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rec_resnet_rfl_visual.yml
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rec_resnet_rfl_visual.yml
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Global:
use_gpu: True
epoch_num: 6
log_smooth_window: 20
print_batch_step: 50
save_model_dir: ./output/rec/rec_resnet_rfl_visual/
save_epoch_step: 1
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 5000]
cal_metric_during_train: False
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/rec_resnet_rfl_visual.txt
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
weight_decay: 0.0
clip_norm_global: 5.0
lr:
name: Piecewise
decay_epochs : [3, 4, 5]
values : [0.001, 0.0003, 0.00009, 0.000027]
Architecture:
model_type: rec
algorithm: RFL
in_channels: 1
Transform:
name: TPS
num_fiducial: 20
loc_lr: 1.0
model_name: large
Backbone:
name: ResNetRFL
use_cnt: True
use_seq: False
Neck:
name: RFAdaptor
use_v2s: False
use_s2v: False
Head:
name: RFLHead
in_channels: 512
hidden_size: 256
batch_max_legnth: 25
out_channels: 38
use_cnt: True
use_seq: False
Loss:
name: RFLLoss
PostProcess:
name: RFLLabelDecode
Metric:
name: CNTMetric
main_indicator: acc
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- RFLLabelEncode: # Class handling label
- RFLRecResizeImg:
image_shape: [1, 32, 100]
padding: false
interpolation: 2
- KeepKeys:
keep_keys: ['image', 'label', 'length', 'cnt_label'] # 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/evaluation
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- RFLLabelEncode: # Class handling label
- RFLRecResizeImg:
image_shape: [1, 32, 100]
padding: false
interpolation: 2
- KeepKeys:
keep_keys: ['image', 'label', 'length', 'cnt_label'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 8