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- Global:
- use_gpu: True
- epoch_num: 20
- log_smooth_window: 20
- print_batch_step: 10
- save_model_dir: ./output/rec/vitstr_none_ce/
- save_epoch_step: 1
- # evaluation is run every 2000 iterations after the 0th 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: 25
- infer_mode: False
- use_space_char: False
- save_res_path: ./output/rec/predicts_vitstr.txt
- Optimizer:
- name: Adadelta
- epsilon: 1.e-8
- rho: 0.95
- clip_norm: 5.0
- lr:
- learning_rate: 1.0
- Architecture:
- model_type: rec
- algorithm: ViTSTR
- in_channels: 1
- Transform:
- Backbone:
- name: ViTSTR
- scale: tiny
- Neck:
- name: SequenceEncoder
- encoder_type: reshape
- Head:
- name: CTCHead
- Loss:
- name: CELoss
- with_all: True
- ignore_index: &ignore_index 0 # Must be zero or greater than the number of character classes
- PostProcess:
- name: ViTSTRLabelDecode
- 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
- - ViTSTRLabelEncode: # Class handling label
- ignore_index: *ignore_index
- - GrayRecResizeImg:
- image_shape: [224, 224] # W H
- resize_type: PIL # PIL or OpenCV
- inter_type: 'Image.BICUBIC'
- scale: false
- - KeepKeys:
- keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
- loader:
- shuffle: True
- batch_size_per_card: 48
- 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
- - ViTSTRLabelEncode: # Class handling label
- ignore_index: *ignore_index
- - GrayRecResizeImg:
- image_shape: [224, 224] # W H
- resize_type: PIL # PIL or OpenCV
- inter_type: 'Image.BICUBIC'
- scale: false
- - 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: 2
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