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chinese_cht_PP-OCRv3_rec.yml
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Global:
debug: false
use_gpu: true
#use_amp: true
epoch_num: 150
log_smooth_window: 20
print_batch_step: 100
save_model_dir: ./output/zh_TW/model/epoch
save_epoch_step: 3
eval_batch_step: [1000, 2000]
cal_metric_during_train: true
pretrained_model: ./pretrained_model/chinese_cht_PP-OCRv3_rec_train/best_accuracy
# checkpoints: ./output/zh_TW/model/epoch/latest
save_inference_dir: ./output/zh_TW/model/inference
use_visualdl: false
character_dict_path: ./output/zh_TW/keys.txt
max_text_length: &max_text_length 25
infer_mode: false
use_space_char: false
distributed: true
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.0002
warmup_epoch: 5
regularizer:
name: L2
factor: 3.0e-05
Architecture:
model_type: rec
algorithm: SVTR
Transform:
Backbone:
name: MobileNetV1Enhance
scale: 0.5
last_conv_stride: [1, 2]
last_pool_type: avg
Head:
name: MultiHead
head_list:
- CTCHead:
Neck:
name: svtr
dims: 64
depth: 2
hidden_dims: 120
use_guide: True
Head:
fc_decay: 0.00001
- SARHead:
enc_dim: 512
max_text_length: *max_text_length
Loss:
name: MultiLoss
loss_config_list:
- CTCLoss:
- SARLoss:
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
ignore_space: False
Train:
dataset:
name: SimpleDataSet
data_dir: ./
ext_op_transform_idx: 1
label_file_list:
- ./output/zh_TW/rec_gt_train.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecConAug:
prob: 0.5
ext_data_num: 2
image_shape: [48, 320, 3]
- RecAug:
- MultiLabelEncode:
- RecResizeImg:
image_shape: [3, 48, 320]
- KeepKeys:
keep_keys:
- image
- label_ctc
- label_sar
- length
- valid_ratio
loader:
shuffle: true
batch_size_per_card: 96
drop_last: true
num_workers: 16
Eval:
dataset:
name: SimpleDataSet
data_dir: ./
label_file_list:
- ./output/zh_TW/rec_gt_test.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- MultiLabelEncode:
- RecResizeImg:
image_shape: [3, 48, 320]
- KeepKeys:
keep_keys:
- image
- label_ctc
- label_sar
- length
- valid_ratio
loader:
shuffle: false
drop_last: false
batch_size_per_card: 96
num_workers: 16