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rec_r45_abinet.yml
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rec_r45_abinet.yml
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Global:
use_gpu: True
epoch_num: 10
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/r45_abinet/
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model: ./pretrain_models/abinet_vl_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_abinet.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.99
clip_norm: 20.0
lr:
name: Piecewise
decay_epochs: [6]
values: [0.0001, 0.00001]
regularizer:
name: 'L2'
factor: 0.
Architecture:
model_type: rec
algorithm: ABINet
in_channels: 3
Transform:
Backbone:
name: ResNet45
Head:
name: ABINetHead
use_lang: True
iter_size: 3
Loss:
name: CELoss
ignore_index: &ignore_index 100 # Must be greater than the number of character classes
PostProcess:
name: ABINetLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- ABINetRecAug:
- ABINetLabelEncode: # Class handling label
ignore_index: *ignore_index
- ABINetRecResizeImg:
image_shape: [3, 32, 128]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 96
drop_last: True
num_workers: 4
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- ABINetLabelEncode: # Class handling label
ignore_index: *ignore_index
- ABINetRecResizeImg:
image_shape: [3, 32, 128]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 4
use_shared_memory: False