The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
```
```
# -c Set the yml configuration file of the training algorithm, you need to write the path of the training model to be converted into the Global.checkpoints parameter in the configuration file, without adding the file suffixes .pdmodel, .pdopt or .pdparams.
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
# -o Set the address where the converted model will be saved.
The recognition model is converted to the inference model in the same way as the detection, as follows:
The recognition model is converted to the inference model in the same way as the detection, as follows:
```
```
# -c Set the yml configuration file of the training algorithm, you need to write the path of the training model to be converted into the Global.checkpoints parameter in the configuration file, without adding the file suffixes .pdmodel, .pdopt or .pdparams.
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
# -o Set the address where the converted model will be saved.
The angle classification model is converted to the inference model in the same way as the detection, as follows:
The angle classification model is converted to the inference model in the same way as the detection, as follows:
```
```
# -c Set the yml configuration file of the training algorithm, you need to write the path of the training model to be converted into the Global.checkpoints parameter in the configuration file, without adding the file suffixes .pdmodel, .pdopt or .pdparams.
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
# -o Set the address where the converted model will be saved.
First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
```
```
# -c Set the yml configuration file of the training algorithm, you need to write the path of the training model to be converted into the Global.checkpoints parameter in the configuration file, without adding the file suffixes .pdmodel, .pdopt or .pdparams.
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
# -o Set the address where the converted model will be saved.
@@ -176,7 +176,7 @@ The visualized text detection results are saved to the `./inference_results` fol
...
@@ -176,7 +176,7 @@ The visualized text detection results are saved to the `./inference_results` fol
First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
```
```
# -c Set the yml configuration file of the training algorithm, you need to write the path of the training model to be converted into the Global.checkpoints parameter in the configuration file, without adding the file suffixes .pdmodel, .pdopt or .pdparams.
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
# -o Set the address where the converted model will be saved.
@@ -200,7 +200,7 @@ The visualized text detection results are saved to the `./inference_results` fol
...
@@ -200,7 +200,7 @@ The visualized text detection results are saved to the `./inference_results` fol
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
```
```
# -c Set the yml configuration file of the training algorithm, you need to write the path of the training model to be converted into the Global.checkpoints parameter in the configuration file, without adding the file suffixes .pdmodel, .pdopt or .pdparams.
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
# -o Set the address where the converted model will be saved.
@@ -220,6 +220,9 @@ The visualized text detection results are saved to the `./inference_results` fol
...
@@ -220,6 +220,9 @@ The visualized text detection results are saved to the `./inference_results` fol
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
```
```
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
@@ -265,7 +268,7 @@ Taking STAR-Net as an example, we introduce the recognition model inference base
...
@@ -265,7 +268,7 @@ Taking STAR-Net as an example, we introduce the recognition model inference base
First, convert the model saved in the STAR-Net text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](link)). It can be converted as follow:
First, convert the model saved in the STAR-Net text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](link)). It can be converted as follow:
```
```
# -c Set the yml configuration file of the training algorithm, you need to write the path of the training model to be converted into the Global.checkpoints parameter in the configuration file, without adding the file suffixes .pdmodel, .pdopt or .pdparams.
-c Set the yml configuration file of the algorithm, you need to set `Global.load_static_weights=False`, and write the path of the training model to be converted under the `Global.pretrained_model` parameter in the configuration file, without adding the file suffix .pdmodel, .pdopt or .pdparams.
# -o Set the address where the converted model will be saved.
# -o Set the address where the converted model will be saved.
