diff --git a/configs/rec/crnn/README.md b/configs/rec/crnn/README.md
index 6a0a11e0c..909811633 100644
--- a/configs/rec/crnn/README.md
+++ b/configs/rec/crnn/README.md
@@ -47,10 +47,10 @@ According to our experiments, the training (following the steps in [Model Traini
-| **model name** | **backbone** | **cards** | **batch size** | **train dataset** | **model params** | **jit level** | **graph compile** | **ms/step** | **img/s** | **avg eval accuracy** | **recipe** | **weight** |
-|:--------------:|:---------:|:--------------:| :-----: |:-----------------:|:----------------:|:---------------------:|:-------:|:-----------:|:---------:|:---------------------:|:-------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| CRNN | VGG7 | 8 | 16 | MJ+ST | 8.72 M | O2| 67.18 s | 22.06 | 5802.71 | 82.03% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c-573dbd61.mindir) |
-| CRNN | ResNet34_vd | 8 | 64 | MJ+ST | 24.48 M | O2| 201.54 s | 76.48 | 6694.84 | 84.45% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07-eb10a0c9.mindir) |
+| **model name** | **backbone** | **train dataset** | **params(M)** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **accuracy** | **recipe** | **weight** |
+|:--------------:|:---------:|:--------------:|:-------------:|:-----------------:|:----------------:|:---------------------:|:-------:|:-----------:|:---------:|:---------------------:|:-------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| CRNN | VGG7 | MJ+ST | 8.72 | 8 | 16 | O2| 67.18 s | 22.06 | 5802.71 | 82.03% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c-573dbd61.mindir) |
+| CRNN | ResNet34_vd | MJ+ST | 24.48 | 8 | 64 | O2| 201.54 s | 76.48 | 6694.84 | 84.45% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07-eb10a0c9.mindir) |
- Detailed accuracy results for each benchmark dataset (IC03, IC13, IC15, IIIT, SVT, SVTP, CUTE):
@@ -66,9 +66,9 @@ According to our experiments, the training (following the steps in [Model Traini
#### Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
-| **model name** | **backbone** | **cards** | **batch size** | **train dataset** | **model params** | **jit level** | **graph compile** | **ms/step** | **img/s** | **avg eval accuracy** |**recipe** | **weight** |
-|:--------------:|:---------:|:--------------:|:------------:|:-----------------:|:----------------:|:---------------------:|:----------------------------:|:-----------:|:---------:|:---------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
-| CRNN | VGG7 | 8 | 16 | MJ+ST | 8.72 M | O2 | 94.36 s | 14.76 | 8672.09 | 81.31% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/crnn/crnn_vgg7-6faf1b2d-910v2.ckpt) |
+| **model name** | **backbone** | **train dataset** | **params(M)** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **accuracy** |**recipe** | **weight** |
+|:--------------:|:---------:|:--------------:|:-------------:|:-----------------:|:----------------:|:---------------------:|:----------------------------:|:-----------:|:---------:|:---------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
+| CRNN | VGG7 | MJ+ST | 8.72 | 8 | 16 | O2 | 94.36 s | 14.76 | 8672.09 | 81.31% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/crnn/crnn_vgg7-6faf1b2d-910v2.ckpt) |
@@ -81,10 +81,10 @@ Experiments are tested on ascend 310P with mindspore lite 2.3.1 graph mode
-| model name | backbone | batch size | params | test dataset | img/s |
-|:----------:|:----------:|:-----------:|:-------:|:------------:|:------:|
-| CRNN | ResNet34_vd | 1 | 24.48 M | IC15 | 361.09 |
-| CRNN | ResNet34_vd | 1 | 24.48 M | SVT | 274.67 |
+| model name | backbone | test dataset | params(M) | cards | batch size | **jit level** | **graph compile** | img/s |
+|:----------:|:----------:|:-----:|:---------:|:-------:|:------------:|:------:|:-----------------:|:------:|
+| CRNN | ResNet34_vd | IC15 | 24.48 | 1 | 1 | O2 | 10.46 s | 361.09 |
+| CRNN | ResNet34_vd | SVT | 24.48 | 1 | 1 | O2 | 10.31 s | 274.67 |
diff --git a/configs/rec/crnn/README_CN.md b/configs/rec/crnn/README_CN.md
index 03f3f9980..c6556c240 100644
--- a/configs/rec/crnn/README_CN.md
+++ b/configs/rec/crnn/README_CN.md
@@ -48,10 +48,10 @@ Table Format:
-| **model name** | **backbone** | **cards** | **batch size** | **train dataset** | **model params** | **jit level** | **graph compile** | **ms/step** | **img/s** | **avg eval accuracy** | **recipe** |**weight** |
-|:--------------:|:---------:|:--------------:| :-----: |:-----------------:|:----------------:|:---------------------:|:-------:|:-----------:|:---------:|:---------------------:|:-------------------------:|:----------------------------------------------------:|
-| CRNN | VGG7 | 8 | 16 | MJ+ST | 8.72 M | O2| 67.18 s | 22.06 | 5802.71 | 82.03% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c-573dbd61.mindir) |
-| CRNN | ResNet34_vd | 8 | 64 | MJ+ST | 24.48 M | O2| 201.54 s | 76.48 | 6694.84 | 84.45% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07-eb10a0c9.mindir) |
+| **model name** | **backbone** | **train dataset** | **params(M)** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **accuracy** | **recipe** | **weight** |
+|:--------------:|:---------:|:--------------:|:-------------:|:-----------------:|:----------------:|:---------------------:|:-------:|:-----------:|:---------:|:---------------------:|:-------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| CRNN | VGG7 | MJ+ST | 8.72 | 8 | 16 | O2| 67.18 s | 22.06 | 5802.71 | 82.03% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c-573dbd61.mindir) |
+| CRNN | ResNet34_vd | MJ+ST | 24.48 | 8 | 64 | O2| 201.54 s | 76.48 | 6694.84 | 84.45% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07-eb10a0c9.mindir) |
- 在各个基准数据集（IC03，IC13，IC15，IIIT，SVT，SVTP，CUTE）上的准确率：
@@ -70,9 +70,9 @@ Table Format:
-| **model name** | **backbone** | **cards** | **batch size** | **train dataset** | **model params** | **jit level** | **graph compile** | **ms/step** | **img/s** | **avg eval accuracy** |**recipe** | **weight** |
-|:--------------:|:---------:|:--------------:|:------------:|:-----------------:|:----------------:|:---------------------:|:----------------------------:|:-----------:|:---------:|:---------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
-| CRNN | VGG7 | 8 | 16 | MJ+ST | 8.72 M | O2 | 94.36 s | 14.76 | 8672.09 | 81.31% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/crnn/crnn_vgg7-6faf1b2d-910v2.ckpt) |
+| **model name** | **backbone** | **train dataset** | **params(M)** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **accuracy** |**recipe** | **weight** |
+|:--------------:|:---------:|:--------------:|:-------------:|:-----------------:|:----------------:|:---------------------:|:----------------------------:|:-----------:|:---------:|:---------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
+| CRNN | VGG7 | MJ+ST | 8.72 | 8 | 16 | O2 | 94.36 s | 14.76 | 8672.09 | 81.31% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/crnn/crnn_vgg7-6faf1b2d-910v2.ckpt) |
@@ -84,10 +84,10 @@ Table Format:
-| model name | backbone | batch size | params | test dataset | img/s |
-|:----------:|:----------:|:-----------:|:-------:|:------------:|:------:|
-| CRNN | ResNet34_vd | 1 | 24.48 M | IC15 | 361.09 |
-| CRNN | ResNet34_vd | 1 | 24.48 M | SVT | 274.67 |
+| model name | backbone | test dataset | params(M) | cards | batch size | **jit level** | **graph compile** | img/s |
+|:----------:|:----------:|:-----:|:---------:|:-------:|:------------:|:------:|:-----------------:|:------:|
+| CRNN | ResNet34_vd | IC15 | 24.48 | 1 | 1 | O2 | 10.46 s | 361.09 |
+| CRNN | ResNet34_vd | SVT | 24.48 | 1 | 1 | O2 | 10.31 s | 274.67 |
diff --git a/configs/rec/svtr/README.md b/configs/rec/svtr/README.md
index a1e4b91de..8d24eb177 100644
--- a/configs/rec/svtr/README.md
+++ b/configs/rec/svtr/README.md
@@ -33,9 +33,12 @@ Table Format:
### Requirements
+
+
| mindspore | ascend driver | firmware | cann toolkit/kernel
|:----------|:--- | :-- |:--
| 2.3.1 | 24.1.RC2 | 7.3.0.1.231 | 8.0.RC2.beta1
+
### Accuracy
@@ -45,10 +48,10 @@ According to our experiments, the evaluation results on public benchmark dataset
-| **model name** | **cards** | **batch size** |**jit level** | **graph compile** | **ms/step** | **img/s** | **avg accuracy** | **recipe** | **weight** |
+| **model name** | **cards** | **batch size** |**jit level** | **graph compile** | **ms/step** | **img/s** | **accuracy** | **recipe** | **weight** |
|:--------------:|:---------:|:--------------:|:----------------:|:-----------------:|:-----------------:|:-------------:|:---------:|:---------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| SVTR-Tiny | 4 | 512 |O2| 226.86 s | 49.38 ms/step | 4560 | 90.23% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3-86ece8c8.mindir) |
-| SVTR-Tiny-8P | 8 | 512 |O2| 230.74 s | 55.16 ms/step | 9840 | 90.32% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6.ckpt) \| [mindir](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6-255191ef.mindir) |
+| SVTR-Tiny | 4 | 512 |O2| 226.86 s | 49.38 | 4560 | 90.23% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3-86ece8c8.mindir) |
+| SVTR-Tiny-8P | 8 | 512 |O2| 230.74 s | 55.16 | 9840 | 90.32% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6.ckpt) \| [mindir](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6-255191ef.mindir) |
Detailed accuracy results for each benchmark dataset
@@ -377,9 +380,9 @@ After training, evaluation results on the benchmark test set are as follows, whe
-| **model name** | **cards** | **batch size** | **language** | **jit level** | **graph compile** | **ms/step** | **img/s** | **scene** | **web** | **document** | **recipe** | **weight** |
-|:--------------:|:---------:|:--------------:| :--------: |:-------------:|:-----------------:|:---------:|:-------:|:------------:|:-----------:|:---------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| SVTR-Tiny | 4 | 256 | Chinese | O2 | 235.1 s| 37.75 | 1580 | 65.93% | 69.64% | 98.01% | [svtr_tiny_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4-3e495768.mindir) |
+| **model name** | **cards** | **batch size** | **languages** | **jit level** | **graph compile** | **ms/step** | **img/s** | **scene** | **web** | **document** | **recipe** | **weight** |
+|:--------------:|:---------:|:--------------:|:-------------:|:-------------:|:-----------------:|:---------:|:-------:|:------------:|:-----------:|:---------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| SVTR-Tiny | 4 | 256 | Chinese | O2 | 235.1 s| 37.75 | 1580 | 65.93% | 69.64% | 98.01% | [svtr_tiny_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4-3e495768.mindir) |
### Training with Custom Datasets
diff --git a/configs/rec/svtr/README_CN.md b/configs/rec/svtr/README_CN.md
index 8bcc6de41..e370e0ac0 100644
--- a/configs/rec/svtr/README_CN.md
+++ b/configs/rec/svtr/README_CN.md
@@ -44,10 +44,10 @@ Table Format:
-| **model name** | **cards** | **batch size** |**jit level** | **graph compile** | **ms/step** | **img/s** | **avg accuracy** | **recipe** | **weight** |
+| **model name** | **cards** | **batch size** |**jit level** | **graph compile** | **ms/step** | **img/s** | **accuracy** | **recipe** | **weight** |
|:--------------:|:---------:|:--------------:|:----------------:|:-----------------:|:-----------------:|:-------------:|:---------:|:---------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| SVTR-Tiny | 4 | 512 |O2| 226.86 s | 49.38 ms/step | 4560 | 90.23% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3-86ece8c8.mindir) |
-| SVTR-Tiny-8P | 8 | 512 |O2| 230.74 s | 55.16 ms/step | 9840 | 90.32% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6.ckpt) \| [mindir](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6-255191ef.mindir) |
+| SVTR-Tiny | 4 | 512 |O2| 226.86 s | 49.38 | 4560 | 90.23% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny-950be1c3-86ece8c8.mindir) |
+| SVTR-Tiny-8P | 8 | 512 |O2| 230.74 s | 55.16 | 9840 | 90.32% | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6.ckpt) \| [mindir](https://download-mindspore.osinfra.cn/toolkits/mindocr/svtr/svtr_tiny_8p-0afc75d6-255191ef.mindir) |
在各个基准数据集上的准确率
@@ -373,9 +373,9 @@ mpirun --allow-run-as-root -n 4 python tools/train.py --config configs/rec/svtr/
-| **model name** | **cards** | **batch size** | **language** | **jit level** | **graph compile** | **ms/step** | **img/s** | **scene** | **web** | **document** | **recipe** | **weight** |
-|:--------------:|:---------:|:--------------:| :--------: |:-------------:|:-----------------:|:---------:|:-------:|:------------:|:-----------:|:---------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| SVTR-Tiny | 4 | 256 | Chinese | O2 | 235.1 s| 65.93% | 37.75 | 1580 | 69.64% | 98.01% | [svtr_tiny_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4-3e495768.mindir) |
+| **model name** | **cards** | **batch size** | **languages** | **jit level** | **graph compile** | **ms/step** | **img/s** | **scene** | **web** | **document** | **recipe** | **weight** |
+|:--------------:|:---------:|:--------------:|:-------------:|:-------------:|:-----------------:|:---------:|:-------:|:------------:|:-----------:|:---------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| SVTR-Tiny | 4 | 256 | Chinese | O2 | 235.1 s| 65.93% | 37.75 | 1580 | 69.64% | 98.01% | [svtr_tiny_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_tiny_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/svtr/svtr_tiny_ch-2ee6ade4-3e495768.mindir) |
### 使用自定义数据集进行训练