Global:

device: gpu

epoch_num: 5

log_smooth_window: 20

print_batch_step: 10

output_dir: ./output/rec/sar

eval_epoch_step: [0, 1]

cal_metric_during_train: true

pretrained_model:

checkpoints:

use_tensorboard: false

infer_mode: false

infer_img: doc/imgs_words/en/word_1.png

character_dict_path: &character_dict_path ppocr/utils/dict90.txt

max_text_length: &max_text_length 30

use_space_char: &use_space_char False

rm_symbol: &rm_symbol True

Export:

export_dir:

export_shape: [ 1, 3, 48, 160 ]

dynamic_axes: []

Optimizer:

name: Adam

lr: 0.001

weight_decay: 0

LRScheduler:

name: MultiStepLR

milestones: [3,4]

warmup_epoch: 0

Architecture:

model_type: rec

algorithm: SAR

Transform:

Backbone:

name: ResNet31

Head:

name: SARHead

Loss:

name: SARLoss

PostProcess:

name: SARLabelDecode

character_dict_path: *character_dict_path

use_space_char: *use_space_char

rm_symbol: *rm_symbol

Metric:

name: RecMetric

Train:

dataset:

name: LMDBDataSet

data_dir: ./train_data/data_lmdb_release/training/

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

Eval:

dataset:

name: LMDBDataSet

data_dir: ./train_data/data_lmdb_release/validation/

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