PaddleOCR/configs/rec/rec_r31_sar.yml

100 lines
2.2 KiB
YAML

Global:
use_gpu: true
epoch_num: 5
log_smooth_window: 20
print_batch_step: 20
save_model_dir: ./sar_rec
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict90.txt
character_type: EN_symbol
max_text_length: 30
infer_mode: False
use_space_char: False
rm_symbol: True
save_res_path: ./output/rec/predicts_sar.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Piecewise
decay_epochs: [3, 4]
values: [0.001, 0.0001, 0.00001]
regularizer:
name: 'L2'
factor: 0
Architecture:
model_type: rec
algorithm: SAR
Transform:
Backbone:
name: ResNet31
Head:
name: SARHead
Loss:
name: SARLoss
PostProcess:
name: SARLabelDecode
Metric:
name: RecMetric
Train:
dataset:
name: SimpleDataSet
label_file_list: ['./train_data/train_list.txt']
data_dir: ./train_data/
ratio_list: 1.0
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SARLabelEncode: # Class handling label
- SARRecResizeImg:
image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
width_downsample_ratio: 0.25
- KeepKeys:
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 64
drop_last: True
num_workers: 8
use_shared_memory: False
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SARLabelEncode: # Class handling label
- SARRecResizeImg:
image_shape: [3, 48, 48, 160]
width_downsample_ratio: 0.25
- KeepKeys:
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 64
num_workers: 4
use_shared_memory: False