eeb9e9..c61b66391 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -2,180 +2,180 @@
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
 
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
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-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
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-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1.  Definitions.
+
+    "License" shall mean the terms and conditions for use, reproduction,
+    and distribution as defined by Sections 1 through 9 of this document.
+
+    "Licensor" shall mean the copyright owner or entity authorized by
+    the copyright owner that is granting the License.
+
+    "Legal Entity" shall mean the union of the acting entity and all
+    other entities that control, are controlled by, or are under common
+    control with that entity. For the purposes of this definition,
+    "control" means (i) the power, direct or indirect, to cause the
+    direction or management of such entity, whether by contract or
+    otherwise, or (ii) ownership of fifty percent (50%) or more of the
+    outstanding shares, or (iii) beneficial ownership of such entity.
+
+    "You" (or "Your") shall mean an individual or Legal Entity
+    exercising permissions granted by this License.
+
+    "Source" form shall mean the preferred form for making modifications,
+    including but not limited to software source code, documentation
+    source, and configuration files.
+
+    "Object" form shall mean any form resulting from mechanical
+    transformation or translation of a Source form, including but
+    not limited to compiled object code, generated documentation,
+    and conversions to other media types.
+
+    "Work" shall mean the work of authorship, whether in Source or
+    Object form, made available under the License, as indicated by a
+    copyright notice that is included in or attached to the work
+    (an example is provided in the Appendix below).
+
+    "Derivative Works" shall mean any work, whether in Source or Object
+    form, that is based on (or derived from) the Work and for which the
+    editorial revisions, annotations, elaborations, or other modifications
+    represent, as a whole, an original work of authorship. For the purposes
+    of this License, Derivative Works shall not include works that remain
+    separable from, or merely link (or bind by name) to the interfaces of,
+    the Work and Derivative Works thereof.
+
+    "Contribution" shall mean any work of authorship, including
+    the original version of the Work and any modifications or additions
+    to that Work or Derivative Works thereof, that is intentionally
+    submitted to Licensor for inclusion in the Work by the copyright owner
+    or by an individual or Legal Entity authorized to submit on behalf of
+    the copyright owner. For the purposes of this definition, "submitted"
+    means any form of electronic, verbal, or written communication sent
+    to the Licensor or its representatives, including but not limited to
+    communication on electronic mailing lists, source code control systems,
+    and issue tracking systems that are managed by, or on behalf of, the
+    Licensor for the purpose of discussing and improving the Work, but
+    excluding communication that is conspicuously marked or otherwise
+    designated in writing by the copyright owner as "Not a Contribution."
+
+    "Contributor" shall mean Licensor and any individual or Legal Entity
+    on behalf of whom a Contribution has been received by Licensor and
+    subsequently incorporated within the Work.
+
+2.  Grant of Copyright License. Subject to the terms and conditions of
+    this License, each Contributor hereby grants to You a perpetual,
+    worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+    copyright license to reproduce, prepare Derivative Works of,
+    publicly display, publicly perform, sublicense, and distribute the
+    Work and such Derivative Works in Source or Object form.
+
+3.  Grant of Patent License. Subject to the terms and conditions of
+    this License, each Contributor hereby grants to You a perpetual,
+    worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+    (except as stated in this section) patent license to make, have made,
+    use, offer to sell, sell, import, and otherwise transfer the Work,
+    where such license applies only to those patent claims licensable
+    by such Contributor that are necessarily infringed by their
+    Contribution(s) alone or by combination of their Contribution(s)
+    with the Work to which such Contribution(s) was submitted. If You
+    institute patent litigation against any entity (including a
+    cross-claim or counterclaim in a lawsuit) alleging that the Work
+    or a Contribution incorporated within the Work constitutes direct
+    or contributory patent infringement, then any patent licenses
+    granted to You under this License for that Work shall terminate
+    as of the date such litigation is filed.
+
+4.  Redistribution. You may reproduce and distribute copies of the
+    Work or Derivative Works thereof in any medium, with or without
+    modifications, and in Source or Object form, provided that You
+    meet the following conditions:
+
+    (a) You must give any other recipients of the Work or
+    Derivative Works a copy of this License; and
+
+    (b) You must cause any modified files to carry prominent notices
+    stating that You changed the files; and
+
+    (c) You must retain, in the Source form of any Derivative Works
+    that You distribute, all copyright, patent, trademark, and
+    attribution notices from the Source form of the Work,
+    excluding those notices that do not pertain to any part of
+    the Derivative Works; and
+
+    (d) If the Work includes a "NOTICE" text file as part of its
+    distribution, then any Derivative Works that You distribute must
+    include a readable copy of the attribution notices contained
+    within such NOTICE file, excluding those notices that do not
+    pertain to any part of the Derivative Works, in at least one
+    of the following places: within a NOTICE text file distributed
+    as part of the Derivative Works; within the Source form or
+    documentation, if provided along with the Derivative Works; or,
+    within a display generated by the Derivative Works, if and
+    wherever such third-party notices normally appear. The contents
+    of the NOTICE file are for informational purposes only and
+    do not modify the License. You may add Your own attribution
+    notices within Derivative Works that You distribute, alongside
+    or as an addendum to the NOTICE text from the Work, provided
+    that such additional attribution notices cannot be construed
+    as modifying the License.
+
+    You may add Your own copyright statement to Your modifications and
+    may provide additional or different license terms and conditions
+    for use, reproduction, or distribution of Your modifications, or
+    for any such Derivative Works as a whole, provided Your use,
+    reproduction, and distribution of the Work otherwise complies with
+    the conditions stated in this License.
+
+5.  Submission of Contributions. Unless You explicitly state otherwise,
+    any Contribution intentionally submitted for inclusion in the Work
+    by You to the Licensor shall be under the terms and conditions of
+    this License, without any additional terms or conditions.
+    Notwithstanding the above, nothing herein shall supersede or modify
+    the terms of any separate license agreement you may have executed
+    with Licensor regarding such Contributions.
+
+6.  Trademarks. This License does not grant permission to use the trade
+    names, trademarks, service marks, or product names of the Licensor,
+    except as required for reasonable and customary use in describing the
+    origin of the Work and reproducing the content of the NOTICE file.
+
+7.  Disclaimer of Warranty. Unless required by applicable law or
+    agreed to in writing, Licensor provides the Work (and each
+    Contributor provides its Contributions) on an "AS IS" BASIS,
+    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+    implied, including, without limitation, any warranties or conditions
+    of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+    PARTICULAR PURPOSE. You are solely responsible for determining the
+    appropriateness of using or redistributing the Work and assume any
+    risks associated with Your exercise of permissions under this License.
+
+8.  Limitation of Liability. In no event and under no legal theory,
+    whether in tort (including negligence), contract, or otherwise,
+    unless required by applicable law (such as deliberate and grossly
+    negligent acts) or agreed to in writing, shall any Contributor be
+    liable to You for damages, including any direct, indirect, special,
+    incidental, or consequential damages of any character arising as a
+    result of this License or out of the use or inability to use the
+    Work (including but not limited to damages for loss of goodwill,
+    work stoppage, computer failure or malfunction, or any and all
+    other commercial damages or losses), even if such Contributor
+    has been advised of the possibility of such damages.
+
+9.  Accepting Warranty or Additional Liability. While redistributing
+    the Work or Derivative Works thereof, You may choose to offer,
+    and charge a fee for, acceptance of support, warranty, indemnity,
+    or other liability obligations and/or rights consistent with this
+    License. However, in accepting such obligations, You may act only
+    on Your own behalf and on Your sole responsibility, not on behalf
+    of any other Contributor, and only if You agree to indemnify,
+    defend, and hold each Contributor harmless for any liability
+    incurred by, or claims asserted against, such Contributor by reason
+    of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
+
+APPENDIX: How to apply the Apache License to your work.
 
       To apply the Apache License to your work, attach the following
       boilerplate notice, with the fields enclosed by brackets "[]"
@@ -186,16 +186,16 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright [yyyy] [name of copyright owner]
+Copyright [yyyy] [name of copyright owner]
 
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
 
        http://www.apache.org/licenses/LICENSE-2.0
 
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
diff --git a/README.md b/README.md
index f4a9cdca2..4f860811d 100644
--- a/README.md
+++ b/README.md
@@ -30,33 +30,32 @@ MindCV is an open-source toolbox for computer vision research and development ba
 The following is the corresponding `mindcv` versions and supported `mindspore` versions.
 
 | mindcv |  mindspore  |
-|:------:|:-----------:|
+| :----: | :---------: |
 |  main  |   master    |
-|  0.5   |   2.5.0     |
+|  0.5   |    2.5.0    |
 |  0.4   | 2.3.0/2.3.1 |
 |  0.3   |   2.2.10    |
 |  0.2   |     2.0     |
 |  0.1   |     1.8     |
 
-
 ### 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:
 
-    ```pycon
-    >>> import mindcv
-    # create a dataset
-    >>> dataset = mindcv.create_dataset('cifar10', download=True)
-    # create a model
-    >>> network = mindcv.create_model('resnet50', pretrained=True)
-    ```
+  ```pycon
+  >>> import mindcv
+  # create a dataset
+  >>> dataset = mindcv.create_dataset('cifar10', download=True)
+  # create a model
+  >>> network = mindcv.create_model('resnet50', pretrained=True)
+  ```
 
-    Users can customize and launch their transfer learning or training task in one command line.
+  Users can customize and launch their transfer learning or training task in one command line.
 
-    ```shell
-    # transfer learning in one command line
-    python train.py --model=swin_tiny --pretrained --opt=adamw --lr=0.001 --data_dir=/path/to/data
-    ```
+  ```shell
+  # transfer learning in one command line
+  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:
 
@@ -88,6 +87,7 @@ Below are a few code snippets for your taste.
 # Create the model object
 >>> network = mindcv.create_model('swin_tiny', pretrained=True)
 ```
+
 ```shell
 # Validate its accuracy
 python validate.py --model=swin_tiny --pretrained --dataset=imagenet --val_split=validation
@@ -108,6 +108,7 @@ Classify the downloaded image with a pretrained SoTA model:
 python infer.py --model=swin_tiny --image_path='./dog.jpg'
 # {'Labrador retriever': 0.5700152, 'golden retriever': 0.034551315, 'kelpie': 0.010108651, 'Chesapeake Bay retriever': 0.008229004, 'Walker hound, Walker foxhound': 0.007791956}
 ```
+
 The top-1 prediction result is labrador retriever, which is the breed of this cut dog.
 
 ### Training
@@ -116,61 +117,61 @@ It is easy to train your model on a standard or customized dataset using `train.
 
 - Standalone Training
 
-    ```shell
-    # standalone training
-    python train.py --model=resnet50 --dataset=cifar10 --dataset_download
-    ```
+  ```shell
+  # standalone training
+  python train.py --model=resnet50 --dataset=cifar10 --dataset_download
+  ```
 
-    Above is an example for training ResNet50 on CIFAR10 dataset on a CPU/GPU/Ascend device
+  Above is an example for training ResNet50 on CIFAR10 dataset on a CPU/GPU/Ascend device
 
 - Distributed Training
 
-    For large datasets like ImageNet, it is necessary to do training in distributed mode on multiple devices. This can be achieved with `msrun` and parallel features supported by MindSpore.
+  For large datasets like ImageNet, it is necessary to do training in distributed mode on multiple devices. This can be achieved with `msrun` and parallel features supported by MindSpore.
 
-    ```shell
-    # distributed training
-    # assume you have 4 NPUs
-    msrun --bind_core=True --worker_num 4 python train.py --distribute \
-        --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet
-    ```
+  ```shell
+  # distributed training
+  # assume you have 4 NPUs
+  msrun --bind_core=True --worker_num 4 python train.py --distribute \
+      --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet
+  ```
 
-    Notice that if you are using msrun startup with 2 devices, please add `--bind_core=True` to improve performance. For example:
+  Notice that if you are using msrun startup with 2 devices, please add `--bind_core=True` to improve performance. For example:
 
-    ```shell
-    msrun --bind_core=True --worker_num=2--local_worker_num=2 --master_port=8118 \
-    --log_dir=msrun_log --join=True --cluster_time_out=300 \
-    python train.py --distribute --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet
-    ```
+  ```shell
+  msrun --bind_core=True --worker_num=2--local_worker_num=2 --master_port=8118 \
+  --log_dir=msrun_log --join=True --cluster_time_out=300 \
+  python train.py --distribute --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet
+  ```
 
-   > For more information, please refer to https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/startup_method.html
+  > For more information, please refer to https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/startup_method.html
 
-    Detailed parameter definitions can be seen in `config.py` and checked by running `python train.py --help'.
+  Detailed parameter definitions can be seen in `config.py` and checked by running `python train.py --help'.
 
-    To resume training, please set the `--ckpt_path` and `--ckpt_save_dir` arguments. The optimizer state including the learning rate of the last stopped epoch will also be recovered.
+  To resume training, please set the `--ckpt_path` and `--ckpt_save_dir` arguments. The optimizer state including the learning rate of the last stopped epoch will also be recovered.
 
 - Config and Training Strategy
 
-    You can configure your model and other components either by specifying external parameters or by writing a yaml config file. Here is an example of training using a preset yaml file.
+  You can configure your model and other components either by specifying external parameters or by writing a yaml config file. Here is an example of training using a preset yaml file.
 
-    ```shell
-    msrun --bind_core=True --worker_num 4 python train.py -c configs/squeezenet/squeezenet_1.0_ascend.yaml
-    ```
+  ```shell
+  msrun --bind_core=True --worker_num 4 python train.py -c configs/squeezenet/squeezenet_1.0_ascend.yaml
+  ```
 
-    **Pre-defined Training Strategies:**
-    We provide more than 20 training recipes that achieve SoTA results on ImageNet currently.
-    Please look into the [`configs`](configs) folder for details.
-    Please feel free to adapt these training strategies to your own model for performance improvement, which can be easily done by modifying the yaml file.
+  **Pre-defined Training Strategies:**
+  We provide more than 20 training recipes that achieve SoTA results on ImageNet currently.
+  Please look into the [`configs`](configs) folder for details.
+  Please feel free to adapt these training strategies to your own model for performance improvement, which can be easily done by modifying the yaml file.
 
 - Train on ModelArts/OpenI Platform
 
-    To run training on the [ModelArts](https://www.huaweicloud.com/intl/en-us/product/modelarts.html) or [OpenI](https://openi.pcl.ac.cn/) cloud platform:
+  To run training on the [ModelArts](https://www.huaweicloud.com/intl/en-us/product/modelarts.html) or [OpenI](https://openi.pcl.ac.cn/) cloud platform:
 
-    ```text
-    1. Create a new training task on the cloud platform.
-    2. Add run parameter `config` and specify the path to the yaml config file on the website UI interface.
-    3. Add run parameter `enable_modelarts` and set True on the website UI interface.
-    4. Fill in other blanks on the website and launch the training task.
-    ```
+  ```text
+  1. Create a new training task on the cloud platform.
+  2. Add run parameter `config` and specify the path to the yaml config file on the website UI interface.
+  3. Add run parameter `enable_modelarts` and set True on the website UI interface.
+  4. Fill in other blanks on the website and launch the training task.
+  ```
 
 **Graph Mode and PyNative Mode**:
 
@@ -226,41 +227,41 @@ Currently, MindCV supports the model families listed below. More models with pre
 <details open markdown>
 <summary> Supported models </summary>
 
-* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
-* ConvNeXt - https://arxiv.org/abs/2201.03545
-* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
-* DenseNet - https://arxiv.org/abs/1608.06993
-* DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
-* EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
-* EfficientNet V2 - https://arxiv.org/abs/2104.00298
-* GhostNet - https://arxiv.org/abs/1911.11907
-* GoogLeNet - https://arxiv.org/abs/1409.4842
-* Inception-V3 - https://arxiv.org/abs/1512.00567
-* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
-* MNASNet - https://arxiv.org/abs/1807.11626
-* MobileNet-V1 - https://arxiv.org/abs/1704.04861
-* MobileNet-V2 - https://arxiv.org/abs/1801.04381
-* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
-* NASNet - https://arxiv.org/abs/1707.07012
-* PNasNet - https://arxiv.org/abs/1712.00559
-* PVT (Pyramid Vision Transformer) - https://arxiv.org/abs/2102.12122
-* PoolFormer models - https://github.com/sail-sg/poolformer
-* RegNet - https://arxiv.org/abs/2003.13678
-* RepMLP https://arxiv.org/abs/2105.01883
-* RepVGG - https://arxiv.org/abs/2101.03697
-* ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
-* ResNeXt - https://arxiv.org/abs/1611.05431
-* Res2Net - https://arxiv.org/abs/1904.01169
-* ReXNet - https://arxiv.org/abs/2007.00992
-* ShuffleNet v1 - https://arxiv.org/abs/1707.01083
-* ShuffleNet v2 - https://arxiv.org/abs/1807.11164
-* SKNet - https://arxiv.org/abs/1903.06586
-* SqueezeNet - https://arxiv.org/abs/1602.07360
-* Swin Transformer - https://arxiv.org/abs/2103.14030
-* VGG - https://arxiv.org/abs/1409.1556
-* Visformer - https://arxiv.org/abs/2104.12533
-* Vision Transformer (ViT) - https://arxiv.org/abs/2010.11929
-* Xception - https://arxiv.org/abs/1610.02357
+- Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
+- ConvNeXt - https://arxiv.org/abs/2201.03545
+- ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
+- DenseNet - https://arxiv.org/abs/1608.06993
+- DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
+- EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
+- EfficientNet V2 - https://arxiv.org/abs/2104.00298
+- GhostNet - https://arxiv.org/abs/1911.11907
+- GoogLeNet - https://arxiv.org/abs/1409.4842
+- Inception-V3 - https://arxiv.org/abs/1512.00567
+- Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
+- MNASNet - https://arxiv.org/abs/1807.11626
+- MobileNet-V1 - https://arxiv.org/abs/1704.04861
+- MobileNet-V2 - https://arxiv.org/abs/1801.04381
+- MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
+- NASNet - https://arxiv.org/abs/1707.07012
+- PNasNet - https://arxiv.org/abs/1712.00559
+- PVT (Pyramid Vision Transformer) - https://arxiv.org/abs/2102.12122
+- PoolFormer models - https://github.com/sail-sg/poolformer
+- RegNet - https://arxiv.org/abs/2003.13678
+- RepMLP https://arxiv.org/abs/2105.01883
+- RepVGG - https://arxiv.org/abs/2101.03697
+- ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
+- ResNeXt - https://arxiv.org/abs/1611.05431
+- Res2Net - https://arxiv.org/abs/1904.01169
+- ReXNet - https://arxiv.org/abs/2007.00992
+- ShuffleNet v1 - https://arxiv.org/abs/1707.01083
+- ShuffleNet v2 - https://arxiv.org/abs/1807.11164
+- SKNet - https://arxiv.org/abs/1903.06586
+- SqueezeNet - https://arxiv.org/abs/1602.07360
+- Swin Transformer - https://arxiv.org/abs/2103.14030
+- VGG - https://arxiv.org/abs/1409.1556
+- Visformer - https://arxiv.org/abs/2104.12533
+- Vision Transformer (ViT) - https://arxiv.org/abs/2010.11929
+- Xception - https://arxiv.org/abs/1610.02357
 
 Please see [configs](./configs) for the details about model performance and pretrained weights.
 
@@ -271,43 +272,43 @@ Please see [configs](./configs) for the details about model performance and pret
 <details open markdown>
 <summary> Supported algorithms </summary>
 
-* Augmentation
-    * [AutoAugment](https://arxiv.org/abs/1805.09501)
-    * [RandAugment](https://arxiv.org/abs/1909.13719)
-    * [Repeated Augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf)
-    * RandErasing (Cutout)
-    * CutMix
-    * MixUp
-    * RandomResizeCrop
-    * Color Jitter, Flip, etc
-* Optimizer
-    * Adam
-    * AdamW
-    * [Lion](https://arxiv.org/abs/2302.06675)
-    * Adan (experimental)
-    * AdaGrad
-    * LAMB
-    * Momentum
-    * RMSProp
-    * SGD
-    * NAdam
-* LR Scheduler
-    * Warmup Cosine Decay
-    * Step LR
-    * Polynomial Decay
-    * Exponential Decay
-* Regularization
-    * Weight Decay
-    * Label Smoothing
-    * Stochastic Depth (depends on networks)
-    * Dropout (depends on networks)
-* Loss
-    * Cross Entropy (w/ class weight and auxiliary logit support)
-    * Binary Cross Entropy  (w/ class weight and auxiliary logit support)
-    * Soft Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
-    * Soft Binary Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
-* Ensemble
-    * Warmup EMA (Exponential Moving Average)
+- Augmentation
+  - [AutoAugment](https://arxiv.org/abs/1805.09501)
+  - [RandAugment](https://arxiv.org/abs/1909.13719)
+  - [Repeated Augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf)
+  - RandErasing (Cutout)
+  - CutMix
+  - MixUp
+  - RandomResizeCrop
+  - Color Jitter, Flip, etc
+- Optimizer
+  - Adam
+  - AdamW
+  - [Lion](https://arxiv.org/abs/2302.06675)
+  - Adan (experimental)
+  - AdaGrad
+  - LAMB
+  - Momentum
+  - RMSProp
+  - SGD
+  - NAdam
+- LR Scheduler
+  - Warmup Cosine Decay
+  - Step LR
+  - Polynomial Decay
+  - Exponential Decay
+- Regularization
+  - Weight Decay
+  - Label Smoothing
+  - Stochastic Depth (depends on networks)
+  - Dropout (depends on networks)
+- Loss
+  - Cross Entropy (w/ class weight and auxiliary logit support)
+  - Binary Cross Entropy (w/ class weight and auxiliary logit support)
+  - Soft Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
+  - Soft Binary Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
+- Ensemble
+  - Warmup EMA (Exponential Moving Average)
 
 </details>
 
@@ -342,9 +343,9 @@ Release `0.3.0` is published. We will drop MindSpore 1.x in the future release.
 6. BREAKING CHANGES:
    - We are going to drop support of MindSpore 1.x for it's EOL.
    - Configuration `filter_bias_and_bn` will be removed and renamed as `weight_decay_filter`,
-   due to a prolonged misunderstanding of the MindSpore optimizer.
-   We will migrate the existing training recipes, but the signature change of function `create_optimizer` will be incompatible
-   and the old version training recipes will also be incompatible. See [PR/752](https://github.com/mindspore-lab/mindcv/pull/752) for details.
+     due to a prolonged misunderstanding of the MindSpore optimizer.
+     We will migrate the existing training recipes, but the signature change of function `create_optimizer` will be incompatible
+     and the old version training recipes will also be incompatible. See [PR/752](https://github.com/mindspore-lab/mindcv/pull/752) for details.
 
 See [RELEASE](RELEASE.md) for detailed history.
 
diff --git a/README_CN.md b/README_CN.md
index 1347e652a..03a27a10e 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -33,20 +33,20 @@ MindCV是一个基于 [MindSpore](https://www.mindspore.cn/) 开发的，致力
 
 - **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。
 
-    ```pycon
-    >>> import mindcv
-    # 创建数据集
-    >>> dataset = mindcv.create_dataset('cifar10', download=True)
-    # 创建模型
-    >>> network = mindcv.create_model('resnet50', pretrained=True)
-    ```
+  ```pycon
+  >>> import mindcv
+  # 创建数据集
+  >>> dataset = mindcv.create_dataset('cifar10', download=True)
+  # 创建模型
+  >>> network = mindcv.create_model('resnet50', pretrained=True)
+  ```
 
-    用户可通过预定义的训练和微调脚本，快速配置并完成训练或迁移学习任务。
+  用户可通过预定义的训练和微调脚本，快速配置并完成训练或迁移学习任务。
 
-    ```shell
-    # 配置和启动迁移学习任务
-    python train.py --model swin_tiny --pretrained --opt=adamw --lr=0.001 --data_dir=/path/to/dataset
-    ```
+  ```shell
+  # 配置和启动迁移学习任务
+  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。
 
@@ -108,61 +108,61 @@ python infer.py --model=swin_tiny --image_path='./dog.jpg'
 
 - 单卡训练
 
-    ```shell
-    # 单卡训练
-    python train.py --model resnet50 --dataset cifar10 --dataset_download
-    ```
+  ```shell
+  # 单卡训练
+  python train.py --model resnet50 --dataset cifar10 --dataset_download
+  ```
 
-    以上代码是在CIFAR10数据集上单卡（CPU/GPU/Ascend）训练ResNet的示例，通过`model`和`dataset`参数分别指定需要训练的模型和数据集。
+  以上代码是在CIFAR10数据集上单卡（CPU/GPU/Ascend）训练ResNet的示例，通过`model`和`dataset`参数分别指定需要训练的模型和数据集。
 
 - 分布式训练
 
-    对于像ImageNet这样的大型数据集，有必要在多个设备上以分布式模式进行训练。基于MindSpore对分布式相关功能的良好支持，用户可以使用`msrun`来进行模型的分布式训练。
+  对于像ImageNet这样的大型数据集，有必要在多个设备上以分布式模式进行训练。基于MindSpore对分布式相关功能的良好支持，用户可以使用`msrun`来进行模型的分布式训练。
 
-    ```shell
-    # 分布式训练
-    # 假设你有4张NPU卡
-    msrun --bind_core=True --worker_num 4 python train.py --distribute \
-        --model densenet121 --dataset imagenet --data_dir ./datasets/imagenet
-    ```
+  ```shell
+  # 分布式训练
+  # 假设你有4张NPU卡
+  msrun --bind_core=True --worker_num 4 python train.py --distribute \
+      --model densenet121 --dataset imagenet --data_dir ./datasets/imagenet
+  ```
 
-    注意，如果在两卡环境下选用msrun作为启动方式，请添加配置项 `--bind_core=True` 增加绑核操作以优化两卡性能，范例代码如下：
+  注意，如果在两卡环境下选用msrun作为启动方式，请添加配置项 `--bind_core=True` 增加绑核操作以优化两卡性能，范例代码如下：
 
-    ```shell
-    msrun --bind_core=True --worker_num=2--local_worker_num=2 --master_port=8118 \
-    --log_dir=msrun_log --join=True --cluster_time_out=300 \
-    python train.py --distribute --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet
-    ```
+  ```shell
+  msrun --bind_core=True --worker_num=2--local_worker_num=2 --master_port=8118 \
+  --log_dir=msrun_log --join=True --cluster_time_out=300 \
+  python train.py --distribute --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet
+  ```
 
-   > 如需更多操作指导，请参考 https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/startup_method.html
+  > 如需更多操作指导，请参考 https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/startup_method.html
 
-    完整的参数列表及说明在`config.py`中定义，可运行`python train.py --help`快速查看。
+  完整的参数列表及说明在`config.py`中定义，可运行`python train.py --help`快速查看。
 
-    如需恢复训练，请指定`--ckpt_path`和`--ckpt_save_dir`参数，脚本将加载路径中的模型权重和优化器状态，并恢复中断的训练进程。
+  如需恢复训练，请指定`--ckpt_path`和`--ckpt_save_dir`参数，脚本将加载路径中的模型权重和优化器状态，并恢复中断的训练进程。
 
 - 超参配置和预训练策略
 
-    您可以编写yaml文件或设置外部参数来指定配置数据、模型、优化器等组件及其超参数。以下是使用预设的训练策略（yaml文件）进行模型训练的示例。
+  您可以编写yaml文件或设置外部参数来指定配置数据、模型、优化器等组件及其超参数。以下是使用预设的训练策略（yaml文件）进行模型训练的示例。
 
-    ```shell
-    msrun --bind_core=True --worker_num 4 python train.py -c configs/squeezenet/squeezenet_1.0_ascend.yaml
-    ```
+  ```shell
+  msrun --bind_core=True --worker_num 4 python train.py -c configs/squeezenet/squeezenet_1.0_ascend.yaml
+  ```
 
-    **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。
-    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）。
+  **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。
+  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）。
 
 - 在ModelArts/OpenI平台上训练
 
-    在[ModelArts](https://www.huaweicloud.com/intl/en-us/product/modelarts.html)或[OpenI](https://openi.pcl.ac.cn/)云平台上进行训练，需要执行以下操作：
+  在[ModelArts](https://www.huaweicloud.com/intl/en-us/product/modelarts.html)或[OpenI](https://openi.pcl.ac.cn/)云平台上进行训练，需要执行以下操作：
 
-    ```text
-    1、在云平台上创建新的训练任务。
-    2、在网站UI界面添加运行参数`config`，并指定yaml配置文件的路径。
-    3、在网站UI界面添加运行参数`enable_modelarts`并设置为True。
-    4、在网站上填写其他训练信息并启动训练任务。
-    ```
+  ```text
+  1、在云平台上创建新的训练任务。
+  2、在网站UI界面添加运行参数`config`，并指定yaml配置文件的路径。
+  3、在网站UI界面添加运行参数`enable_modelarts`并设置为True。
+  4、在网站上填写其他训练信息并启动训练任务。
+  ```
 
 **静态图和动态图模式**
 
@@ -219,41 +219,41 @@ python train.py --model=resnet50 --dataset=cifar10 \
 <details open markdown>
 <summary> 支持模型 </summary>
 
-* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
-* ConvNeXt - https://arxiv.org/abs/2201.03545
-* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
-* DenseNet - https://arxiv.org/abs/1608.06993
-* DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
-* EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
-* EfficientNet V2 - https://arxiv.org/abs/2104.00298
-* GhostNet - https://arxiv.org/abs/1911.11907
-* GoogLeNet - https://arxiv.org/abs/1409.4842
-* Inception-V3 - https://arxiv.org/abs/1512.00567
-* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
-* MNASNet - https://arxiv.org/abs/1807.11626
-* MobileNet-V1 - https://arxiv.org/abs/1704.04861
-* MobileNet-V2 - https://arxiv.org/abs/1801.04381
-* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
-* NASNet - https://arxiv.org/abs/1707.07012
-* PNasNet - https://arxiv.org/abs/1712.00559
-* PVT (Pyramid Vision Transformer) - https://arxiv.org/abs/2102.12122
-* PoolFormer models - https://github.com/sail-sg/poolformer
-* RegNet - https://arxiv.org/abs/2003.13678
-* RepMLP https://arxiv.org/abs/2105.01883
-* RepVGG - https://arxiv.org/abs/2101.03697
-* ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
-* ResNeXt - https://arxiv.org/abs/1611.05431
-* Res2Net - https://arxiv.org/abs/1904.01169
-* ReXNet - https://arxiv.org/abs/2007.00992
-* ShuffleNet v1 - https://arxiv.org/abs/1707.01083
-* ShuffleNet v2 - https://arxiv.org/abs/1807.11164
-* SKNet - https://arxiv.org/abs/1903.06586
-* SqueezeNet - https://arxiv.org/abs/1602.07360
-* Swin Transformer - https://arxiv.org/abs/2103.14030
-* VGG - https://arxiv.org/abs/1409.1556
-* Visformer - https://arxiv.org/abs/2104.12533
-* Vision Transformer (ViT) - https://arxiv.org/abs/2010.11929
-* Xception - https://arxiv.org/abs/1610.02357
+- Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
+- ConvNeXt - https://arxiv.org/abs/2201.03545
+- ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
+- DenseNet - https://arxiv.org/abs/1608.06993
+- DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
+- EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
+- EfficientNet V2 - https://arxiv.org/abs/2104.00298
+- GhostNet - https://arxiv.org/abs/1911.11907
+- GoogLeNet - https://arxiv.org/abs/1409.4842
+- Inception-V3 - https://arxiv.org/abs/1512.00567
+- Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
+- MNASNet - https://arxiv.org/abs/1807.11626
+- MobileNet-V1 - https://arxiv.org/abs/1704.04861
+- MobileNet-V2 - https://arxiv.org/abs/1801.04381
+- MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
+- NASNet - https://arxiv.org/abs/1707.07012
+- PNasNet - https://arxiv.org/abs/1712.00559
+- PVT (Pyramid Vision Transformer) - https://arxiv.org/abs/2102.12122
+- PoolFormer models - https://github.com/sail-sg/poolformer
+- RegNet - https://arxiv.org/abs/2003.13678
+- RepMLP https://arxiv.org/abs/2105.01883
+- RepVGG - https://arxiv.org/abs/2101.03697
+- ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
+- ResNeXt - https://arxiv.org/abs/1611.05431
+- Res2Net - https://arxiv.org/abs/1904.01169
+- ReXNet - https://arxiv.org/abs/2007.00992
+- ShuffleNet v1 - https://arxiv.org/abs/1707.01083
+- ShuffleNet v2 - https://arxiv.org/abs/1807.11164
+- SKNet - https://arxiv.org/abs/1903.06586
+- SqueezeNet - https://arxiv.org/abs/1602.07360
+- Swin Transformer - https://arxiv.org/abs/2103.14030
+- VGG - https://arxiv.org/abs/1409.1556
+- Visformer - https://arxiv.org/abs/2104.12533
+- Vision Transformer (ViT) - https://arxiv.org/abs/2010.11929
+- Xception - https://arxiv.org/abs/1610.02357
 
 关于模型性能和预训练权重的信息请查看 [configs](./configs) 文件夹。
 
@@ -266,43 +266,43 @@ python train.py --model=resnet50 --dataset=cifar10 \
 <details open markdown>
 <summary> 支持算法列表 </summary>
 
-* 数据增强
-    * [AutoAugment](https://arxiv.org/abs/1805.09501)
-    * [RandAugment](https://arxiv.org/abs/1909.13719)
-    * [Repeated Augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf)
-    * RandErasing (Cutout)
-    * CutMix
-    * MixUp
-    * RandomResizeCrop
-    * Color Jitter, Flip, etc
-* 优化器
-    * Adam
-    * AdamW
-    * [Lion](https://arxiv.org/abs/2302.06675)
-    * Adan (experimental)
-    * AdaGrad
-    * LAMB
-    * Momentum
-    * RMSProp
-    * SGD
-    * NAdam
-* 学习率调度器
-    * Warmup Cosine Decay
-    * Step LR
-    * Polynomial Decay
-    * Exponential Decay
-* 正则化
-    * Weight Decay
-    * Label Smoothing
-    * Stochastic Depth (depends on networks)
-    * Dropout (depends on networks)
-* 损失函数
-    * Cross Entropy (w/ class weight and auxiliary  logit support)
-    * Binary Cross Entropy  (w/ class weight and auxiliary  logit support)
-    * Soft Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
-    * Soft Binary Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
-* 模型融合
-    * Warmup EMA (Exponential Moving Average)
+- 数据增强
+  - [AutoAugment](https://arxiv.org/abs/1805.09501)
+  - [RandAugment](https://arxiv.org/abs/1909.13719)
+  - [Repeated Augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf)
+  - RandErasing (Cutout)
+  - CutMix
+  - MixUp
+  - RandomResizeCrop
+  - Color Jitter, Flip, etc
+- 优化器
+  - Adam
+  - AdamW
+  - [Lion](https://arxiv.org/abs/2302.06675)
+  - Adan (experimental)
+  - AdaGrad
+  - LAMB
+  - Momentum
+  - RMSProp
+  - SGD
+  - NAdam
+- 学习率调度器
+  - Warmup Cosine Decay
+  - Step LR
+  - Polynomial Decay
+  - Exponential Decay
+- 正则化
+  - Weight Decay
+  - Label Smoothing
+  - Stochastic Depth (depends on networks)
+  - Dropout (depends on networks)
+- 损失函数
+  - Cross Entropy (w/ class weight and auxiliary logit support)
+  - Binary Cross Entropy (w/ class weight and auxiliary logit support)
+  - Soft Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
+  - Soft Binary Cross Entropy Loss (automatically enabled if mixup or label smoothing is used)
+- 模型融合
+  - Warmup EMA (Exponential Moving Average)
 
 </details>
 
@@ -337,9 +337,10 @@ python train.py --model=resnet50 --dataset=cifar10 \
 6. BREAKING CHANGES:
    - 我们将在此小版本的未来发布中丢弃对MindSpore1.x的支持。
    - 配置项`filter_bias_and_bn`将被移除并更名为`weight_decay_filter`。
-   我们会对已有训练策略进行迁移，但函数`create_optimizer`的签名变更将是不兼容的，且未迁移旧版本的训练策略也将变得不兼容。详见[PR/752](https://github.com/mindspore-lab/mindcv/pull/752)。
+     我们会对已有训练策略进行迁移，但函数`create_optimizer`的签名变更将是不兼容的，且未迁移旧版本的训练策略也将变得不兼容。详见[PR/752](https://github.com/mindspore-lab/mindcv/pull/752)。
 
 - 2023/6/16
+
 1. 新版本 `0.2.2` 发布啦！我们将`MindSpore`升级到了2.0版本，同时保持了对1.8版本的兼容
 2. 新模型:
    - [ConvNextV2](configs/convnextv2)
@@ -357,90 +358,101 @@ python train.py --model=resnet50 --dataset=cifar10 \
    - 文档网站上的损坏链接
 
 - 2023/6/2
+
 1. 新版本：`0.2.1` 发布
 2. 新[文档](https://mindspore-lab.github.io/mindcv/zh/)上线
 
 - 2023/5/30
+
 1. 新模型:
-    - [VGG](configs/vgg)混合精度(O2)版本
-    - [GhostNet](configs/ghostnet)
-    - [MobileNetV2](configs/mobilenetv2) 和 [MobileNetV3](configs/mobilenetv3)混合精度(O3)版本
-    - [RegNet](configs/regnet)的(x,y)_(200,400,600,800)mf版本
-    - [RepVGG](configs/repvgg)的b1g2, b1g4 & b2g4版本
-    - [MnasNet](configs/mnasnet)的0.5版本
-    - [PVTv2](configs/pvtv2)的b3 & b4版本
+   - [VGG](configs/vgg)混合精度(O2)版本
+   - [GhostNet](configs/ghostnet)
+   - [MobileNetV2](configs/mobilenetv2) 和 [MobileNetV3](configs/mobilenetv3)混合精度(O3)版本
+   - [RegNet](configs/regnet)的(x,y)\_(200,400,600,800)mf版本
+   - [RepVGG](configs/repvgg)的b1g2, b1g4 & b2g4版本
+   - [MnasNet](configs/mnasnet)的0.5版本
+   - [PVTv2](configs/pvtv2)的b3 & b4版本
 2. 新特性:
-    - 3-Augment, Augmix, TrivialAugmentWide
+   - 3-Augment, Augmix, TrivialAugmentWide
 3. 错误修复:
-    - ViT 池化模式
+   - ViT 池化模式
 
 - 2023/04/28
+
 1. 增添了一些新模型，列出如下：
-    - [VGG](configs/vgg)
-    - [DPN](configs/dpn)
-    - [ResNet v2](configs/resnetv2)
-    - [MnasNet](configs/mnasnet)
-    - [MixNet](configs/mixnet)
-    - [RepVGG](configs/repvgg)
-    - [ConvNeXt](configs/convnext)
-    - [Swin Transformer](configs/swintransformer)
-    - [EdgeNeXt](configs/edgenext)
-    - [CrossViT](configs/crossvit)
-    - [XCiT](configs/xcit)
-    - [CoAT](configs/coat)
-    - [PiT](configs/pit)
-    - [PVT v2](configs/pvtv2)
-    - [MobileViT](configs/mobilevit)
+   - [VGG](configs/vgg)
+   - [DPN](configs/dpn)
+   - [ResNet v2](configs/resnetv2)
+   - [MnasNet](configs/mnasnet)
+   - [MixNet](configs/mixnet)
+   - [RepVGG](configs/repvgg)
+   - [ConvNeXt](configs/convnext)
+   - [Swin Transformer](configs/swintransformer)
+   - [EdgeNeXt](configs/edgenext)
+   - [CrossViT](configs/crossvit)
+   - [XCiT](configs/xcit)
+   - [CoAT](configs/coat)
+   - [PiT](configs/pit)
+   - [PVT v2](configs/pvtv2)
+   - [MobileViT](configs/mobilevit)
 2. 错误修正:
-    - 分布式训练时，需对每个进程设置相同的随机数种子
-    - 检查YAML配置文件中的选项是否存在于命令行解析器
-    - 修正了优化器`Adan`中标志变量不为`Tensor`的错误
+   - 分布式训练时，需对每个进程设置相同的随机数种子
+   - 检查YAML配置文件中的选项是否存在于命令行解析器
+   - 修正了优化器`Adan`中标志变量不为`Tensor`的错误
 
 - 2023/03/25
+
 1. 更新ResNet网络预训练权重，现在预训练权重有更高Top1精度
-    - ResNet18精度从70.09提升到70.31
-    - ResNet34精度从73.69提升到74.15
-    - ResNet50精度从76.64提升到76.69
-    - ResNet101精度从77.63提升到78.24
-    - ResNet152精度从78.63提升到78.72
+   - ResNet18精度从70.09提升到70.31
+   - ResNet34精度从73.69提升到74.15
+   - ResNet50精度从76.64提升到76.69
+   - ResNet101精度从77.63提升到78.24
+   - ResNet152精度从78.63提升到78.72
 2. 按照规则(model_scale-sha256sum.ckpt)更新预训练权重名字和相应下载URL链接
 
 - 2023/03/05
+
 1. 增加Lion (EvoLved Sign Momentum)优化器，论文 https://arxiv.org/abs/2302.06675
-    - Lion所使用的学习率一般比Adamw小3到10倍，而权重衰减(weigt_decay)要大3到10倍
+   - Lion所使用的学习率一般比Adamw小3到10倍，而权重衰减(weigt_decay)要大3到10倍
 2. 增加6个模型及其训练策略、预训练权重：
-    - [HRNet](configs/hrnet)
-    - [SENet](configs/senet)
-    - [GoogLeNet](configs/googlenet)
-    - [Inception V3](configs/inceptionv3)
-    - [Inception V4](configs/inceptionv4)
-    - [Xception](configs/xception)
+   - [HRNet](configs/hrnet)
+   - [SENet](configs/senet)
+   - [GoogLeNet](configs/googlenet)
+   - [Inception V3](configs/inceptionv3)
+   - [Inception V4](configs/inceptionv4)
+   - [Xception](configs/xception)
 3. 支持梯度裁剪
 
 - 2023/01/10
+
 1. MindCV 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
+
 1. 支持在所有学习率策略中添加学习率预热操作，除cosine decay策略外
 2. 支持`Repeated Augmenation`操作，可以通过`--aug_repeats`对其进行设置，设置值应大于1(通常为3或4)
 3. 支持EMA
 4. 通过支持mixup和cutmix操作进一步优化BCE损失函数
 
 - 2022/11/21
+
 1. 支持模型损失和正确率的可视化
 2. 支持轮次维度的cosine decay策略的学习率预热操作（之前仅支持步维度）
 
 - 2022/11/09
+
 1. 支持2个ViT预训练模型
 2. 支持RandAugment augmentation操作
 3. 提高了CutMix操作的可用性，CutMix和Mixup目前可以一起使用
 4. 解决了学习率画图的bug
 
 - 2022/10/12
+
 1. BCE和CE损失函数目前都支持class-weight config操作、label smoothing操作、auxilary logit input操作（适用于类似Inception模型）
 
 - 2022/09/13
+
 1. 支持Adan优化器(试用版)
 
 ## 贡献方式
diff --git a/RELEASE.md b/RELEASE.md
index a1b3c72f6..d36d68e71 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -29,9 +29,9 @@ Release `0.3.0` is published. We will drop MindSpore 1.x in the future release.
 6. BREAKING CHANGES:
    - We are going to drop support of MindSpore 1.x for it's EOL.
    - Configuration `filter_bias_and_bn` will be removed and renamed as `weight_decay_filter`,
-   due to a prolonged misunderstanding of the MindSpore optimizer.
-   We will migrate the existing training recipes, but the signature change of function a will be incompatible
-   and the old version training recipes will also be incompatible. See [PR/752](https://github.com/mindspore-lab/mindcv/pull/752) for details.
+     due to a prolonged misunderstanding of the MindSpore optimizer.
+     We will migrate the existing training recipes, but the signature change of function a will be incompatible
+     and the old version training recipes will also be incompatible. See [PR/752](https://github.com/mindspore-lab/mindcv/pull/752) for details.
 
 ## 0.2.2 (2023/6/16)
 
@@ -54,99 +54,110 @@ Release `0.3.0` is published. We will drop MindSpore 1.x in the future release.
 ## 0.2.1
 
 - 2023/6/2
+
 1. New version: `0.2.1` is released!
 2. New [documents](https://mindspore-lab.github.io/mindcv/) is online!
 
 - 2023/5/30
+
 1. New Models:
-    - AMP(O2) version of [VGG](configs/vgg)
-    - [GhostNet](configs/ghostnet)
-    - AMP(O3) version of [MobileNetV2](configs/mobilenetv2) and [MobileNetV3](configs/mobilenetv3)
-    - (x,y)_(200,400,600,800)mf of [RegNet](configs/regnet)
-    - b1g2, b1g4 & b2g4 of [RepVGG](configs/repvgg)
-    - 0.5 of [MnasNet](configs/mnasnet)
-    - b3 & b4 of [PVTv2](configs/pvtv2)
+   - AMP(O2) version of [VGG](configs/vgg)
+   - [GhostNet](configs/ghostnet)
+   - AMP(O3) version of [MobileNetV2](configs/mobilenetv2) and [MobileNetV3](configs/mobilenetv3)
+   - (x,y)\_(200,400,600,800)mf of [RegNet](configs/regnet)
+   - b1g2, b1g4 & b2g4 of [RepVGG](configs/repvgg)
+   - 0.5 of [MnasNet](configs/mnasnet)
+   - b3 & b4 of [PVTv2](configs/pvtv2)
 2. New Features:
-    - 3-Augment, Augmix, TrivialAugmentWide
+   - 3-Augment, Augmix, TrivialAugmentWide
 3. Bug Fixes:
-    - ViT pooling mode
+   - ViT pooling mode
 
 - 2023/04/28
+
 1. Add some new models, listed as following
-    - [VGG](configs/vgg)
-    - [DPN](configs/dpn)
-    - [ResNet v2](configs/resnetv2)
-    - [MnasNet](configs/mnasnet)
-    - [MixNet](configs/mixnet)
-    - [RepVGG](configs/repvgg)
-    - [ConvNeXt](configs/convnext)
-    - [Swin Transformer](configs/swintransformer)
-    - [EdgeNeXt](configs/edgenext)
-    - [CrossViT](configs/crossvit)
-    - [XCiT](configs/xcit)
-    - [CoAT](configs/coat)
-    - [PiT](configs/pit)
-    - [PVT v2](configs/pvtv2)
-    - [MobileViT](configs/mobilevit)
+   - [VGG](configs/vgg)
+   - [DPN](configs/dpn)
+   - [ResNet v2](configs/resnetv2)
+   - [MnasNet](configs/mnasnet)
+   - [MixNet](configs/mixnet)
+   - [RepVGG](configs/repvgg)
+   - [ConvNeXt](configs/convnext)
+   - [Swin Transformer](configs/swintransformer)
+   - [EdgeNeXt](configs/edgenext)
+   - [CrossViT](configs/crossvit)
+   - [XCiT](configs/xcit)
+   - [CoAT](configs/coat)
+   - [PiT](configs/pit)
+   - [PVT v2](configs/pvtv2)
+   - [MobileViT](configs/mobilevit)
 2. Bug fix:
-    - Setting the same random seed for each rank
-    - Checking if options from yaml config exist in argument parser
-    - Initializing flag variable as `Tensor` in Optimizer `Adan`
+   - Setting the same random seed for each rank
+   - Checking if options from yaml config exist in argument parser
+   - Initializing flag variable as `Tensor` in Optimizer `Adan`
 
 ## 0.2.0
 
 - 2023/03/25
+
 1. Update checkpoints for pretrained ResNet for better accuracy
-    - ResNet18 (from 70.09 to 70.31 @Top1 accuracy)
-    - ResNet34 (from 73.69 to 74.15 @Top1 accuracy)
-    - ResNet50 (from 76.64 to 76.69 @Top1 accuracy)
-    - ResNet101 (from 77.63 to 78.24 @Top1 accuracy)
-    - ResNet152 (from 78.63 to 78.72 @Top1 accuracy)
+   - ResNet18 (from 70.09 to 70.31 @Top1 accuracy)
+   - ResNet34 (from 73.69 to 74.15 @Top1 accuracy)
+   - ResNet50 (from 76.64 to 76.69 @Top1 accuracy)
+   - ResNet101 (from 77.63 to 78.24 @Top1 accuracy)
+   - ResNet152 (from 78.63 to 78.72 @Top1 accuracy)
 2. Rename checkpoint file name to follow naming rule ({model_scale-sha256sum.ckpt}) and update download URLs.
 
 - 2023/03/05
+
 1. Add Lion (EvoLved Sign Momentum) optimizer from paper https://arxiv.org/abs/2302.06675
-    - To replace adamw with lion, LR is usually 3-10x smaller, and weight decay is usually 3-10x larger than adamw.
+   - To replace adamw with lion, LR is usually 3-10x smaller, and weight decay is usually 3-10x larger than adamw.
 2. Add 6 new models with training recipes and pretrained weights for
-    - [HRNet](configs/hrnet)
-    - [SENet](configs/senet)
-    - [GoogLeNet](configs/googlenet)
-    - [Inception V3](configs/inceptionv3)
-    - [Inception V4](configs/inceptionv4)
-    - [Xception](configs/xception)
+   - [HRNet](configs/hrnet)
+   - [SENet](configs/senet)
+   - [GoogLeNet](configs/googlenet)
+   - [Inception V3](configs/inceptionv3)
+   - [Inception V4](configs/inceptionv4)
+   - [Xception](configs/xception)
 3. Support gradient clip
 4. Arg name `use_ema` changed to **`ema`**, add `ema: True` in yaml to enable EMA.
 
 ## 0.1.1
 
 - 2023/01/10
+
 1. MindCV v0.1 released! It can be installed via PyPI `pip install mindcv` now.
 2. Add training recipe and trained weights of googlenet, inception_v3, inception_v4, xception
 
 ## 0.1.0
 
 - 2022/12/09
+
 1. Support lr warmup for all lr scheduling algorithms besides cosine decay.
 2. Add repeated augmentation, which can be enabled by setting `--aug_repeats` to be a value larger than 1 (typically, 3 or 4 is a common choice).
 3. Add EMA.
 4. Improve BCE loss to support mixup/cutmix.
 
 - 2022/11/21
+
 1. Add visualization for loss and acc curves
 2. Support epochwise lr warmup cosine decay (previous is stepwise)
 
 - 2022/11/09
+
 1. Add 7 pretrained ViT models.
 2. Add RandAugment augmentation.
 3. Fix CutMix efficiency issue and CutMix and Mixup can be used together.
 4. Fix lr plot and scheduling bug.
 
 - 2022/10/12
+
 1. Both BCE and CE loss now support class-weight config, label smoothing, and auxiliary logit input (for networks like inception).
 
 ## 0.0.1-beta
 
 - 2022/09/13
+
 1. Add Adan optimizer (experimental)
 
 ## MindSpore Computer Vision 0.0.1
diff --git a/benchmark_results.md b/benchmark_results.md
index ae930e38c..ef87c29ba 100644
--- a/benchmark_results.md
+++ b/benchmark_results.md
@@ -1,8 +1,6 @@
-
 <details open markdown>
 <summary><b>performance tested on Ascend 910(8p) with graph mode</b></summary>
 
-
 | model name             | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                              | weight                                                                                                              |
 | ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
 | bit_resnet50           | 25.55     | 8     | 32         | 224x224    | O2        | 146s          | 74.52   | 3413.33  | 76.81    | 93.17    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml)                      | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt)                             |
@@ -63,9 +61,6 @@
 <details open markdown>
 <summary><b>performance tested on Ascend 910*(8p) with graph mode</b></summary>
 
-
-
-
 | model name             | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                              | weight                                                                                                                            |
 | ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
 | convit_tiny            | 5.71      | 8     | 256        | 224x224    | O2        | 153s          | 226.51  | 9022.03  | 73.79    | 91.70    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml)                    | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt)                           |
@@ -115,4 +110,5 @@
 </details>
 
 ### Notes
+
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/README.md b/configs/README.md
index 79e2d5d98..465737abf 100644
--- a/configs/README.md
+++ b/configs/README.md
@@ -1,6 +1,6 @@
 ### File Structure and Naming
-This folder contains training recipes and model readme files for each model. The folder structure and naming rule of model configurations are as follows.
 
+This folder contains training recipes and model readme files for each model. The folder structure and naming rule of model configurations are as follows.
 
 ```
     ├── configs
@@ -25,22 +25,20 @@ This folder contains training recipes and model readme files for each model. The
 > generalization across different hardware.
 
 ### Model Readme Writing Guideline
+
 The model readme file in each sub-folder provides the introduction, reproduced results, and running guideline for each model.
 
 Please follow the outline structure and **table format** shown in [densenet/README.md](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/README.md) when contributing your models :)
 
 #### Table Format
 
-
-
 | model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                                    |
 | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
 | densenet121 | 8.06      | 8     | 32         | 224x224    | O2        | 300s          | 47,34   | 5446.81 | 75.67    | 92.77    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) |
 
-
-
 Illustration:
-- model name: model name in lower case with _ seperator.
+
+- model name: model name in lower case with \_ seperator.
 - top-1 and top-5: Accuracy reported on the validatoin set of ImageNet-1K. Keep 2 digits after the decimal point.
 - params(M): # of model parameters in millions (10^6). Keep **2 digits** after the decimal point
 - batch size: Training batch size
@@ -51,27 +49,28 @@ Illustration:
 - weight: url of the pretrained model weights
 
 ### Model Checkpoint Format
- The checkpoint (i.e., model weight) name should follow this format:  **{model_name}_{specification}-{sha256sum}.ckpt**, e.g., `poolformer_s12-5be5c4e4.ckpt`.
 
- You can run the following command and take the first 8 characters of the computing result as the sha256sum value in the checkpoint name.
+The checkpoint (i.e., model weight) name should follow this format: **{model*name}*{specification}-{sha256sum}.ckpt**, e.g., `poolformer_s12-5be5c4e4.ckpt`.
 
- ```shell
- sha256sum your_model.ckpt
- ```
+You can run the following command and take the first 8 characters of the computing result as the sha256sum value in the checkpoint name.
 
+```shell
+sha256sum your_model.ckpt
+```
 
 #### Training Script Format
 
 For consistency, it is recommended to provide distributed training commands based on `msrun --bind_core=True --worker_num {num_devices} python train.py`, instead of using shell script such as `distrubuted_train.sh`.
 
-  ```shell
-  # standalone training on single NPU device
-  python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/dataset --distribute False
+```shell
+# standalone training on single NPU device
+python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/dataset --distribute False
 
-  # distributed training on NPU divices
-  msrun --bind_core=True --worker_num 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet
+# distributed training on NPU divices
+msrun --bind_core=True --worker_num 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet
 
-  ```
+```
 
 #### URL and Hyperlink Format
+
 Please use **absolute path** in the hyperlink or url for linking the target resource in the readme file and table.
diff --git a/configs/bit/README.md b/configs/bit/README.md
index ab287b379..5f6b6d219 100644
--- a/configs/bit/README.md
+++ b/configs/bit/README.md
@@ -69,16 +69,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name   | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                  |
-| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
-| bit_resnet50 | 25.55     | 8     | 32         | 224x224    | O2        | 146s          | 74.52   | 3413.33 | 76.81    | 93.17    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) |
-
-
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                                  | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- |-----------| ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -------- | -------- |
+| bit         | 8     | 32         | 224x224    | O2        | 171s          | 60.48   | 4232.80 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml)             | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/bit/BiT_resnet50_best_v2.ckpt)             | 76.72    | 93.25    |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/cmt/README.md b/configs/cmt/README.md
index 3e2cd97c7..af94335a9 100644
--- a/configs/cmt/README.md
+++ b/configs/cmt/README.md
@@ -67,13 +67,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                          | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- | -------- | -------- |
+| cmt         | 8     | 128        | 224x224    | O2        | 1210s         | 324.95  | 3151.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml)             | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/cmt/cmt_small-184_1251_v2.ckpt)             | 83.15    | 96.48    |
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                      | weight                                                                               |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
-| cmt_small  | 26.09     | 8     | 128        | 224x224    | O2        | 1268s         | 500.64  | 2048.01 | 83.24    | 96.41    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/coat/README.md b/configs/coat/README.md
index c9cbc9eab..edd8770c4 100644
--- a/configs/coat/README.md
+++ b/configs/coat/README.md
@@ -63,17 +63,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                       | weight                                                                                |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
-| coat_tiny  | 5.50      | 8     | 32         | 224x224    | O2        | 543s          | 254.95  | 1003.92 | 79.67    | 94.88    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) |
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                         | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------- | -------- | -------- |
+| coat        | 8     | 32         | 224x224    | O2        | 644s          | 373.00  | 686.33  | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_lite_tiny_ascend.yaml)           | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/coat/coat_tiny-dcca16b1-910v2.ckpt)           | 79.27    | 94.29    |
 
 
 
diff --git a/configs/convit/README.md b/configs/convit/README.md
index 3ba9dbfc6..a493a44d0 100644
--- a/configs/convit/README.md
+++ b/configs/convit/README.md
@@ -77,15 +77,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                           | weight                                                                                                  |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- |
-| convit_tiny | 5.71      | 8     | 256        | 224x224    | O2        | 153s          | 226.51  | 9022.03 | 73.79    | 91.70    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                           | weight                                                                                    |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
-| convit_tiny | 5.71      | 8     | 256        | 224x224    | O2        | 133s          | 231.62  | 8827.59 | 73.66    | 91.72    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) |
-
+| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- |
+| convit_tiny | 5.71      | 8     | 256        | 224x224    | O2        | 153s          | 221.21  | 9258.17 | 73.79    | 91.70    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/convnext/README.md b/configs/convnext/README.md
index 781150b92..9d4cd8fd4 100644
--- a/configs/convnext/README.md
+++ b/configs/convnext/README.md
@@ -75,14 +75,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name    | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                               | weight                                                                                                      |
-| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
-| convnext_tiny | 28.59     | 8     | 16         | 224x224    | O2        | 137s          | 48.7    | 2612.24 | 81.28    | 95.61    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name    | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                               | weight                                                                                        |
-| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
-| convnext_tiny | 28.59     | 8     | 16         | 224x224    | O2        | 127s          | 66.79   | 1910.45 | 81.91    | 95.79    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) |
+| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
+| convnext_tiny | 28.59     | 8     | 16         | 224x224    | O2        | 137s          | 36.08   | 3547.67 | 81.28    | 95.61    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/convnextv2/README.md b/configs/convnextv2/README.md
index 4ef7f251b..41bdaca27 100644
--- a/configs/convnextv2/README.md
+++ b/configs/convnextv2/README.md
@@ -74,14 +74,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                   | weight                                                                                                          |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
-| convnextv2_tiny | 28.64     | 8     | 128        | 224x224    | O2        | 268s          | 257.2   | 3984.44 | 82.39    | 95.95    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                   | weight                                                                                            |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
-| convnextv2_tiny | 28.64     | 8     | 128        | 224x224    | O2        | 237s          | 400.20  | 2560.00 | 82.43    | 95.98    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |
+| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
+| convnextv2_tiny | 28.64     | 8     | 128        | 224x224    | O2        | 268s          | 280.47  | 3651.01 | 82.39    | 95.95    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/crossvit/README.md b/configs/crossvit/README.md
index 1e19361c2..9f792cddf 100644
--- a/configs/crossvit/README.md
+++ b/configs/crossvit/README.md
@@ -70,14 +70,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                            | weight                                                                                                   |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
-| crossvit_9 | 8.55      | 8     | 256        | 240x240    | O2        | 221s          | 514.36  | 3984.44 | 73.38    | 91.51    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                            | weight                                                                                     |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
-| crossvit_9 | 8.55      | 8     | 256        | 240x240    | O2        | 206s          | 550.79  | 3719.30 | 73.56    | 91.79    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
+| crossvit_9 | 8.55      | 8     | 256        | 240x240    | O2        | 221s          | 498.96  | 4104.53 | 73.38    | 91.51    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/densenet/README.md b/configs/densenet/README.md
index 77f4402b0..f5d6ed835 100644
--- a/configs/densenet/README.md
+++ b/configs/densenet/README.md
@@ -78,15 +78,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
-| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                                    |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
-| densenet121 | 8.06      | 8     | 32         | 224x224    | O2        | 300s          | 47,34   | 5446.81 | 75.67    | 92.77    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                             |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
-| densenet121 | 8.06      | 8     | 32         | 224x224    | O2        | 191s          | 43.28   | 5914.97 | 75.64    | 92.84    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) |
+| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                                    |
+| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| --- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
+| densenet121 | 8.06      | 8     | 32         | 224x224    | O2        | 300s          | 50.01   | 5118.97 | 75.67    | 92.77    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/dpn/README.md b/configs/dpn/README.md
index a7b58c5d9..8d724a17f 100644
--- a/configs/dpn/README.md
+++ b/configs/dpn/README.md
@@ -75,14 +75,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                          | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- | -------- | -------- |
+| dpn         | 8     | 32         | 224x224    | O2        | 336s          | 76.23   | 3358.26 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn131_ascend.yaml)             | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/dpn/dpn92-189_5004_v2.ckpt)             | 76.00    | 92.45    |
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                  | weight                                                                          |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- |
-| dpn92      | 37.79     | 8     | 32         | 224x224    | O2        | 293s          | 78.22   | 3272.82 | 79.46    | 94.49    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/edgenext/README.md b/configs/edgenext/README.md
index a91d50d8f..ca49d8c30 100644
--- a/configs/edgenext/README.md
+++ b/configs/edgenext/README.md
@@ -76,14 +76,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name        | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                   | weight                                                                                                        |
-| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
-| edgenext_xx_small | 1.33      | 8     | 256        | 256x256    | O2        | 389s          | 239.38  | 8555.43 | 70.64    | 89.75    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_xx_small-cad13d2c-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name        | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                   | weight                                                                                          |
-| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
-| edgenext_xx_small | 1.33      | 8     | 256        | 256x256    | O2        | 311s          | 191.24  | 10709.06 | 71.02    | 89.99    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/edgenext/edgenext_xx_small-afc971fb.ckpt) |
+| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |--------| ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
+| edgenext_xx_small | 1.33      | 8     | 256        | 256x256    | O2        | 389s          | 225.09 | 9098.58 | 70.64    | 89.75    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_xx_small-cad13d2c-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/efficientnet/README.md b/configs/efficientnet/README.md
index e05800fc9..ea1cba257 100644
--- a/configs/efficientnet/README.md
+++ b/configs/efficientnet/README.md
@@ -76,14 +76,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                     | weight                                                                                                            |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
-| efficientnet_b0 | 5.33      | 8     | 128        | 224x224    | O2        | 353s          | 172.64  | 5931.42 | 76.88    | 93.28    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-f8d7aa2a-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                     | weight                                                                                              |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
-| efficientnet_b0 | 5.33      | 8     | 128        | 224x224    | O2        | 203s          | 172.78  | 5926.61 | 76.89    | 93.16    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/efficientnet/efficientnet_b0-103ec70c.ckpt) |
+| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
+| efficientnet_b0 | 5.33      | 8     | 128        | 224x224    | O2        | 353s          | 173.47  | 5903.03| 76.88    | 93.28    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-f8d7aa2a-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/ghostnet/README.md b/configs/ghostnet/README.md
index afe4055db..da8e781fc 100644
--- a/configs/ghostnet/README.md
+++ b/configs/ghostnet/README.md
@@ -77,13 +77,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                     | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- |-----------| ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ | -------- | -------- |
+| ghostnet    | 8     | 128        | 224x224    | O2        | 125s          | 201.46  | 5082.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml)   | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/ghostnet/ghostnet_050-ae7771cb-910v2.ckpt)   | 65.93    | 86.65    |
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name   | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                       |
-| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
-| ghostnet_050 | 2.60      | 8     | 128        | 224x224    | O2        | 383s          | 211.13  | 4850.09 | 66.03    | 86.64    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/googlenet/README.md b/configs/googlenet/README.md
index d76ec1dc6..b75607c05 100644
--- a/configs/googlenet/README.md
+++ b/configs/googlenet/README.md
@@ -77,12 +77,6 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
 | googlenet  | 6.99      | 8     | 32         | 224x224    | O2        | 113s          | 23.5    | 10893.62 | 72.89    | 90.89    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-de74c31d-910v2.ckpt) |
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                            | weight                                                                                     |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
-| googlenet  | 6.99      | 8     | 32         | 224x224    | O2        | 72s           | 21.40   | 11962.62 | 72.68    | 90.89    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) |
-
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
 
diff --git a/configs/halonet/README.md b/configs/halonet/README.md
index 86fa5a24d..86e563e6c 100644
--- a/configs/halonet/README.md
+++ b/configs/halonet/README.md
@@ -82,11 +82,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                      | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- |-----------| ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------- | -------- | -------- |
+| halonet     | 8     | 64         | 224x224    | O2        | 351s          | 134.72  | 3800.48 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/halonet/halonet_50t_ascend.yaml)     | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/halonet/halonet_50t-533da6be.ckpt)     | 0.10     | 0.10     |
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-*coming soon*
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md
index 89027f29a..5d8747fe3 100644
--- a/configs/hrnet/README.md
+++ b/configs/hrnet/README.md
@@ -74,14 +74,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                        | weight                                                                                               |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
-| hrnet_w32  | 41.30     | 8     | 128        | 224x224    | O2        | 1069s         | 238.03  | 4301.98 | 80.66    | 95.30    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/hrnet/hrnet_w32-e616cdcb-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                        | weight                                                                                 |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- |
-| hrnet_w32  | 41.30     | 128   | 8          | 224x224    | O2        | 1312s         | 279.10  | 3668.94 | 80.64    | 95.44    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/hrnet/hrnet_w32-cc4fbd91.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| hrnet_w32  | 41.30     | 8     | 128        | 224x224    | O2        | 1069s         | 244.02  | 4196.37 | 80.66    | 95.30    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/hrnet/hrnet_w32-e616cdcb-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/inceptionv3/README.md b/configs/inceptionv3/README.md
index 3245a0481..a763c0bb0 100644
--- a/configs/inceptionv3/README.md
+++ b/configs/inceptionv3/README.md
@@ -77,14 +77,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name   | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                 | weight                                                                                                         |
-| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- |
-| inception_v3 | 27.20     | 8     | 32         | 299x299    | O2        | 172s          | 70.83   | 3614.29 | 79.25    | 94.47    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v3/inception_v3-61a8e9ed-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name   | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                 | weight                                                                                           |
-| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ |
-| inception_v3 | 27.20     | 8     | 32         | 299x299    | O2        | 120s          | 76.42   | 3349.91 | 79.11    | 94.40    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) |
+| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- |
+| inception_v3 | 27.20     | 8     | 32         | 299x299    | O2        | 172s          | 73.43   | 3486.31 | 79.25    | 94.47    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v3/inception_v3-61a8e9ed-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/inceptionv4/README.md b/configs/inceptionv4/README.md
index d2ed27bb1..4ee9430d3 100644
--- a/configs/inceptionv4/README.md
+++ b/configs/inceptionv4/README.md
@@ -73,16 +73,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name   | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                 | weight                                                                                                         |
-| ------------ | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- |
-| inception_v4 | 42.74     | 8     | 32         | 299x299    | O2        | 263s          | 80.97   | 3161.66 | 80.98    | 95.25    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v4/inception_v4-56e798fc-910v2.ckpt) |
+| ------------ | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- |
+| inception_v4 | 42.74     | 8     | 32         | 299x299    | O2        | 263s          | 83.24   | 3075.44 | 80.98    | 95.25    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v4/inception_v4-56e798fc-910v2.ckpt) |
 
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name   | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                 | weight                                                                                           |
-| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ |
-| inception_v4 | 42.74     | 8     | 32         | 299x299    | O2        | 177s          | 76.19   | 3360.02 | 80.88    | 95.34    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) |
-
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
 
diff --git a/configs/mixnet/README.md b/configs/mixnet/README.md
index 5f825b80b..23464810d 100644
--- a/configs/mixnet/README.md
+++ b/configs/mixnet/README.md
@@ -77,15 +77,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                        | weight                                                                                               |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
-| mixnet_s   | 4.17      | 8     | 128        | 224x224    | O2        | 706s          | 228.03  | 4490.64 | 75.58    | 95.54    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mixnet/mixnet_s-fe4fcc63-910v2.ckpt) |
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                        | weight                                                                                 |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- |
-| mixnet_s   | 4.17      | 8     | 128        | 224x224    | O2        | 556s          | 252.49  | 4055.61 | 75.52    | 92.52    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mixnet/mixnet_s-2a5ef3a3.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| mixnet_s   | 4.17      | 8     | 128        | 224x224    | O2        | 706s          | 223.55  | 4580.63 | 75.58    | 95.54    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mixnet/mixnet_s-fe4fcc63-910v2.ckpt) |
 
 
 ### Notes
diff --git a/configs/mnasnet/README.md b/configs/mnasnet/README.md
index edc2877b6..a99f05923 100644
--- a/configs/mnasnet/README.md
+++ b/configs/mnasnet/README.md
@@ -43,8 +43,6 @@ msrun --bind_core=True --worker_num 8 python train.py --config configs/mnasnet/m
 ```
 
 
-
-
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py).
 
 **Note:**  As the global batch size  (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.
@@ -74,16 +72,9 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                             | weight                                                                                                   |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
-| mnasnet_075 | 3.20      | 8     | 256        | 224x224    | O2        | 144s          | 175.85  | 11646.29 | 71.77    | 90.52    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mnasnet/mnasnet_075-083b2bc4-910v2.ckpt) |
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
+| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
+| mnasnet_075 | 3.20      | 8     | 256        | 224x224    | O2        | 144s          | 175.6   | 11662.87 | 71.77    | 90.52    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mnasnet/mnasnet_075-083b2bc4-910v2.ckpt) |
 
-| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                             | weight                                                                                     |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
-| mnasnet_075 | 3.20      | 8     | 256        | 224x224    | O2        | 140s          | 165.43  | 12379.86 | 71.81    | 90.53    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mnasnet/mnasnet_075-465d366d.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/mobilenetv1/README.md b/configs/mobilenetv1/README.md
index c3a615392..be327d4cd 100644
--- a/configs/mobilenetv1/README.md
+++ b/configs/mobilenetv1/README.md
@@ -70,14 +70,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name       | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                      | weight                                                                                                                      |
-| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
-| mobilenet_v1_025 | 0.47      | 8     | 64         | 224x224    | O2        | 195s          | 47.47   | 10785.76 | 54.05    | 77.74    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-cbe3d3b3-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name       | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                      | weight                                                                                                        |
-| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
-| mobilenet_v1_025 | 0.47      | 8     | 64         | 224x224    | O2        | 89s           | 42.43   | 12066.93 | 53.87    | 77.66    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-d3377fba.ckpt) |
+| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
+| mobilenet_v1_025 | 0.47      | 8     | 64         | 224x224    | O2        | 195s          | 46.4    | 11034.48 | 54.05    | 77.74    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-cbe3d3b3-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/mobilenetv2/README.md b/configs/mobilenetv2/README.md
index 226cd6b73..35fb585e7 100644
--- a/configs/mobilenetv2/README.md
+++ b/configs/mobilenetv2/README.md
@@ -74,16 +74,10 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name       | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                      | weight                                                                                                                      |
-| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
-| mobilenet_v2_075 | 2.66      | 8     | 256        | 224x224    | O2        | 233s          | 174.65  | 11726.31 | 69.73    | 89.35    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-755932c4-910v2.ckpt) |
+| ---------------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
+| mobilenet_v2_075 | 2.66      | 8     | 256        | 224x224    | O2        | 233s          | 175.15  | 11692.83 | 69.73    | 89.35    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-755932c4-910v2.ckpt) |
 
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name       | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                      | weight                                                                                                        |
-| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
-| mobilenet_v2_075 | 2.66      | 8     | 256        | 224x224    | O2        | 164s          | 155.94  | 13133.26 | 69.98    | 89.32    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-bd7bd4c4.ckpt) |
-
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
 
diff --git a/configs/mobilenetv3/README.md b/configs/mobilenetv3/README.md
index 1a7b98d2a..f958699b5 100644
--- a/configs/mobilenetv3/README.md
+++ b/configs/mobilenetv3/README.md
@@ -76,17 +76,9 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name             | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                       | weight                                                                                                                            |
-| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------- |
-| mobilenet_v3_small_100 | 2.55      | 8     | 75         | 224x224    | O2        | 184s          | 52.38   | 11454.75 | 68.07    | 87.77    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) |
-| mobilenet_v3_large_100 | 5.51      | 8     | 75         | 224x224    | O2        | 354s          | 55.89   | 10735.37 | 75.59    | 92.57    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name             | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                       | weight                                                                                                              |
-| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------- |
-| mobilenet_v3_small_100 | 2.55      | 8     | 75         | 224x224    | O2        | 145s          | 48.14   | 12463.65 | 68.10    | 87.86    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-509c6047.ckpt) |
-| mobilenet_v3_large_100 | 5.51      | 8     | 75         | 224x224    | O2        | 271s          | 47.49   | 12634.24 | 75.23    | 92.31    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) |
+| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------- |
+| mobilenet_v3_small_100 | 2.55      | 8     | 75         | 224x224    | O2        | 184s          | 54.21   | 11068.06 | 68.07    | 87.77    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) |
+| mobilenet_v3_large_100 | 5.51      | 8     | 75         | 224x224    | O2        | 354s          | 56.87   | 10550.37 | 75.59    | 92.57    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/mobilevit/README.md b/configs/mobilevit/README.md
index 2daf7cf84..aae42ef67 100644
--- a/configs/mobilevit/README.md
+++ b/configs/mobilevit/README.md
@@ -72,16 +72,9 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name         | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                     | weight                                                                                                            |
-| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
-| mobilevit_xx_small | 1.27      | 8     | 64         | 256x256    | O2        | 437s          | 67.24   | 7614.52 | 67.11    | 87.85    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-6f2745c3-910v2.ckpt) |
+| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
+| mobilevit_xx_small | 1.27      | 8     | 64         | 256x256    | O2        | 437s          | 57.34   | 8929.19 | 67.11    | 87.85    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-6f2745c3-910v2.ckpt) |
 
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name         | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                     | weight                                                                                              |
-| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
-| mobilevit_xx_small | 1.27      | 64    | 8          | 256x256    | O2        | 301s          | 53.52   | 9566.52 | 68.91    | 88.91    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-af9da8a0.ckpt) |
-
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/nasnet/README.md b/configs/nasnet/README.md
index 612313679..57f4bb49f 100644
--- a/configs/nasnet/README.md
+++ b/configs/nasnet/README.md
@@ -79,17 +79,11 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                               | weight                                                                                                       |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ |
-| nasnet_a_4x1056 | 5.33      | 8     | 256        | 224x224    | O2        | 800s          | 364.35  | 5620.97 | 74.12    | 91.36    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-015ba575c-910v2.ckpt) |
+| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ |
+| nasnet_a_4x1056 | 5.33      | 8     | 256        | 224x224    | O2        | 800s          | 364.55  | 5617.88 | 74.12    | 91.36    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-015ba575c-910v2.ckpt) |
 
 
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-| model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                               | weight                                                                                        |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
-| nasnet_a_4x1056 | 5.33      | 8     | 256        | 224x224    | O2        | 656s          | 330.89  | 6189.37 | 73.65    | 91.25    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-0fbb5cdd.ckpt) |
-
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
 
diff --git a/configs/pit/README.md b/configs/pit/README.md
index 765a66e14..cc531df1b 100644
--- a/configs/pit/README.md
+++ b/configs/pit/README.md
@@ -76,20 +76,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                   | weight                                                                                          |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
-| pit_ti     | 4.85      | 8     | 128        | 224x224    | O2        | 212s          | 266.47  | 3842.83 | 73.26    | 91.57    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pit/pit_ti-33466a0d-910v2.ckpt) |
-
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                   | weight                                                                            |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
-| pit_ti     | 4.85      | 8     | 128        | 224x224    | O2        | 192s          | 271.50  | 3771.64 | 72.96    | 91.33    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pit/pit_ti-e647a593.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
+| pit_ti     | 4.85      | 8     | 128        | 224x224    | O2        | 212s          | 257.31  | 3979.63 | 73.26    | 91.57    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pit/pit_ti-33466a0d-910v2.ckpt) |
 
 
 
diff --git a/configs/poolformer/README.md b/configs/poolformer/README.md
index ad4046a23..ce4246f23 100644
--- a/configs/poolformer/README.md
+++ b/configs/poolformer/README.md
@@ -73,15 +73,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name     | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                  | weight                                                                                                         |
-| -------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
-| poolformer_s12 | 11.92     | 8     | 128        | 224x224    | O2        | 177s          | 211.81  | 4834.52 | 77.49    | 93.55    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/poolformer/poolformer_s12-c7e14eea-910v2.ckpt) |
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name     | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                  | weight                                                                                           |
-| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ |
-| poolformer_s12 | 11.92     | 8     | 128        | 224x224    | O2        | 118s          | 220.13  | 4651.80 | 77.33    | 93.34    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/poolformer/poolformer_s12-5be5c4e4.ckpt) |
+| -------------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
+| poolformer_s12 | 11.92     | 8     | 128        | 224x224    | O2        | 177s          | 222.46  | 4603.07 | 77.49    | 93.55    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/poolformer/poolformer_s12-c7e14eea-910v2.ckpt) |
 
 ### Notes
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/pvt/README.md b/configs/pvt/README.md
index 704746139..cbc10f6c8 100644
--- a/configs/pvt/README.md
+++ b/configs/pvt/README.md
@@ -81,16 +81,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                     | weight                                                                                            |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- |
-| pvt_tiny   | 13.23     | 8     | 128        | 224x224    | O2        | 212s          | 237.5   | 4311.58 | 74.88    | 92.12    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt/pvt_tiny-6676051f-910v2.ckpt) |
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                     | weight                                                                              |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------- |
-| pvt_tiny   | 13.23     | 8     | 128        | 224x224    | O2        | 192s          | 229.63  | 4459.35 | 74.81    | 92.18    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt/pvt_tiny-6abb953d.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- |
+| pvt_tiny   | 13.23     | 8     | 128        | 224x224    | O2        | 212s          | 231.81  | 4417.41 | 74.88    | 92.12    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt/pvt_tiny-6676051f-910v2.ckpt) |
 
 
 ### Notes
diff --git a/configs/pvtv2/README.md b/configs/pvtv2/README.md
index 37e74eefd..2d5bf8cce 100644
--- a/configs/pvtv2/README.md
+++ b/configs/pvtv2/README.md
@@ -82,16 +82,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                        | weight                                                                                                |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
-| pvt_v2_b0  | 3.67      | 8     | 128        | 224x224    | O2        | 323s          | 255.76  | 4003.75 | 71.25    | 90.50    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-d9cd9d6a-910v2.ckpt) |
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                        | weight                                                                                  |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
-| pvt_v2_b0  | 3.67      | 8     | 128        | 224x224    | O2        | 269s          | 269.38  | 3801.32 | 71.50    | 90.60    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-1c4f6683.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
+| pvt_v2_b0  | 3.67      | 8     | 128        | 224x224    | O2        | 323s          | 264.24  | 3875.26 | 71.25    | 90.50    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-d9cd9d6a-910v2.ckpt) |
 
 
 ### Notes
diff --git a/configs/regnet/README.md b/configs/regnet/README.md
index 4aeca99b7..6d0cd5598 100644
--- a/configs/regnet/README.md
+++ b/configs/regnet/README.md
@@ -83,16 +83,8 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 | model name     | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                                     |
-| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
-| regnet_x_800mf | 7.26      | 8     | 64         | 224x224    | O2        | 228s          | 50.74   | 10090.66 | 76.11    | 93.00    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/regnet/regnet_x_800mf-68fe1cca-910v2.ckpt) |
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name     | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                       |
-| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
-| regnet_x_800mf | 7.26      | 8     | 64         | 224x224    | O2        | 99s           | 42.49   | 12049.89 | 76.04    | 92.97    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/regnet/regnet_x_800mf-617227f4.ckpt) |
+| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
+| regnet_x_800mf | 7.26      | 8     | 64         | 224x224    | O2        | 228s          | 52.48   | 9756.09 | 76.11    | 93.00    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/regnet/regnet_x_800mf-68fe1cca-910v2.ckpt) |
 
 
 ### Notes
diff --git a/configs/repmlp/README.md b/configs/repmlp/README.md
index 578c5457e..137ba95ce 100644
--- a/configs/repmlp/README.md
+++ b/configs/repmlp/README.md
@@ -89,13 +89,6 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 *coming soon*
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                           | weight                                                                                    |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
-| repmlp_t224 | 38.30     | 8     | 128        | 224x224    | O2        | 289s          | 578.23  | 1770.92 | 76.71    | 93.30    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repmlp/repmlp_t224_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repmlp/repmlp_t224-8dbedd00.ckpt) |
-
 ### Notes
 
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/repvgg/README.md b/configs/repvgg/README.md
index 7eb5623a6..3997e06c7 100644
--- a/configs/repvgg/README.md
+++ b/configs/repvgg/README.md
@@ -93,19 +93,11 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                                |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
-| repvgg_a0  | 9.13      | 8     | 32         | 224x224    | O2        | 76s           | 24.12   | 10613.60 | 72.29    | 90.78    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) |
-| repvgg_a1  | 14.12     | 8     | 32         | 224x224    | O2        | 81s           | 28.29   | 9096.13  | 73.68    | 91.51    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
+| repvgg_a0  | 9.13      | 8     | 32         | 224x224    | O2        | 76s           | 25.2    | 10158.73 | 72.29    | 90.78    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) |
+| repvgg_a1  | 14.12     | 8     | 32         | 224x224    | O2        | 81s           | 28.31   | 9042.74  | 73.68    | 91.51    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) |
 
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                  |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
-| repvgg_a0  | 9.13      | 8     | 32         | 224x224    | O2        | 50s<br>       | 20.58   | 12439.26 | 72.19    | 90.75    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) |
-| repvgg_a1  | 14.12     | 8     | 32         | 224x224    | O2        | 29s           | 20.70   | 12367.15 | 74.19    | 91.89    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) |
-
 ### Notes
 
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/res2net/README.md b/configs/res2net/README.md
index af3a839f3..3d7a2b366 100644
--- a/configs/res2net/README.md
+++ b/configs/res2net/README.md
@@ -89,19 +89,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                           | weight                                                                                                 |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ |
-| res2net50  | 25.76     | 8     | 32         | 224x224    | O2        | 174s          | 39.6    | 6464.65 | 79.33    | 94.64    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/res2net/res2net50-aa758355-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                           | weight                                                                                   |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
-| res2net50  | 25.76     | 8     | 32         | 224x224    | O2        | 119s          | 39.68   | 6451.61 | 79.35    | 94.64    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/res2net/res2net50-f42cf71b.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------|---------| -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ |
+| res2net50  | 25.76     | 8     | 32         | 224x224    | O2        | 174s          | 40.63   | 6300.76 | 79.33    | 94.64    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/res2net/res2net50-aa758355-910v2.ckpt) |
 
 
 
diff --git a/configs/resnest/README.md b/configs/resnest/README.md
index 55285f134..31afbf330 100644
--- a/configs/resnest/README.md
+++ b/configs/resnest/README.md
@@ -84,17 +84,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                          | weight                                                                                   |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- |
-| resnest50  | 27.55     | 8     | 128        | 224x224    | O2        | 83s           | 244.92  | 4552.73 | 80.81    | 95.16    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnest/resnest50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnest/resnest50-f2e7fc9c.ckpt) |
-
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                          | weight                                                                                                  | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- |-------------------------------------------------------------------------------------------------| ------------------------------------------------------------------------------------------------------- | -------- | -------- |
+| resnest     | 8     | 128        | 224x224    | O2        | 279s          | 243.77  | 4200.68 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnest/resnest50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnest/resnest50-180_1251_v2.ckpt)     | 80.88    | 95.32    |
 
 
 ### Notes
diff --git a/configs/resnet/README.md b/configs/resnet/README.md
index d1906dac8..a071bd4c2 100644
--- a/configs/resnet/README.md
+++ b/configs/resnet/README.md
@@ -87,20 +87,11 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                               |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
-| resnet50   | 25.61     | 8     | 32         | 224x224    | O2        | 77s           | 31.9    | 8025.08 | 76.76    | 93.31    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnet/resnet50-f369a08d-910v2.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| resnet50   | 25.61     | 8     | 32         | 224x224    | O2        | 77s           | 33.09   | 7736.47 | 76.76    | 93.31    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnet/resnet50-f369a08d-910v2.ckpt) |
 
 
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                 |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- |
-| resnet50   | 25.61     | 8     | 32         | 224x224    | O2        | 43s           | 31.41   | 8150.27 | 76.69    | 93.50    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnet/resnet50-e0733ab8.ckpt) |
-
 ### Notes
 
 - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.
diff --git a/configs/resnetv2/README.md b/configs/resnetv2/README.md
index 10e1dc463..b2a1b672e 100644
--- a/configs/resnetv2/README.md
+++ b/configs/resnetv2/README.md
@@ -85,20 +85,9 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 
-| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                             | weight                                                                                                    |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
-| resnetv2_50 | 25.60     | 8     | 32         | 224x224    | O2        | 120s          | 32.19   | 7781.16 | 77.03    | 93.29    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnetv2/resnetv2_50-a0b9f7f8-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                             | weight                                                                                      |
-| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- |
-| resnetv2_50 | 25.60     | 8     | 32         | 224x224    | O2        | 52s           | 32.66   | 7838.33 | 76.90    | 93.37    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnetv2/resnetv2_50-3c2f143b.ckpt) |
+| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe                                                                                             | weight                                                                                                    |
+| ----------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| ---- | -------- | -------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
+| resnetv2_50 | 25.60     | 8     | 32         | 224x224    | O2        | 120s          | 33.2    | 7710.84 | 77.03    | 93.29    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnetv2/resnetv2_50-a0b9f7f8-910v2.ckpt) |
 
 
 
diff --git a/configs/resnext/README.md b/configs/resnext/README.md
index ac62a9e03..f2ecf3e34 100644
--- a/configs/resnext/README.md
+++ b/configs/resnext/README.md
@@ -90,19 +90,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                | weight                                                                                                       |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ |
-| resnext50_32x4d | 25.10     | 8     | 32         | 224x224    | O2        | 156s          | 44.61   | 5738.62 | 78.64    | 94.18    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnext/resnext50_32x4d-988f75bc-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name      | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                | weight                                                                                         |
-| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- |
-| resnext50_32x4d | 25.10     | 8     | 32         | 224x224    | O2        | 49s           | 37.22   | 6878.02 | 78.53    | 94.10    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnext/resnext50_32x4d-af8aba16.ckpt) |
+| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ |
+| resnext50_32x4d | 25.10     | 8     | 32         | 224x224    | O2        | 156s          | 45.49   | 5627.61 | 78.64    | 94.18    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnext/resnext50_32x4d-988f75bc-910v2.ckpt) |
 
 
 
diff --git a/configs/rexnet/README.md b/configs/rexnet/README.md
index 86c96592c..132016b91 100644
--- a/configs/rexnet/README.md
+++ b/configs/rexnet/README.md
@@ -76,11 +76,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                       | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------- | -------- | -------- |
+| rexnet      | 8     | 64         | 224x224    | O2        | 463s          | 122.56  | 4177.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml)       | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/rexnet/rexnet_09-00223eb4-910v2.ckpt)       | 76.15    | 92.89    |
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-*coming soon*
 
 ### Notes
 
diff --git a/configs/senet/README.md b/configs/senet/README.md
index 3b852d18c..3aa6bc35d 100644
--- a/configs/senet/README.md
+++ b/configs/senet/README.md
@@ -89,19 +89,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                                |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
-| seresnet18 | 11.80     | 8     | 64         | 224x224    | O2        | 90s           | 51.09   | 10021.53 | 72.05    | 90.59    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/senet/seresnet18-7b971c78-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                  |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
-| seresnet18 | 11.80     | 8     | 64         | 224x224    | O2        | 43s           | 44.40   | 11531.53 | 71.81    | 90.49    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/senet/seresnet18-7880643b.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
+| seresnet18 | 11.80     | 8     | 64         | 224x224    | O2        | 90s           | 50.43   | 10152.68 | 72.05    | 90.59    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/senet/seresnet18-7b971c78-910v2.ckpt) |
 
 
 
diff --git a/configs/shufflenetv1/README.md b/configs/shufflenetv1/README.md
index a45290e56..3dd2f9996 100644
--- a/configs/shufflenetv1/README.md
+++ b/configs/shufflenetv1/README.md
@@ -88,19 +88,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name          | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                       | weight                                                                                                                           |
-| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- |
-| shufflenet_v1_g3_05 | 0.73      | 8     | 64         | 224x224    | O2        | 191s          | 47.77   | 10718.02 | 57.08    | 79.89    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-56209ef3-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name          | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                       | weight                                                                                                             |
-| ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------ |
-| shufflenet_v1_g3_05 | 0.73      | 8     | 64         | 224x224    | O2        | 169s          | 40.62   | 12604.63 | 57.05    | 79.73    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-42cfe109.ckpt) |
+| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- |
+| shufflenet_v1_g3_05 | 0.73      | 8     | 64         | 224x224    | O2        | 191s          | 45.46   | 11262.64 | 57.08    | 79.89    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-56209ef3-910v2.ckpt) |
 
 
 
diff --git a/configs/shufflenetv2/README.md b/configs/shufflenetv2/README.md
index 820e962ba..cb47f141d 100644
--- a/configs/shufflenetv2/README.md
+++ b/configs/shufflenetv2/README.md
@@ -95,19 +95,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name         | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                       | weight                                                                                                                          |
-| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- |
-| shufflenet_v2_x0_5 | 1.37      | 8     | 64         | 224x224    | O2        | 100s          | 47.32   | 10819.95 | 60.65    | 82.26    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-39d05bb6-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name         | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                       | weight                                                                                                            |
-| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------- |
-| shufflenet_v2_x0_5 | 1.37      | 8     | 64         | 224x224    | O2        | 62s           | 41.87   | 12228.33 | 60.53    | 82.11    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-8c841061.ckpt) |
+| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- |
+| shufflenet_v2_x0_5 | 1.37      | 8     | 64         | 224x224    | O2        | 100s          | 48.15   | 10633.43 | 60.65    | 82.26    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-39d05bb6-910v2.ckpt) |
 
 
 
diff --git a/configs/sknet/README.md b/configs/sknet/README.md
index 9d2ece81e..940c1d14d 100644
--- a/configs/sknet/README.md
+++ b/configs/sknet/README.md
@@ -93,19 +93,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                                |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
-| skresnet18 | 11.97     | 8     | 64         | 224x224    | O2        | 134s          | 49.83   | 10274.93 | 72.85    | 90.83    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/sknet/skresnet18-9d8b1afc-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                  |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
-| skresnet18 | 11.97     | 8     | 64         | 224x224    | O2        | 60s           | 45.84   | 11169.28 | 73.09    | 91.20    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/sknet/skresnet18-868228e5.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
+| skresnet18 | 11.97     | 8     | 64         | 224x224    | O2        | 134s          | 46.83   | 10933.16 | 72.85    | 90.83    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/sknet/skresnet18-9d8b1afc-910v2.ckpt) |
 
 
 
diff --git a/configs/squeezenet/README.md b/configs/squeezenet/README.md
index f56c19331..2844a4247 100644
--- a/configs/squeezenet/README.md
+++ b/configs/squeezenet/README.md
@@ -90,19 +90,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name    | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                  | weight                                                                                                        |
-| ------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
-| squeezenet1_0 | 1.25      | 8     | 32         | 224x224    | O2        | 64s           | 23.48   | 10902.90 | 58.75    | 80.76    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/squeezenet/squeezenet1_0-24010b28-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name    | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s    | acc@top1 | acc@top5 | recipe                                                                                                  | weight                                                                                          |
-| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
-| squeezenet1_0 | 1.25      | 8     | 32         | 224x224    | O2        | 45s           | 22.36   | 11449.02 | 58.67    | 80.61    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/squeezenet/squeezenet1_0-eb911778.ckpt) |
+| ------------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+| squeezenet1_0 | 1.25      | 8     | 32         | 224x224    | O2        | 64s           | 23.7    | 10801.68 | 58.75    | 80.76    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/squeezenet/squeezenet1_0-24010b28-910v2.ckpt) |
 
 
 
diff --git a/configs/swintransformer/README.md b/configs/swintransformer/README.md
index 33c087b28..bed287058 100644
--- a/configs/swintransformer/README.md
+++ b/configs/swintransformer/README.md
@@ -98,19 +98,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                  | weight                                                                                              |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
-| swin_tiny  | 33.38     | 8     | 256        | 224x224    | O2        | 266s          | 466.6   | 4389.20 | 80.90    | 94.90    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swin/swin_tiny-72b3c5e6-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                  | weight                                                                                |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
-| swin_tiny  | 33.38     | 8     | 256        | 224x224    | O2        | 226s          | 454.49  | 4506.15 | 80.82    | 94.80    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swin/swin_tiny-0ff2f96d.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- |---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
+| swin_tiny  | 33.38     | 8     | 256        | 224x224    | O2        | 266s          | 454.01  | 4510.91 | 80.90    | 94.90    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swin/swin_tiny-72b3c5e6-910v2.ckpt) |
 
 
 
diff --git a/configs/swintransformerv2/README.md b/configs/swintransformerv2/README.md
index 8621c3a4a..dd7b348c8 100644
--- a/configs/swintransformerv2/README.md
+++ b/configs/swintransformerv2/README.md
@@ -91,20 +91,8 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name          | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                              | weight                                                                                                          |
-| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
-| swinv2_tiny_window8 | 28.78     | 8     | 128        | 256x256    | O2        | 385s          | 335.18  | 3055.07 | 81.38    | 95.46    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-70c5e903-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode.
-
-
-
-
-| model name          | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                              | weight                                                                                            |
-| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
-| swinv2_tiny_window8 | 28.78     | 8     | 128        | 256x256    | O2        | 273s          | 317.19  | 3228.35 | 81.42    | 95.43    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-3ef8b787.ckpt) |
-
+| ------------------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
+| swinv2_tiny_window8 | 28.78     | 8     | 128        | 256x256    | O2        | 385s          | 326.16  | 3139.56 | 81.38    | 95.46    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-70c5e903-910v2.ckpt) |
 
 
 ### Notes
diff --git a/configs/vgg/README.md b/configs/vgg/README.md
index 1b7544a01..500912beb 100644
--- a/configs/vgg/README.md
+++ b/configs/vgg/README.md
@@ -92,21 +92,9 @@ Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
 
 | model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                  | weight                                                                                         |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- |
-| vgg13      | 133.04    | 8     | 32         | 224x224    | O2        | 41s           | 30.52   | 8387.94 | 72.81    | 91.02    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) |
-| vgg19      | 143.66    | 8     | 32         | 224x224    | O2        | 53s           | 39.17   | 6535.61 | 75.24    | 92.55    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) |
-
-
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-
-
-| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                  | weight                                                                           |
-| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------| ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
-| vgg13      | 133.04    | 8     | 32         | 224x224    | O2        | 23s           | 55.20   | 4637.68 | 72.87    | 91.02    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg13-da805e6e.ckpt) |
-| vgg19      | 143.66    | 8     | 32         | 224x224    | O2        | 22s           | 67.42   | 3797.09 | 75.21    | 92.56    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg19-bedee7b6.ckpt) |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- |---------------|---------| ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- |
+| vgg13      | 133.04    | 8     | 32         | 224x224    | O2        | 41s           | 30.15   | 8490.87 | 72.81    | 91.02    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) |
+| vgg19      | 143.66    | 8     | 32         | 224x224    | O2        | 53s           | 38.72   | 6611.57 | 75.24    | 92.55    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) |
 
 
 
diff --git a/configs/visformer/README.md b/configs/visformer/README.md
index 8aa4530ac..2c2c815ae 100644
--- a/configs/visformer/README.md
+++ b/configs/visformer/README.md
@@ -83,15 +83,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-
-| model name     | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                                 | weight                                                                                          |
-| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- |
-| visformer_tiny | 10.33     | 8     | 128        | 224x224    | O2        | 137s          | 217.92  | 4698.97 | 78.28    | 94.15    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/visformer/visformer_tiny-daee0322.ckpt) |
-
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                       | weight                                                                                                  | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- |-----------| ------------- | ------- | ------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | -------- | -------- |
+| visformer   | 8     | 128        | 224x224    | O2        | 141s          | 207.35  | 4938.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/visformer/visformer_tiny-df995ba4-910v2.ckpt) | 74.93    | 92.55    |
 
 
 ### Notes
diff --git a/configs/vit/README.md b/configs/vit/README.md
index a0ae7b534..84e9f3a54 100644
--- a/configs/vit/README.md
+++ b/configs/vit/README.md
@@ -102,11 +102,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                                  | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -------- | -------- |
+| vit         | 8     | 256        | 224x224    | O2        | 225s          | 425.36  | 4814.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vit/vit_l32_224_ascend.yaml)             | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vit/vit_l_32_224-e0039f16-910v2.ckpt)             | 74.63    | 92.21    |
 
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-*coming soon*
 
 ### Notes
 
diff --git a/configs/volo/README.md b/configs/volo/README.md
index 26ee17272..93e99392e 100644
--- a/configs/volo/README.md
+++ b/configs/volo/README.md
@@ -84,7 +84,10 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 performance tested on ascend 910*(8p) with graph mode
 
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                     | weight                                                                                                  | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- |-----------| ------------- | ------- | ------- |--------------------------------------------------------------------------------------------| ------------------------------------------------------------------------------------------------------- | -------- | -------- |
+| volo        | 8     | 128        | 224x224    | O2        | 368s          | 230.05  | 4451.21 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/volo/volo_d1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/volo/volo_d1-177_1251_v2.ckpt)           | 82.97    | 96.21    |
+
 
 performance tested on ascend 910(8p) with graph mode
 
diff --git a/configs/xception/README.md b/configs/xception/README.md
index 20ed6d449..f7b93f3cc 100644
--- a/configs/xception/README.md
+++ b/configs/xception/README.md
@@ -87,11 +87,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                                  | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -------- | -------- |
+| xception   | 8     | 32         | 224x224    | O2        | 186s          | 83.40   | 3069.54 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xception/xception_ascend.yaml)   | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/xception/xception-174_5004_v2.ckpt)   | 76.31    | 92.80    |
 
 
 ### Notes
diff --git a/configs/xcit/README.md b/configs/xcit/README.md
index a794e1e12..b56d7cad0 100644
--- a/configs/xcit/README.md
+++ b/configs/xcit/README.md
@@ -81,11 +81,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 Experiments are tested on ascend 910* with mindspore 2.5.0 graph mode.
 
-*coming soon*
-
-Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode.
-
-*coming soon*
+| model name  | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | recipe                                                                                           | weight                                                                                                  | acc@top1 | acc@top5 |
+| ----------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -------- | -------- |
+| xcit       | 8     | 128        | 224x224    | O2        | 329s          | 233.86  | 4378.69 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_224_ascend.yaml)           | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-bd90776e-910v2.ckpt)           | 77.16    | 93.57    |
 
 ### Notes
 
diff --git a/mindcv/version.py b/mindcv/version.py
index e5ded93b9..9bcd67d23 100644
--- a/mindcv/version.py
+++ b/mindcv/version.py
@@ -1,2 +1,2 @@
 """version init"""
-__version__ = "0.3.0"
+__version__ = "0.5.0"