现在OCRv5的识别模型，为何微调训练后的模型识别精度严重下降，之前可以识别的文本现在都会识别错误，是要进行什么冻结操作吗？

[Wu10Wu]

Global:
model_name: PP-OCRv5_server_rec # To use static model for inference.
debug: false
use_gpu: true
epoch_num: 200
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/PP-OCRv5_server_rec
save_epoch_step: 1
eval_batch_step: [0, 500]
cal_metric_during_train: true
calc_epoch_interval: 1
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: false
infer_img: doc/imgs_words/ch/word_1.jpg
character_dict_path: ./ppocr/utils/dict/ppocrv5_dict.txt
max_text_length: &max_text_length 70
infer_mode: false
use_space_char: true
distributed: true
save_res_path: ./output/rec/predicts_ppocrv5.txt
d2s_train_image_shape: [3, 48, 320]

Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.00001
warmup_epoch: 1
regularizer:
name: L2
factor: 3.0e-05

Architecture:
model_type: rec
algorithm: SVTR_HGNet
Transform:
Backbone:
name: PPHGNetV2_B4
text_rec: True
Head:
name: MultiHead
head_list:
- CTCHead:
Neck:
name: svtr
dims: 120
depth: 2
hidden_dims: 120
kernel_size: [1, 3]
use_guide: True
Head:
fc_decay: 0.00001
- NRTRHead:
nrtr_dim: 384
max_text_length: *max_text_length

Loss:
name: MultiLoss
loss_config_list:
- CTCLoss:
- NRTRLoss:

PostProcess:
name: CTCLabelDecode

Metric:
name: RecMetric
main_indicator: acc

Train:
dataset:
name: MultiScaleDataSet
ds_width: false
data_dir: ./train_data/
ext_op_transform_idx: 1
label_file_list:
- ./train_data/train_list.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecAug:
- MultiLabelEncode:
gtc_encode: NRTRLabelEncode
- KeepKeys:
keep_keys:
- image
- label_ctc
- label_gtc
- length
- valid_ratio
sampler:
name: MultiScaleSampler
scales: [[320, 32], [320, 48], [320, 64]]
first_bs: &bs 128
fix_bs: false
divided_factor: [8, 16] # w, h
is_training: True
loader:
shuffle: true
batch_size_per_card: *bs
drop_last: true
num_workers: 16
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data
label_file_list:
- ./train_data/val_list.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- MultiLabelEncode:
gtc_encode: NRTRLabelEncode
- RecResizeImg:
image_shape: [3, 48, 320]
- KeepKeys:
keep_keys:
- image
- label_ctc
- label_gtc
- length
- valid_ratio
loader:
shuffle: false
drop_last: false
batch_size_per_card: 128
num_workers: 4

这是我现在的.yaml文件，现在微调是为了解决模型识别不到数字与字母之间空格的问题，训练后发现空格可以识别到了，但是现在模型检测识别原先的文本会出现很多错误。
请问应该如何调整训练方式，需要冻结操作吗？还是其他什么原因，该怎么调整？