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- Global:
- use_gpu: True
- epoch_num: 400
- log_smooth_window: 20
- print_batch_step: 10
- save_model_dir: ./output/rec/b3_rare_r34_none_gru/
- save_epoch_step: 3
- # evaluation is run every 5000 iterations after the 4000th iteration
- eval_batch_step: [0, 2000]
- cal_metric_during_train: True
- pretrained_model:
- checkpoints:
- save_inference_dir:
- use_visualdl: False
- infer_img: doc/imgs_words/ch/word_1.jpg
- # for data or label process
- character_dict_path:
- max_text_length: 25
- infer_mode: False
- use_space_char: False
- save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.txt
- Optimizer:
- name: Adam
- beta1: 0.9
- beta2: 0.999
- lr:
- learning_rate: 0.0005
- regularizer:
- name: 'L2'
- factor: 0.00000
- Architecture:
- model_type: rec
- algorithm: RARE
- Transform:
- name: TPS
- num_fiducial: 20
- loc_lr: 0.1
- model_name: large
- Backbone:
- name: ResNet
- layers: 34
- Neck:
- name: SequenceEncoder
- encoder_type: rnn
- hidden_size: 256 #96
- Head:
- name: AttentionHead # AttentionHead
- hidden_size: 256 #
- l2_decay: 0.00001
- Loss:
- name: AttentionLoss
- PostProcess:
- name: AttnLabelDecode
- Metric:
- name: RecMetric
- 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
- - AttnLabelEncode: # Class handling label
- - RecResizeImg:
- image_shape: [3, 32, 100]
- - KeepKeys:
- keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
- loader:
- shuffle: True
- batch_size_per_card: 256
- 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
- - AttnLabelEncode: # Class handling label
- - RecResizeImg:
- image_shape: [3, 32, 100]
- - 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: 8
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