rec_r34_vd_tps_bilstm_attn.yml

Global:
  algorithm: RARE
  use_gpu: true
  epoch_num: 72
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: output/rec_RARE
  save_epoch_step: 3
  eval_batch_step: 2000
  train_batch_size_per_card: 256
  drop_last: true
  test_batch_size_per_card: 256
  image_shape: [3, 32, 100]
  max_text_length: 25
  character_type: en
  loss_type: attention
  reader_yml: ./configs/rec/rec_benchmark_reader.yml
  pretrain_weights:
  checkpoints:
  save_inference_dir:
  infer_img:


Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel

TPS:
  function: ppocr.modeling.stns.tps,TPS
  num_fiducial: 20
  loc_lr: 0.1
  model_name: large

Backbone:
  function: ppocr.modeling.backbones.rec_resnet_vd,ResNet
  layers: 34
 
Head:
  function: ppocr.modeling.heads.rec_attention_head,AttentionPredict
  encoder_type: rnn
  SeqRNN:
    hidden_size: 256
  Attention:
    decoder_size: 128
    word_vector_dim: 128
  
Loss:
  function: ppocr.modeling.losses.rec_attention_loss,AttentionLoss
  
Optimizer:
  function: ppocr.optimizer,AdamDecay
  base_lr: 0.001
  beta1: 0.9
  beta2: 0.999