PP-OCRv3 is a further upgrade of PP-OCRv2. This section introduces the training steps of the PP-OCRv3 detection model. For an introduction to the PP-OCRv3 strategy, refer to [document](../blog/PP-OCRv3_introduction.md).
## 2. Detection training
The PP-OCRv3 detection model is an upgrade of the [CML](https://arxiv.org/pdf/2109.03144.pdf) (Collaborative Mutual Learning) collaborative mutual learning text detection distillation strategy in PP-OCRv2. PP-OCRv3 further optimizes the detection teacher model and student model. Among them, when optimizing the teacher model, the PAN structure LK-PAN with a large receptive field and the DML (Deep Mutual Learning) distillation strategy are proposed; when optimizing the student model, the FPN structure RSE-FPN with a residual attention mechanism is proposed.
PP-OCRv3 detection training includes two steps:
- Step 1: Use DML distillation method to train detection teacher model
- Step 2: Use the teacher model obtained in step 1 to train a lightweight student model using CML method
### 2.1 Prepare data and operating environment
The training data uses icdar2015 data. For the steps of preparing the training set, refer to [ocr_dataset](./dataset/ocr_datasets.md).
For the preparation of the operating environment, refer to [document](./installation.md).
### 2.2 Train the teacher model
The configuration file for teacher model training is [ch_PP-OCRv3_det_dml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.5/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml). The Backbone, Neck, and Head of the teacher model structure are Resnet50, LKPAN, and DBHead respectively, and are trained using the DML distillation method. For a detailed introduction to the configuration file, refer to [Document](./knowledge_distillation.md).
The trained teacher model has a larger structure and higher accuracy, which is used to improve the accuracy of the student model.
**Extract teacher model parameters**
best_accuracy contains the parameters of two models, corresponding to Student and Student2 in the configuration file. The method to extract the parameters of Student is as follows:
The extracted model parameters can be used for further fine-tuning or distillation training of the model.
### 2.3 Training the student model
The configuration file for training the student model is [ch_PP-OCRv3_det_cml.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.5/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)
The teacher model trained in the previous section is used as supervision, and the CML method is used to train a lightweight student model.
Download the ImageNet pre-trained model of the student model:
best_accuracy contains the parameters of three models, corresponding to Student, Student2, and Teacher in the configuration file. The method to extract Student parameters is as follows:
The extracted Student parameters can be used for model deployment or further fine-tuning training.
## 3. Fine-tune training based on PP-OCRv3 detection
This section describes how to use the PP-OCRv3 detection model for fine-tune training in other scenarios.
Fine-tune training is applicable to three scenarios:
- Fine-tune training based on the CML distillation method is applicable to scenarios where the teacher model has higher accuracy than the PP-OCRv3 detection model in the usage scenario and a lightweight detection model is desired.
- Fine-tune training based on the PP-OCRv3 lightweight detection model does not require the training of the teacher model and is intended to improve the accuracy of the usage scenario based on the PP-OCRv3 detection model.
- Fine-tune training based on the DML distillation method is applicable to scenarios where the DML method is used to further improve accuracy.
**Finetune training based on CML distillation method**
s_params = {key[len("Student."):]: all_params[key] for key in all_params if "Student." in key}
# View the keys of the model weight parameters
print(s_params.keys())
# Save
paddle.save(s_params, "./student.pdparams")
```
Train using the configuration file [ch_PP-OCRv3_det_student.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.5/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml).
**Finetune training based on DML distillation method**
Take the Teacher model in ch_PP-OCRv3_det_distill_train as an example. First, extract the parameters of the Teacher structure. The method is as follows:
