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- Global:
- use_gpu: True
- epoch_num: 6
- log_smooth_window: 20
- print_batch_step: 10
- save_model_dir: ./output/rec/seed
- 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_en/word_10.png
- # for data or label process
- character_dict_path: ppocr/utils/EN_symbol_dict.txt
- max_text_length: 100
- infer_mode: False
- use_space_char: False
- save_res_path: ./output/rec/predicts_seed.txt
- Optimizer:
- name: Adadelta
- weight_deacy: 0.0
- momentum: 0.9
- lr:
- name: Piecewise
- decay_epochs: [4, 5]
- values: [1.0, 0.1, 0.01]
- regularizer:
- name: 'L2'
- factor: 2.0e-05
- Architecture:
- model_type: rec
- algorithm: SEED
- Transform:
- name: STN_ON
- tps_inputsize: [32, 64]
- tps_outputsize: [32, 100]
- num_control_points: 20
- tps_margins: [0.05,0.05]
- stn_activation: none
- Backbone:
- name: ResNet_ASTER
- Head:
- name: AsterHead # AttentionHead
- sDim: 512
- attDim: 512
- max_len_labels: 100
- Loss:
- name: AsterLoss
- PostProcess:
- name: SEEDLabelDecode
- Metric:
- name: RecMetric
- main_indicator: acc
- is_filter: True
- Train:
- dataset:
- name: LMDBDataSet
- data_dir: ./train_data/data_lmdb_release/training/
- transforms:
- - Fasttext:
- path: "./cc.en.300.bin"
- - DecodeImage: # load image
- img_mode: BGR
- channel_first: False
- - SEEDLabelEncode: # Class handling label
- - RecResizeImg:
- character_dict_path:
- image_shape: [3, 64, 256]
- padding: False
- - KeepKeys:
- keep_keys: ['image', 'label', 'length', 'fast_label'] # dataloader will return list in this order
- loader:
- shuffle: True
- batch_size_per_card: 256
- drop_last: True
- num_workers: 6
- Eval:
- dataset:
- name: LMDBDataSet
- data_dir: ./train_data/data_lmdb_release/evaluation/
- transforms:
- - DecodeImage: # load image
- img_mode: BGR
- channel_first: False
- - SEEDLabelEncode: # Class handling label
- - RecResizeImg:
- character_dict_path:
- image_shape: [3, 64, 256]
- padding: False
- - KeepKeys:
- keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
- loader:
- shuffle: False
- drop_last: True
- batch_size_per_card: 256
- num_workers: 4
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