@@ -103,6 +103,66 @@ It is recommended to choose the PP-OCRv3 model (configuration file: [ch_PP-OCRv3
For more PP-OCR series models, please refer to [PP-OCR Series Model Library](./models_list_en.md)。
The PP-OCRv3 model uses the GTC strategy. The SAR branch has a large number of parameters. When the training data is a simple scene, the model is easy to overfit, resulting in poor fine-tuning effect. It is recommended to remove the GTC strategy. The configuration file of the model structure is modified as follows:
```yaml
Architecture:
model_type:rec
algorithm:SVTR
Transform:
Backbone:
name:MobileNetV1Enhance
scale:0.5
last_conv_stride:[1,2]
last_pool_type:avg
Neck:
name:SequenceEncoder
encoder_type:svtr
dims:64
depth:2
hidden_dims:120
use_guide:False
Head:
name:CTCHead
fc_decay:0.00001
Loss:
name:CTCLoss
Train:
dataset:
......
transforms:
# remove RecConAug
# - RecConAug:
# prob: 0.5
# ext_data_num: 2
# image_shape: [48, 320, 3]
# max_text_length: *max_text_length
-RecAug:
# modify Encode
-CTCLabelEncode:
-KeepKeys:
keep_keys:
-image
-label
-length
...
Eval:
dataset:
...
transforms:
...
- CTCLabelEncode:
-KeepKeys:
keep_keys:
-image
-label
-length
...
```
### 3.3 Training hyperparameter
...
...
@@ -165,3 +225,5 @@ Train:
### 3.4 training optimization
The training process does not happen overnight. After completing a stage of training evaluation, it is recommended to collect and analyze the badcase of the current model in the real scene, adjust the proportion of training data in a targeted manner, or further add synthetic data. Through multiple iterations of training, the model effect is continuously optimized.
If you modify the custom dictionary during training, since the parameters of the last layer of FC cannot be loaded, it is normal for acc=0 at the beginning of the iteration. Don't worry, loading the pre-trained model can still speed up the model convergence.
