-[2.6. Training on other platform(Windows/macOS/Linux DCU)](#26-training-on-other-platformwindowsmacoslinux-dcu)
-[2.7. Fine-tuning](#27-fine-tuning)
-[2.7.1 Dataset](#271-dataset)
-[2.7.2 model selection](#272-model-selection)
-[2.7.3 Training hyperparameter selection](#273-training-hyperparameter-selection)
-[3. Evaluation and Test](#3-evaluation-and-test)
-[3.1. Evaluation](#31-evaluation)
-[3.2. Test table structure recognition effect](#32-test-table-structure-recognition-effect)
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@@ -226,8 +229,40 @@ Running on a DCU device requires setting the environment variable `export HIP_VI
## 2.7. Fine-tuning
In the actual use process, it is recommended to load the officially provided pre-training model and fine-tune it in your own data set. For the fine-tuning method of the table recognition model, please refer to: [Model fine-tuning tutorial](./finetune.md).
### 2.7.1 Dataset
Data number: It is recommended to prepare at least 2000 table recognition datasets for model fine-tuning.
### 2.7.2 model selection
It is recommended to choose the SLANet model (configuration file: [SLANet_ch.yml](../../configs/table/SLANet_ch.yml), pre-training model: [ch_ppstructure_mobile_v2.0_SLANet_train.tar](https://paddleocr.bj.bcebos .com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar)) for fine-tuning, its accuracy and generalization performance is the best Chinese table pre-training model currently available.
For more table recognition models, please refer to [PP-Structure Series Model Library](../../ppstructure/docs/models_list.md).
### 2.7.3 Training hyperparameter selection
When fine-tuning the model, the most important hyperparameters are the pretrained model path `pretrained_model`, the learning rate `learning_rate`, and some configuration files are shown below.
```yaml
Global:
pretrained_model: ./ch_ppstructure_mobile_v2.0_SLANet_train/best_accuracy.pdparams # Pre-trained model path
Optimizer:
lr:
name: Cosine
learning_rate: 0.001 #
warmup_epoch: 0
regularizer:
name: 'L2'
factor: 0
```
In the above configuration file, you first need to specify the `pretrained_model` field as the `best_accuracy.pdparams` file path.
The configuration file provided by PaddleOCR is for 4-card training (equivalent to a total batch size of `4*48=192`) and no pre-trained model is loaded. Therefore, in your scenario, the learning rate is the same as the total The batch size needs to be adjusted linearly, for example
* If your scenario is single card training, single card batch_size=48, then the total batch_size=48, it is recommended to adjust the learning rate to about `0.00025`.
* If your scenario is for single-card training, due to memory limitations, you can only set batch_size=32 for a single card, then the total batch_size=32, it is recommended to adjust the learning rate to about `0.00017`.
