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- Global:
- device: gpu
- epoch_num: 10
- log_smooth_window: 20
- print_batch_step: 10
- output_dir: ./output/rec/u14m_filter/vit_busnet_pretraining/
- eval_epoch_step: [0, 1]
- eval_batch_step: [0, 500]
- cal_metric_during_train: True
- pretrained_model:
- checkpoints:
- use_tensorboard: false
- infer_img:
- # for data or label process
- character_dict_path: ./tools/utils/EN_symbol_dict.txt
- max_text_length: 25
- use_space_char: False
- save_res_path: ./output/rec/u14m_filter/predicts_vit_busnet_pretraining.txt
- grad_clip_val: 20
- use_amp: True
- Optimizer:
- name: Adam
- lr: 0.00053 # 4gpus bs256/gpu
- weight_decay: 0.0
- filter_bias_and_bn: False
- LRScheduler:
- name: MultiStepLR
- milestones: [6]
- gamma: 0.1
- Architecture:
- model_type: rec
- algorithm: BUSBet
- Transform:
- Encoder:
- name: ViT
- img_size: [32,128]
- patch_size: [4, 8]
- embed_dim: 384
- depth: 12
- num_heads: 6
- mlp_ratio: 4
- qkv_bias: True
- Decoder:
- name: BUSDecoder
- nhead: 6
- num_layers: 6
- dim_feedforward: 1536
- ignore_index: &ignore_index 100
- pretraining: True
- Loss:
- name: ABINetLoss
- ignore_index: *ignore_index
- PostProcess:
- name: ABINetLabelDecode
- Metric:
- name: RecMetric
- main_indicator: acc
- is_filter: True
- Train:
- dataset:
- name: LMDBDataSet
- data_dir: ../Union14M-L-LMDB-Filtered
- transforms:
- - DecodeImagePIL: # load image
- img_mode: RGB
- - PARSeqAugPIL:
- - ABINetLabelEncode:
- ignore_index: *ignore_index
- - RecTVResize:
- image_shape: [32, 128]
- padding: False
- - 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: 4
- Eval:
- dataset:
- name: LMDBDataSet
- data_dir: ../evaluation
- transforms:
- - DecodeImagePIL: # load image
- img_mode: RGB
- - ABINetLabelEncode:
- ignore_index: *ignore_index
- - RecTVResize:
- image_shape: [32, 128]
- padding: 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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