Delete configs

configs/det/dbnet/repvit_db.yml

@@ -1,173 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: &epoch_num 500
-  log_smooth_window: 20
-  print_batch_step: 100
-  save_model_dir: ./output/det_repsvtr_db
-  save_epoch_step: 10
-  eval_batch_step:
-  - 0
-  - 1000
-  cal_metric_during_train: false
-  checkpoints:
-  pretrained_model: openocr_det_repvit_ch.pth
-  save_inference_dir: null
-  use_visualdl: false
-  infer_img: ./testA
-  save_res_path: ./checkpoints/det_db/predicts_db.txt
-  distributed: true
-  model_type: det
-
-Architecture:
-  algorithm: DB
-  Backbone:
-    name: RepSVTR_det
-  Neck:
-    name: RSEFPN
-    out_channels: 96
-    shortcut: True
-  Head:
-    name: DBHead
-    k: 50
-
-# Loss:
-#   name: DBLoss
-#   balance_loss: true
-#   main_loss_type: DiceLoss
-#   alpha: 5
-#   beta: 10
-#   ohem_ratio: 3
-
-# Optimizer:
-#   name: Adam
-#   beta1: 0.9
-#   beta2: 0.999
-#   lr:
-#     name: Cosine
-#     learning_rate: 0.001 #(8*8c)
-#     warmup_epoch: 2
-#   regularizer:
-#     name: L2
-#     factor: 5.0e-05
-
-PostProcess:
-  name: DBPostProcess
-  thresh: 0.3
-  box_thresh: 0.6
-  max_candidates: 1000
-  unclip_ratio: 1.5
-  score_mode: 'slow'
-
-# Metric:
-#   name: DetMetric
-#   main_indicator: hmean
-
-# Train:
-#   dataset:
-#     name: SimpleDataSet
-#     data_dir: ./train_data/icdar2015/text_localization/
-#     label_file_list:
-#       - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
-#     ratio_list: [1.0]
-#     transforms:
-#     - DecodeImage:
-#         img_mode: BGR
-#         channel_first: false
-#     - DetLabelEncode: null
-#     - CopyPaste: null
-#     - IaaAugment:
-#         augmenter_args:
-#         - type: Fliplr
-#           args:
-#             p: 0.5
-#         - type: Affine
-#           args:
-#             rotate:
-#             - -10
-#             - 10
-#         - type: Resize
-#           args:
-#             size:
-#             - 0.5
-#             - 3
-#     - EastRandomCropData:
-#         size:
-#         - 640
-#         - 640
-#         max_tries: 50
-#         keep_ratio: true
-#     - MakeBorderMap:
-#         shrink_ratio: 0.4
-#         thresh_min: 0.3
-#         thresh_max: 0.7
-#         total_epoch: *epoch_num
-#     - MakeShrinkMap:
-#         shrink_ratio: 0.4
-#         min_text_size: 8
-#         total_epoch: *epoch_num
-#     - NormalizeImage:
-#         scale: 1./255.
-#         mean:
-#         - 0.485
-#         - 0.456
-#         - 0.406
-#         std:
-#         - 0.229
-#         - 0.224
-#         - 0.225
-#         order: hwc
-#     - ToCHWImage: null
-#     - KeepKeys:
-#         keep_keys:
-#         - image
-#         - threshold_map
-#         - threshold_mask
-#         - shrink_map
-#         - shrink_mask
-#   loader:
-#     shuffle: true
-#     drop_last: false
-#     batch_size_per_card: 8
-#     num_workers: 8
-
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./train_data/icdar2015/text_localization/
-    label_file_list:
-      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - DetLabelEncode: null
-    - DetResizeForTest:
-        # image_shape: [1280, 1280]
-        # keep_ratio: True
-        # padding: True
-        limit_side_len: 960
-        limit_type: max
-    - NormalizeImage:
-        scale: 1./255.
-        mean:
-        - 0.485
-        - 0.456
-        - 0.406
-        std:
-        - 0.229
-        - 0.224
-        - 0.225
-        order: hwc
-    - ToCHWImage: null
-    - KeepKeys:
-        keep_keys:
-        - image
-        - shape
-        - polys
-        - ignore_tags
-  loader:
-    shuffle: false
-    drop_last: false
-    batch_size_per_card: 1
-    num_workers: 2
-profiler_options: null

configs/rec/abinet/resnet45_trans_abinet_lang.yml

@@ -1,94 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/resnet45_trans_abinet_lang/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./openocr_nolang_abinet_lang.pth
-  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_resnet45_trans_abinet_lang.txt
-  grad_clip_val: 20
-  use_amp: True
-
-Optimizer:
-  name: Adam
-  lr: 0.000267
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: MultiStepLR
-  milestones: [12]
-  gamma: 0.1
-
-Architecture:
-  model_type: rec
-  algorithm: ABINet
-  Transform:
-  Encoder:
-    name: ResNet45
-    in_channels: 3
-    strides: [2, 1, 2, 1, 1]
-  Decoder:
-    name: ABINetDecoder
-    iter_size: 3
-
-Loss:
-  name: ABINetLoss
-
-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:
-      - 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:
-      - 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

configs/rec/abinet/resnet45_trans_abinet_wo_lang.yml

@@ -1,93 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/resnet45_trans_abinet_wo_lang/
-  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_resnet45_trans_abinet_wo_lang.txt
-  grad_clip_val: 20
-  use_amp: True
-
-Optimizer:
-  name: Adam
-  lr: 0.000267
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: MultiStepLR
-  milestones: [12]
-  gamma: 0.1
-
-Architecture:
-  model_type: rec
-  algorithm: ABINet
-  Transform:
-  Encoder:
-    name: ResNet45
-    in_channels: 3
-    strides: [2, 1, 2, 1, 1]
-  Decoder:
-    name: ABINetDecoder
-    iter_size: 0
-
-Loss:
-  name: ABINetLoss
-
-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:
-      - 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:
-      - 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

configs/rec/abinet/svtrv2_abinet_lang.yml

@@ -1,130 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_abinet_lang/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./openocr_svtrv2_nolang_abinet_lang.pth
-  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_svtrv2_abinet_lang.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: ABINet
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: ABINetDecoder
-    iter_size: 3
-    num_layers: 0
-
-Loss:
-  name: ABINetLoss
-
-PostProcess:
-  name: ABINetLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ABINetLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ABINetLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/abinet/svtrv2_abinet_wo_lang.yml

@@ -1,128 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_abinet_wo_lang/
-  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_svtrv2_abinet_wo_lang.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: ABINet
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: ABINetDecoder
-    iter_size: 0
-    num_layers: 0
-Loss:
-  name: ABINetLoss
-
-PostProcess:
-  name: ABINetLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ABINetLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ABINetLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/aster/resnet31_lstm_aster_tps_on.yml

@@ -1,93 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/resnet31_lstm_aster_tps_on
-  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/predicts_aster_tps.txt
-  use_amp: True
-  grad_clip_val: 1.0
-
-Optimizer:
-  name: Adam
-  lr: 0.002 # for 1gpus bs1024/gpu
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: aster
-  Transform:
-    name: Aster_TPS
-    tps_inputsize: [32, 64]
-    tps_outputsize: [32, 128]
-  Encoder:
-    name: ResNet_ASTER
-  Decoder:
-    name: ASTERDecoder
-
-Loss:
-  name: ARLoss
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    batch_size_per_card: 1024
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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

configs/rec/aster/svtrv2_aster.yml

@@ -1,127 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_aster
-  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_svtrv2_aster.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: aster
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: False
-  Decoder:
-    name: ASTERDecoder
-
-Loss:
-  name: ARLoss
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/aster/svtrv2_aster_tps_on.yml

@@ -1,102 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_aster_tps_on
-  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_svtrv2_aster_tps_on.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: aster
-  Transform:
-    name: Aster_TPS
-    tps_inputsize: [32, 64]
-    tps_outputsize: [32, 128]
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: False
-  Decoder:
-    name: ASTERDecoder
-
-Loss:
-  name: ARLoss
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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

configs/rec/autostr/autostr_lstm_aster_tps_on.yml

@@ -1,95 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/autostr_lstm_aster_tps_on
-  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_autostr_lstm_aster_tps_on.txt
-  use_amp: True
-  grad_clip_val: 1.0
-
-Optimizer:
-  name: Adam
-  lr: 0.002 # for 4gpus bs256/gpu
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: autostr
-  Transform:
-    name: Aster_TPS
-    tps_inputsize: [32, 64]
-    tps_outputsize: [32, 128]
-  Encoder:
-    name: AutoSTREncoder
-    stride_stages: '[(2, 2), (2, 1), (2, 2), (2, 1), (2, 1)]'
-    conv_op_ids: [2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 4, 1, 1, 6, 6]
-  Decoder:
-    name: ASTERDecoder
-
-Loss:
-  name: ARLoss
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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

configs/rec/busnet/svtrv2_busnet.yml

@@ -1,135 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 10
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_busnet/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./output/rec/u14m_filter/svtrv2_busnet_pretraining/best.pth
-  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_svtrv2_busnet.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: BUSBet
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: False
-  Decoder:
-    name: BUSDecoder
-    nhead: 6
-    num_layers: 6
-    dim_feedforward: 1536
-    ignore_index: &ignore_index 100
-    pretraining: False
-    # return_id: 2
-Loss:
-  name: ABINetLoss
-  ignore_index: *ignore_index
-
-PostProcess:
-  name: ABINetLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ABINetLabelEncode:
-          ignore_index: *ignore_index
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ABINetLabelEncode:
-          ignore_index: *ignore_index
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/busnet/svtrv2_busnet_pretraining.yml

@@ -1,134 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 10
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_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_svtrv2_busnet_pretraining.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: BUSBet
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: False
-  Decoder:
-    name: BUSDecoder
-    nhead: 6
-    num_layers: 6
-    dim_feedforward: 1536
-    ignore_index: &ignore_index 100
-    pretraining: True
-    # return_id: 0
-Loss:
-  name: ABINetLoss
-  ignore_index: *ignore_index
-
-PostProcess:
-  name: ABINetLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ABINetLabelEncode:
-          ignore_index: *ignore_index
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ABINetLabelEncode:
-          ignore_index: *ignore_index
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/busnet/vit_busnet.yml

@@ -1,104 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 10
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/vit_busnet/
-  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.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: False
-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

configs/rec/busnet/vit_busnet_pretraining.yml

@@ -1,104 +0,0 @@
-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

configs/rec/cam/convnextv2_cam_tps_on.yml

@@ -1,118 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/convnextv2_cam_tps_on
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: False
-  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: &max_text_length 25
-  use_space_char: False
-  save_res_path: ./output/rec/u14m_filter/predicts_convnextv2_cam_tps_on.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.0008 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-  eps: 1.e-8
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 :  1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CAM
-  Transform:
-    name: Aster_TPS
-    tps_inputsize: [32, 64]
-    tps_outputsize: &img_shape [32, 128]
-  Encoder:
-    name: CAMEncoder
-    encoder_config:
-      name: ConvNeXtV2
-      depths: [2, 2, 8, 2]
-      dims: [80, 160, 320, 640]
-      strides: [[4,4], [2,1], [2,1], [1,1]]
-      drop_path_rate: 0.2
-      feat2d: True
-    nb_classes: 97
-    strides: [[4,4], [2,1], [2,1], [1,1]]
-    deform_stride: 2
-    stage_idx: 2
-    use_depthwise_unet: True
-    use_more_unet: False
-    binary_loss_type: BanlanceMultiClassCrossEntropyLoss
-    mid_size: True
-    d_embedding: 384
-  Decoder:
-    name: CAMDecoder
-    num_encoder_layers: -1
-    beam_size: 0
-    num_decoder_layers: 2
-    nhead: 8
-    max_len: *max_text_length
-
-Loss:
-  name: CAMLoss
-  loss_weight_binary: 1.5
-  label_smoothing: 0.
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - CAMLabelEncode: # Class handling label
-          font_path: ./arial.ttf
-          image_shape: *img_shape
-      - RecTVResize:
-          image_shape: [64, 256]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length', 'binary_mask'] # 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
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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

configs/rec/cam/convnextv2_tiny_cam_tps_on.yml

@@ -1,118 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/convnextv2_tiny_cam_tps_on
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: False
-  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: &max_text_length 25
-  use_space_char: False
-  save_res_path: ./output/rec/u14m_filter/predicts_convnextv2_cam_tps_on.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.0008 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-  eps: 1.e-8
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 :  1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CAM
-  Transform:
-    name: Aster_TPS
-    tps_inputsize: [32, 64]
-    tps_outputsize: &img_shape [32, 128]
-  Encoder:
-    name: CAMEncoder
-    encoder_config:
-      name: ConvNeXtV2
-      depths: [3, 3, 9, 3]
-      dims: [96, 192, 384, 768]
-      strides: [[4,4], [2,1], [2,1], [1,1]]
-      drop_path_rate: 0.2
-      feat2d: True
-    nb_classes: 97
-    strides: [[4,4], [2,1], [2,1], [1,1]]
-    deform_stride: 2
-    stage_idx: 2
-    use_depthwise_unet: True
-    use_more_unet: False
-    binary_loss_type: BanlanceMultiClassCrossEntropyLoss
-    mid_size: False
-    d_embedding: 512
-  Decoder:
-    name: CAMDecoder
-    num_encoder_layers: -1
-    beam_size: 0
-    num_decoder_layers: 2
-    nhead: 8
-    max_len: *max_text_length
-
-Loss:
-  name: CAMLoss
-  loss_weight_binary: 1.5
-  label_smoothing: 0.
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - CAMLabelEncode: # Class handling label
-          font_path: ./arial.ttf
-          image_shape: *img_shape
-      - RecTVResize:
-          image_shape: [64, 256]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length', 'binary_mask'] # 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
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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

configs/rec/cam/svtrv2_cam_tps_on.yml

@@ -1,123 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_cam_tps_on
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: False
-  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: &max_text_length 25
-  use_space_char: False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtrv2_cam_tps_on.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 :  1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CAM
-  Transform:
-    name: Aster_TPS
-    tps_inputsize: [32, 64]
-    tps_outputsize: &img_shape [32, 128]
-  Encoder:
-    name: CAMEncoder
-    encoder_config:
-      name: SVTRv2LNConvTwo33
-      use_pos_embed: False
-      dims: [128, 256, 384]
-      depths: [6, 6, 6]
-      num_heads: [4, 8, 12]
-      mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-      local_k: [[5, 5], [5, 5], [-1, -1]]
-      sub_k: [[1, 1], [2, 1], [-1, -1]]
-      last_stage: false
-      feat2d: True
-    nb_classes: 97
-    strides: [[4, 4], [1, 1], [2, 1], [1, 1]]
-    k_size: [[2, 2], [1, 1], [2, 1], [1, 1]]
-    q_size: [4, 32]
-    deform_stride: 2
-    stage_idx: 2
-    use_depthwise_unet: True
-    use_more_unet: False
-    binary_loss_type: BanlanceMultiClassCrossEntropyLoss
-    mid_size: True
-    d_embedding: 384
-  Decoder:
-    name: CAMDecoder
-    num_encoder_layers: -1
-    beam_size: 0
-    num_decoder_layers: 2
-    nhead: 8
-    max_len: *max_text_length
-
-Loss:
-  name: CAMLoss
-  loss_weight_binary: 1.5
-  label_smoothing: 0.
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - CAMLabelEncode: # Class handling label
-          font_path: ./arial.ttf
-          image_shape: *img_shape
-      - RecTVResize:
-          image_shape: [64, 256]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length', 'binary_mask'] # 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
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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

configs/rec/cdistnet/resnet45_trans_cdistnet.yml

@@ -1,93 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/resnet45_trans_cdistnet
-  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_resnet45_trans_cdistnet.txt
-  use_amp: True
-  grad_clip_val: 5
-
-Optimizer:
-  name: Adam
-  lr: 0.002 # for 4gpus bs256/gpu
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CDistNet
-  Transform:
-  Encoder:
-    name: ResNet45
-    in_channels: 3
-    strides: [2, 1, 2, 1, 1]
-  Decoder:
-    name: CDistNetDecoder
-    add_conv: True
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-
-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:
-      - ARLabelEncode: # Class handling label
-      - 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
-      - ARLabelEncode: # Class handling label
-      - 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

configs/rec/cdistnet/svtrv2_cdistnet.yml

@@ -1,139 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_cdistnet/
-  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: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtrv2_cdistnet.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 #4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CDistNet
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: CDistNetDecoder
-    add_conv: False
-    num_encoder_blocks: 0
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/cppd/svtr_base_cppd.yml

@@ -1,123 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtr_base_cppd/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path
-  # ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtr_base_cppd.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CPPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet
-    img_size: [32, 128]
-    out_char_num: 25
-    out_channels: 256
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv', 'Conv','Conv', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    last_stage: False
-    prenorm: True
-  Decoder:
-    name: CPPDDecoder
-    vis_seq: 64
-    num_layer: 2
-    pos_len: False
-    rec_layer: 1
-
-
-Loss:
-  name: CPPDLoss
-  ignore_index: 100
-  smoothing: True
-  pos_len: False
-  sideloss_weight: 1.0
-
-PostProcess:
-  name: CPPDLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_node', '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
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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: 128
-    num_workers: 4

configs/rec/cppd/svtr_base_cppd_ch.yml

@@ -1,126 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 100
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/ch/svtr_base_cppd/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 2000]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: False
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/ppocr_keys_v1.txt
-  # ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/ch/predicts_svtr_base_cppd.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.0005 # for 4gpus bs128/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: CosineAnnealingLR
-  warmup_epoch: 5
-
-Architecture:
-  model_type: rec
-  algorithm: CPPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet
-    img_size: [32, 256]
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 4]
-    num_heads: [4, 8, 12]
-    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv', 'Conv','Conv', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    last_stage: False
-    prenorm: True
-  Decoder:
-    name: CPPDDecoder
-    vis_seq: 128
-    num_layer: 3
-    pos_len: False
-    rec_layer: 1
-    ch: True
-
-
-Loss:
-  name: CPPDLoss
-  ignore_index: 7000
-  smoothing: True
-  pos_len: False
-  sideloss_weight: 1.0
-
-PostProcess:
-  name: CPPDLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../benchmark_bctr/benchmark_bctr_train
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          ch: True
-          ignore_index: 7000
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - SVTRResize:
-          image_shape: [3, 32, 256]
-          padding: True
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_node', 'label_index', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    batch_size_per_card: 128
-    drop_last: True
-    num_workers: 8
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../benchmark_bctr/benchmark_bctr_test/scene_test
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          ch: True
-          ignore_index: 7000
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - SVTRResize:
-          image_shape: [3, 32, 256]
-          padding: True
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_node', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 256
-    num_workers: 4

configs/rec/cppd/svtr_base_cppd_h8.yml

@@ -1,123 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtr_base_h8_cppd/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtr_base_cppd.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CPPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet
-    img_size: [32, 128]
-    out_char_num: 25
-    out_channels: 256
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    sub_k: [[1, 1], [2, 1]]
-    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv', 'Conv','Conv', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    last_stage: False
-    prenorm: True
-  Decoder:
-    name: CPPDDecoder
-    vis_seq: 128
-    num_layer: 2
-    pos_len: False
-    rec_layer: 1
-
-Loss:
-  name: CPPDLoss
-  ignore_index: 100
-  smoothing: True
-  pos_len: False
-  sideloss_weight: 1.0
-
-PostProcess:
-  name: CPPDLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-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:
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_node', '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
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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: 128
-    num_workers: 4

configs/rec/cppd/svtr_base_cppd_syn.yml

@@ -1,124 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 60
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/syn/svtr_base_cppd/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path
-  # ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/syn/predicts_svtr_base_cppd.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.0005 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: CosineAnnealingLR
-  warmup_epoch: 6
-
-Architecture:
-  model_type: rec
-  algorithm: CPPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet
-    img_size: [32, 100]
-    out_char_num: 25
-    out_channels: 256
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 4]
-    num_heads: [4, 8, 12]
-    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv', 'Conv','Conv', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    last_stage: False
-    prenorm: True
-  Decoder:
-    name: CPPDDecoder
-    vis_seq: 50
-    num_layer: 3
-    pos_len: False
-    rec_layer: 1
-
-
-Loss:
-  name: CPPDLoss
-  ignore_index: 100
-  smoothing: True
-  pos_len: False
-  sideloss_weight: 1.0
-
-PostProcess:
-  name: CPPDLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-Train:
-  dataset:
-    name: STRLMDBDataSet
-    data_dir: ./
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-    #   - SVTRRAug:
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - SVTRResize:
-          image_shape: [3, 32, 100]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_node', '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: ../evaluation/
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - SVTRResize:
-          image_shape: [3, 32, 100]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_node', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 256
-    num_workers: 4

configs/rec/cppd/svtrv2_cppd.yml

@@ -1,150 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_cppd/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtrv2_cppd.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: CPPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: False
-  Decoder:
-    name: CPPDDecoder
-    ds: True
-    num_layer: 2
-    pos_len: False
-    rec_layer: 1
-
-
-Loss:
-  name: CPPDLoss
-  ignore_index: 100
-  smoothing: True
-  pos_len: False
-  sideloss_weight: 1.0
-
-PostProcess:
-  name: CPPDLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_node', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - CPPDLabelEncode: # Class handling label
-          pos_len: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/dan/resnet45_fpn_dan.yml

@@ -1,98 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/resnet45_fpn_dan/
-  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_resnet45_fpn_dan.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: Adam
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: DAN
-  Transform:
-  Encoder:
-    name: ResNet45
-    in_channels: 3
-    strides: [2, 1, 2, 1, 1]
-    return_list: True
-  Decoder:
-    name: DANDecoder
-    max_len: 25
-    channels_list: [64, 128, 256, 512]
-    strides_list: [[2, 2], [1, 1], [1, 1]]
-    in_shape: [8, 32]
-    depth: 4
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-
-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:
-      - ARLabelEncode:
-      - 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
-      - ARLabelEncode:
-      - 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

configs/rec/dan/svtrv2_dan.yml

@@ -1,130 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_dan
-  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_svtrv2_dan.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # 4gpus 256bs/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: DAN
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: DANDecoder
-    use_cam: False
-    max_len: 25
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/focalsvtr/focalsvtr_ctc.yml

@@ -1,137 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/focalsvtr_ctc/
-  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: &character_dict_path
-  # ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_focalsvtr_ctc.txt
-
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Encoder:
-    name: FocalSVTR
-    img_size: [32, 128]
-    depths: [6, 6, 6]
-    embed_dim: 96
-    sub_k: [[1, 1], [2, 1], [1, 1]]
-    focal_levels: [3, 3, 3]
-    out_channels: 256
-    last_stage: True
-  Decoder:
-    name: CTCDecoder
-
-Loss:
-  name: CTCLoss
-  zero_infinity: True
-
-PostProcess:
-  name: CTCLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-
-Train:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    padding: &padding False
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - PARSeqAug:
-      - CTCLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length']
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: 12
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    padding: True
-    data_dir_list: ['../evaluation/CUTE80',
-    '../evaluation/IC13_857',
-    '../evaluation/IC15_1811',
-    '../evaluation/IIIT5k',
-    '../evaluation/SVT',
-    '../evaluation/SVTP',
-    ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length']
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: 128
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 128
-    max_ratio: 12
-    num_workers: 4

configs/rec/gtc/svtrv2_lnconv_nrtr_gtc.yml

@@ -1,168 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/svtrv2_lnconv_nrtr_gtc
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img: ../ltb/img
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/predicts_smtr.txt
-  use_amp: True
-  distributed: true
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: BGPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: GTCDecoder
-    infer_gtc: True
-    detach: False
-    gtc_decoder:
-      name: NRTRDecoder
-      num_encoder_layers: -1
-      beam_size: 0
-      num_decoder_layers: 2
-      nhead: 12
-      max_len: *max_text_length
-    ctc_decoder:
-      name: RCTCDecoder
-
-Loss:
-  name: GTCLoss
-  gtc_loss:
-    name: ARLoss
-
-PostProcess:
-  name: GTCLabelDecode
-  gtc_label_decode:
-    name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecGTCMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    # max_ratio: &max_ratio 4
-    # min_ratio: 1
-    # base_shape: &base_shape [[64, 64], [96, 48], [112, 40], [128, 32]]
-    # base_h: &base_h 32
-    # padding: &padding False
-    padding: false
-    # padding_rand: true
-    # padding_doub: true
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - PARSeqAug:
-      - GTCLabelEncode: # Class handling label
-          gtc_label_encode:
-            name: ARLabelEncode
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length', 'ctc_label', 'ctc_length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - GTCLabelEncode: # Class handling label
-          gtc_label_encode:
-            name: ARLabelEncode
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length', 'ctc_label', 'ctc_length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/gtc/svtrv2_lnconv_smtr_gtc_long_infer.yml

@@ -1,151 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/svtrv2_lnconv_smtr_gtc_long_infer
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 1000]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img: ../ltb/img
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/predicts_smtr.txt
-  use_amp: True
-  distributed: true
-
-Optimizer:
-  name: AdamW
-  lr: 0.000325
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: BGPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: GTCDecoder
-    infer_gtc: False
-    detach: False
-    gtc_decoder:
-      name: SMTRDecoder
-      num_layer: 1
-      ds: True
-      max_len: *max_text_length
-      next_mode: &next True
-      sub_str_len: &subsl 5
-    ctc_decoder:
-      name: RCTCDecoder
-
-Loss:
-  name: CTCLoss
-
-PostProcess:
-  name: CTCLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - CTCLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 128
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 12
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - CTCLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/gtc/svtrv2_lnconv_smtr_gtc_smtr_long.yml

@@ -1,150 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/svtrv2_lnconv_smtr_gtc_nodetach_smtr_long_infer
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 1000]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/predicts_smtr.txt
-  use_amp: True
-  distributed: true
-
-Optimizer:
-  name: AdamW
-  lr: 0.000325
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-Architecture:
-  model_type: rec
-  algorithm: BGPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: GTCDecoder
-    infer_gtc: True
-    detach: False
-    gtc_decoder:
-      name: SMTRDecoder
-      num_layer: 1
-      ds: True
-      max_len: *max_text_length
-      next_mode: &next True
-      sub_str_len: &subsl 5
-      infer_aug: True
-    ctc_decoder:
-      name: RCTCDecoder
-
-Loss:
-  name: GTCLoss
-  ctc_weight: 0.1
-  gtc_loss:
-    name: SMTRLoss
-
-PostProcess:
-  name: GTCLabelDecode
-  gtc_label_decode:
-    name: SMTRLabelDecode
-    next_mode: *next
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-  only_gtc: True
-
-Metric:
-  name: RecGTCMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - SMTRLabelEncode: # Class handling label
-          sub_str_len: *subsl
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_subs', 'label_next', 'length_subs',
-          'label_subs_pre', 'label_next_pre', 'length_subs_pre', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 12
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ../ltb/
-    label_file_list: ['../ltb/ultra_long_70_list.txt']
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - GTCLabelEncode: # Class handling label
-          gtc_label_encode:
-            name: ARLabelEncode
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: 200
-      - SliceResize:
-          image_shape: [3, 32, 128]
-          padding: False
-          max_ratio: 12
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length', 'ctc_label', 'ctc_length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 1
-    num_workers: 2

configs/rec/gtc/svtrv2_lnconv_smtr_gtc_stream.yml

@@ -1,152 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 60
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/svtrv2_lnconv_smtr_gtc_stream
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/predicts_smtr.txt
-  use_amp: True
-  distributed: true
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: BGPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: GTCDecoder
-    infer_gtc: True
-    detach: False
-    gtc_decoder:
-      name: SMTRDecoder
-      num_layer: 1
-      ds: True
-      max_len: *max_text_length
-      next_mode: &next True
-      sub_str_len: &subsl 5
-      infer_aug: False
-    ctc_decoder:
-      name: RCTCDecoder
-
-Loss:
-  name: GTCLoss
-  ctc_weight: 0.25
-  gtc_loss:
-    name: SMTRLoss
-
-PostProcess:
-  name: GTCLabelDecode
-  gtc_label_decode:
-    name: SMTRLabelDecode
-    next_mode: *next
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-  only_gtc: True
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-  stream: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - SMTRLabelEncode: # Class handling label
-          sub_str_len: *subsl
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'label_subs', 'label_next', 'length_subs',
-          'label_subs_pre', 'label_next_pre', 'length_subs_pre', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 12
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ../ltb/
-    label_file_list: ['../ltb/ultra_long_70_list.txt']
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - GTCLabelEncode: # Class handling label
-          gtc_label_encode:
-            name: ARLabelEncode
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - SliceTVResize:
-          image_shape: [32, 128]
-          padding: False
-          max_ratio: 4
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length', 'ctc_label', 'ctc_length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 1
-    num_workers: 2

configs/rec/igtr/readme.md

@@ -1,189 +0,0 @@
-# IGTR
-
-- [IGTR](#igtr)
-  - [1. Introduction](#1-introduction)
-  - [2. Environment](#2-environment)
-    - [Dataset Preparation](#dataset-preparation)
-  - [3. Model Training / Evaluation](#3-model-training--evaluation)
-  - [Citation](#citation)
-
-<a name="1"></a>
-
-## 1. Introduction
-
-Paper:
-
-> [Instruction-Guided Scene Text Recognition](https://arxiv.org/abs/2401.17851)
-> Yongkun Du, Zhineng Chen, Yuchen Su, Caiyan Jia, Yu-Gang Jiang
-
-<a name="model"></a>
-Multi-modal models show appealing performance in visual recognition tasks recently, as free-form text-guided training evokes the ability to understand fine-grained visual content. However, current models are either inefficient or cannot be trivially upgraded to scene text recognition (STR) due to the composition difference between natural and text images. We propose a novel instruction-guided scene text recognition (IGTR) paradigm that formulates STR as an instruction learning problem and understands text images by predicting character attributes, e.g., character frequency, position, etc. IGTR first devises $\\left \\langle condition,question,answer\\right \\rangle$ instruction triplets, providing rich and diverse descriptions of character attributes. To effectively learn these attributes through question-answering, IGTR develops lightweight instruction encoder, cross-modal feature fusion module and multi-task answer head, which guides nuanced text image understanding. Furthermore, IGTR realizes different recognition pipelines simply by using different instructions, enabling a character-understanding-based text reasoning paradigm that considerably differs from current methods. Experiments on English and Chinese benchmarks show that IGTR outperforms existing models by significant margins, while maintaining a small model size and efficient inference speed. Moreover, by adjusting the sampling of instructions, IGTR offers an elegant way to tackle the recognition of both rarely appearing and morphologically similar characters, which were previous challenges.
-
-<a name="model"></a>
-The accuracy (%) and model files of IGTR on the public dataset of scene text recognition are as follows:
-
-- Trained on Synth dataset(MJ+ST), test on Common Benchmarks, training and test datasets both from [PARSeq](https://github.com/baudm/parseq).
-
-|  Model  | IC13<br/>857 | SVT  | IIIT5k<br/>3000 | IC15<br/>1811 | SVTP | CUTE80 |  Avg  |                                        Config&Model&Log                                         |
-| :-----: | :----------: | :--: | :-------------: | :-----------: | :--: | :----: | :---: | :---------------------------------------------------------------------------------------------: |
-| IGTR-PD |     97.6     | 95.2 |      97.6       |     88.4      | 91.6 |  95.5  | 94.30 | [link](https://drive.google.com/drive/folders/1Pv0CW2hiWC_dIyaB74W1fsXqiX3z5yXA?usp=drive_link) |
-| IGTR-AR |     98.6     | 95.7 |      98.2       |     88.4      | 92.4 |  95.5  | 94.78 |                                            as above                                             |
-
-- Test on Union14M-L benchmark, from [Union14M](https://github.com/Mountchicken/Union14M/).
-
-|  Model  | Curve | Multi-<br/>Oriented | Artistic | Contextless | Salient | Multi-<br/>word | General |  Avg  |    Config&Model&Log     |
-| :-----: | :---: | :-----------------: | :------: | :---------: | :-----: | :-------------: | :-----: | :---: | :---------------------: |
-| IGTR-PD | 76.9  |        30.6         |   59.1   |    63.3     |  77.8   |      62.5       |  66.7   | 62.40 | Same as the above table |
-| IGTR-AR | 78.4  |        31.9         |   61.3   |    66.5     |  80.2   |      69.3       |  67.9   | 65.07 |        as above         |
-
-- Trained on Union14M-L training dataset.
-
-|    Model     | IC13<br/>857 | SVT  | IIIT5k<br/>3000 | IC15<br/>1811 | SVTP | CUTE80 |  Avg  |                                        Config&Model&Log                                         |
-| :----------: | :----------: | :--: | :-------------: | :-----------: | :--: | :----: | :---: | :---------------------------------------------------------------------------------------------: |
-|   IGTR-PD    |     97.7     | 97.7 |      98.3       |     89.8      | 93.7 |  97.9  | 95.86 | [link](https://drive.google.com/drive/folders/1ZGlzDqEzjrBg8qG2wBkbOm3bLRzFbTzo?usp=drive_link) |
-|   IGTR-AR    |     98.1     | 98.4 |      98.7       |     90.5      | 94.9 |  98.3  | 96.48 |                                            as above                                             |
-| IGTR-PD-60ep |     97.9     | 98.3 |      99.2       |     90.8      | 93.7 |  97.6  | 96.24 | [link](https://drive.google.com/drive/folders/1ik4hxZDRsjU1RbCA19nwE45Kg1bCnMoa?usp=drive_link) |
-| IGTR-AR-60ep |     98.4     | 98.1 |      99.3       |     91.5      | 94.3 |  97.6  | 96.54 |                                            as above                                             |
-|  IGTR-PD-PT  |     98.6     | 98.0 |      99.1       |     91.7      | 96.8 |  99.0  | 97.20 | [link](https://drive.google.com/drive/folders/1QM0EWV66IfYI1G0Xm066V2zJA62hH6-1?usp=drive_link) |
-|  IGTR-AR-PT  |     98.8     | 98.3 |      99.2       |     92.0      | 96.8 |  99.0  | 97.34 |                                            as above                                             |
-
-|    Model     | Curve | Multi-<br/>Oriented | Artistic | Contextless | Salient | Multi-<br/>word | General |  Avg  |    Config&Model&Log     |
-| :----------: | :---: | :-----------------: | :------: | :---------: | :-----: | :-------------: | :-----: | :---: | :---------------------: |
-|   IGTR-PD    | 88.1  |        89.9         |   74.2   |    80.3     |  82.8   |      79.2       |  83.0   | 82.51 | Same as the above table |
-|   IGTR-AR    | 90.4  |        91.2         |   77.0   |    82.4     |  84.7   |      84.0       |  84.4   | 84.86 |        as above         |
-| IGTR-PD-60ep | 90.0  |        92.1         |   77.5   |    82.8     |  86.0   |      83.0       |  84.8   | 85.18 | Same as the above table |
-| IGTR-AR-60ep | 91.0  |        93.0         |   78.7   |    84.6     |  87.3   |      84.8       |  85.6   | 86.43 |        as above         |
-|  IGTR-PD-PT  | 92.4  |        92.1         |   80.7   |    83.6     |  87.7   |      86.9       |  85.0   | 86.92 | Same as the above table |
-|  IGTR-AR-PT  | 93.0  |        92.9         |   81.3   |    83.4     |  88.6   |      88.7       |  85.6   | 87.65 |        as above         |
-
-- Trained and test on Chinese dataset, from [Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition).
-
-|    Model    | Scene | Web  | Document | Handwriting |  Avg  |                                        Config&Model&Log                                         |
-| :---------: | :---: | :--: | :------: | :---------: | :---: | :---------------------------------------------------------------------------------------------: |
-|   IGTR-PD   | 73.1  | 74.8 |   98.6   |    52.5     | 74.75 |                                                                                                 |
-|   IGTR-AR   | 75.1  | 76.4 |   98.7   |    55.3     | 76.37 |                                                                                                 |
-| IGTR-PD-TS  | 73.5  | 75.9 |   98.7   |    54.5     | 75.65 | [link](https://drive.google.com/drive/folders/1H3VRdGHjhawd6fkSC-qlBzVzvYYTpHRg?usp=drive_link) |
-| IGTR-AR-TS  | 75.6  | 77.0 |   98.8   |    57.3     | 77.17 |                                            as above                                             |
-| IGTR-PD-Aug | 79.5  | 80.0 |   99.4   |    58.9     | 79.45 | [link](https://drive.google.com/drive/folders/1XFQkCILwcFwA7iYyQY9crnrouaI5sqcZ?usp=drive_link) |
-| IGTR-AR-Aug | 82.0  | 81.7 |   99.5   |    63.8     | 81.74 |                                            as above                                             |
-
-Download all Configs, Models, and Logs from [Google Drive](https://drive.google.com/drive/folders/1mSRDg9Mj5R6PspAdFGXZHDHTCQmjkd8d?usp=drive_link).
-
-<a name="2"></a>
-
-## 2. Environment
-
-- [PyTorch](http://pytorch.org/) version >= 1.13.0
-- Python version >= 3.7
-
-```shell
-git clone -b develop https://github.com/Topdu/OpenOCR.git
-cd OpenOCR
-# A100 Ubuntu 20.04 Cuda 11.8
-conda create -n openocr python==3.8
-conda activate openocr
-conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=11.8 -c pytorch -c nvidia
-pip install -r requirements.txt
-```
-
-#### Dataset Preparation
-
-[English dataset download](https://github.com/baudm/parseq)
-
-[Union14M-L download](https://github.com/Mountchicken/Union14M)
-
-[Chinese dataset download](https://github.com/fudanvi/benchmarking-chinese-text-recognition#download)
-
-The expected filesystem structure is as follows:
-
-```
-benchmark_bctr
-├── benchmark_bctr_test
-│   ├── document_test
-│   ├── handwriting_test
-│   ├── scene_test
-│   └── web_test
-└── benchmark_bctr_train
-    ├── document_train
-    ├── handwriting_train
-    ├── scene_train
-    └── web_train
-evaluation
-├── CUTE80
-├── IC13_857
-├── IC15_1811
-├── IIIT5k
-├── SVT
-└── SVTP
-OpenOCR
-synth
-├── MJ
-│   ├── test
-│   ├── train
-│   └── val
-└── ST
-test # from PARSeq
-├── ArT
-├── COCOv1.4
-├── CUTE80
-├── IC13_1015
-├── IC13_1095
-├── IC13_857
-├── IC15_1811
-├── IC15_2077
-├── IIIT5k
-├── SVT
-├── SVTP
-└── Uber
-u14m # lmdb format
-├── artistic
-├── contextless
-├── curve
-├── general
-├── multi_oriented
-├── multi_words
-└── salient
-Union14M-LMDB-L # lmdb format
-├── train_challenging
-├── train_easy
-├── train_hard
-├── train_medium
-└── train_normal
-```
-
-<a name="3"></a>
-
-## 3. Model Training / Evaluation
-
-Training:
-
-```shell
-# The configuration file is available from the link provided in the table above.
-# Multi GPU training
-CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 tools/train_rec.py --c PATH/svtr_base_igtr_XXX.yml
-```
-
-Evaluation:
-
-```shell
-# The configuration file is available from the link provided in the table above.
-# en
-python tools/eval_rec_all_ratio.py --c PATH/svtr_base_igtr_syn.yml
-# ch
-python tools/eval_rec_all_ch.py --c PATH/svtr_base_igtr_ch_aug.yml
-```
-
-## Citation
-
-```bibtex
-@article{Du2024IGTR,
-  title     = {Instruction-Guided Scene Text Recognition},
-  author    = {Du, Yongkun and Chen, Zhineng and Su, Yuchen and Jia, Caiyan and Jiang, Yu-Gang},
-  journal   = {CoRR},
-  eprinttype = {arXiv},
-  primaryClass={cs.CV},
-  volume    = {abs/2401.17851},
-  year      = {2024},
-  url       = {https://arxiv.org/abs/2401.17851}
-}
-```

configs/rec/igtr/svtr_base_ds_igtr.yml

@@ -1,157 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtr_base_igtr
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path
-  # ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtr_base_igtr.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.0005 # 2gpus 384bs/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: IGTR
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet2DPos
-    img_size: [32, -1]
-    out_char_num: 25
-    out_channels: 256
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: ['ConvB','ConvB','ConvB','ConvB','ConvB','ConvB', 'ConvB','ConvB', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    last_stage: False
-    prenorm: True
-    use_first_sub: False
-  Decoder:
-    name: IGTRDecoder
-    dim: 384
-    num_layer: 1
-    ar: False
-    refine_iter: 0
-    # next_pred: True
-    next_pred: False
-    pos2d: True
-    ds: True
-    # pos_len: False
-    # rec_layer: 1
-
-
-Loss:
-  name: IGTRLoss
-
-PostProcess:
-  name: IGTRLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-Train:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    padding: &padding False
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - PARSeqAug:
-      - IGTRLabelEncode: # Class handling label
-          k: 8
-          prompt_error: False
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'prompt_pos_idx_list',
-          'prompt_char_idx_list', 'ques_pos_idx_list', 'ques1_answer_list',
-          'ques2_char_idx_list', 'ques2_answer_list', 'ques3_answer', 'ques4_char_num_list',
-          'ques_len_list', 'ques2_len_list', 'prompt_len_list', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 384
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    padding: *padding
-    data_dir_list: ['../evaluation/CUTE80',
-    '../evaluation/IC13_857',
-    '../evaluation/IC15_1811',
-    '../evaluation/IIIT5k',
-    '../evaluation/SVT',
-    '../evaluation/SVTP']
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 256
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/lister/focalsvtr_lister_wo_fem_maxratio12.yml

@@ -1,133 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/focalsvtr_lister_wo_fem_maxratio12/
-  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: &character_dict_path ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_focalsvtr_lister_wo_fem_maxratio12.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: LISTER
-  Transform:
-  Encoder:
-    name: FocalSVTR
-    img_size: [32, 128]
-    depths: [6, 6, 9]
-    embed_dim: 96
-    sub_k: [[1, 1], [2, 1], [1, 1]]
-    focal_levels: [3, 3, 3]
-    last_stage: False
-    feat2d: True
-  Decoder:
-    name: LISTERDecoder
-    detach_grad: False
-    attn_scaling: True
-    use_fem: False
-
-Loss:
-  name: LISTERLoss
-
-PostProcess:
-  name: LISTERLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - EPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length']
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: 12
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: ['../evaluation/CUTE80',
-    '../evaluation/IC13_857',
-    '../evaluation/IC15_1811',
-    '../evaluation/IIIT5k',
-    '../evaluation/SVT',
-    '../evaluation/SVTP',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - EPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length']
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: 12
-    num_workers: 4

configs/rec/lister/svtrv2_lister_wo_fem_maxratio12.yml

@@ -1,138 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_lister_wo_fem_maxratio12/
-  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: &character_dict_path ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtrv2_lister_wo_fem_maxratio12.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.000325
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: LISTER
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: LISTERDecoder
-    detach_grad: False
-    attn_scaling: True
-    use_fem: False
-
-Loss:
-  name: LISTERLoss
-
-PostProcess:
-  name: LISTERLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - EPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length']
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 128
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: 12
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: ['../evaluation/CUTE80',
-    '../evaluation/IC13_857',
-    '../evaluation/IC15_1811',
-    '../evaluation/IIIT5k',
-    '../evaluation/SVT',
-    '../evaluation/SVTP',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - EPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: 12
-    num_workers: 4

configs/rec/lpv/svtr_base_lpv.yml

@@ -1,124 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtr_base_lpv/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./output/rec/u14m_filter/svtr_base_lpv_wo_glrm/best.pth
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtr_lpv.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: Adam
-  lr: 0.0001 # for 4gpus bs128/gpu
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-  betas: [0.9, 0.99]
-
-LRScheduler:
-  name: MultiStepLR
-  milestones: [12]
-  gamma: 0.1
-
-Architecture:
-  model_type: rec
-  algorithm: LPV
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet
-    img_size: [32, 128]
-    out_char_num: 25
-    out_channels: 256
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv', 'Conv','Conv', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    sub_k: [[1, 1], [1, 1]]
-    feature2d: True
-    last_stage: False
-    prenorm: True
-  Decoder:
-    name: LPVDecoder
-    num_layer: 3
-    max_len: *max_text_length
-    use_mask: True
-    dim_feedforward: 1536
-    nhead: 12
-    dropout: 0.1
-    trans_layer: 3
-
-Loss:
-  name: LPVLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-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:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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: 128
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation/
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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: 128
-    num_workers: 4

configs/rec/lpv/svtr_base_lpv_wo_glrm.yml

@@ -1,123 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtr_base_lpv_wo_glrm/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtr_base_lpv_wo_glrm.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: Adam
-  lr: 0.0001 # for 4gpus bs128/gpu
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-  betas: [0.9, 0.99]
-
-LRScheduler:
-  name: MultiStepLR
-  milestones: [12]
-  gamma: 0.1
-
-Architecture:
-  model_type: rec
-  algorithm: LPV
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet
-    img_size: [32, 128]
-    out_char_num: 25
-    out_channels: 256
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv', 'Conv','Conv', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    sub_k: [[1, 1], [1, 1]]
-    feature2d: True
-    last_stage: False
-    prenorm: True
-  Decoder:
-    name: LPVDecoder
-    num_layer: 3
-    max_len: *max_text_length
-    use_mask: False
-    dim_feedforward: 1536
-    nhead: 12
-    dropout: 0.1
-    trans_layer: 3
-
-Loss:
-  name: LPVLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-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:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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: 128
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation/
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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: 128
-    num_workers: 4

configs/rec/lpv/svtrv2_lpv.yml

@@ -1,147 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_lpv/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./output/rec/u14m_filter/svtrv2_lpv_wo_glrm/best.pth
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtrv2_lpv.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.000325 # for 4gpus bs128/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: LPV
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: LPVDecoder
-    num_layer: 3
-    max_len: *max_text_length
-    use_mask: True
-    dim_feedforward: 1536
-    nhead: 12
-    dropout: 0.1
-    trans_layer: 3
-
-Loss:
-  name: LPVLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 128
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/lpv/svtrv2_lpv_wo_glrm.yml

@@ -1,146 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_lpv_wo_glrm/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtrv2_lpv_wo_glrm.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.000325 # for 4gpus bs128/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: LPV
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: LPVDecoder
-    num_layer: 3
-    max_len: *max_text_length
-    use_mask: False
-    dim_feedforward: 1536
-    nhead: 12
-    dropout: 0.1
-    trans_layer: 3
-
-Loss:
-  name: LPVLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 128
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/maerec/vit_nrtr.yml

@@ -1,116 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 10
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/vit_nrtr_ft_mae/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./open_ocr_vit_small_params.pth
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_vit_nrtr_ft_mae.txt
-  use_amp: True
-  project_name: maerec
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 :  1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: BGPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: ViT
-    img_size: [32, 128]
-    patch_size: [4, 4]
-    embed_dim: 384
-    depth: 12
-    num_heads: 6
-    mlp_ratio: 4
-    qkv_bias: True
-    use_cls_token: True
-  Decoder:
-    name: NRTRDecoder
-    num_encoder_layers: -1
-    beam_size: 0
-    num_decoder_layers: 6
-    nhead: 8
-    max_len: *max_text_length
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-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:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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: 4

configs/rec/matrn/resnet45_trans_matrn.yml

@@ -1,95 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/resnet45_trans_matrn/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./openocr_nolang_abinet_lang.pth
-  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_resnet45_trans_matrn.txt
-  grad_clip_val: 20
-  use_amp: True
-
-Optimizer:
-  name: Adam
-  lr: 0.000133 # 4gpus 128bs/gpu
-  weight_decay: 0.0
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: MultiStepLR
-  milestones: [12, 18]
-  gamma: 0.1
-
-Architecture:
-  model_type: rec
-  algorithm: MATRN
-  Transform:
-  Encoder:
-    name: ResNet45
-    in_channels: 3
-    strides: [2, 1, 2, 1, 1]
-  Decoder:
-    name: MATRNDecoder
-    iter_size: 3
-
-Loss:
-  name: ABINetLoss
-  align_weight: 3.0
-
-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:
-      - 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: 128
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ABINetLabelEncode:
-      - 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

configs/rec/matrn/svtrv2_matrn.yml

@@ -1,130 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_matrn/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: True
-  pretrained_model:
-  # ./openocr_svtrv2_nolang_abinet_lang.pth
-  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_svtrv2_matrn.txt
-  use_amp: True
-  grad_clip_val: 20
-
-Optimizer:
-  name: AdamW
-  lr: 0.000325 # for 4gpus bs128/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: MATRN
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: True
-  Decoder:
-    name: MATRNDecoder
-    iter_size: 3
-    num_layers: 0
-
-Loss:
-  name: ABINetLoss
-
-PostProcess:
-  name: ABINetLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ABINetLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 128
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ABINetLabelEncode:
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/mgpstr/svtrv2_mgpstr_only_char.yml

@@ -1,140 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtrv2_mgpstr_only_char/
-  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: &character_dict_path ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  use_amp: True
-  save_res_path: ./output/rec/u14m_filter/predicts_svtrv2_mgpstr_only_char.txt
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # 4gpus 256bs/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: MGPSTR
-  Transform:
-  Encoder:
-    name: SVTRv2LNConvTwo33
-    use_pos_embed: False
-    out_channels: 256
-    dims: [128, 256, 384]
-    depths: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: [['Conv','Conv','Conv','Conv','Conv','Conv'],['Conv','Conv','FGlobal','Global','Global','Global'],['Global','Global','Global','Global','Global','Global']]
-    local_k: [[5, 5], [5, 5], [-1, -1]]
-    sub_k: [[1, 1], [2, 1], [-1, -1]]
-    last_stage: false
-    feat2d: false
-  Decoder:
-    name: MGPDecoder
-    only_char: &only_char True
-
-Loss:
-  name: MGPLoss
-  only_char: *only_char
-
-PostProcess:
-  name: MPGLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-  only_char: *only_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: false
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_filter_train_easy',
-    ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 4
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSetTVResize
-    ds_width: True
-    padding: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: *bs
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: *bs
-    max_ratio: *max_ratio
-    num_workers: 4

configs/rec/mgpstr/vit_base_mgpstr_only_char.yml

@@ -1,111 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/vit_base_mgpstr/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: False
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  use_amp: True
-  save_res_path: ./output/rec/u14m_filter/predicts_vit_mgpstr_only_char.txt
-  grad_clip_val: 5
-  project_name: mgpstr_base
-
-Optimizer:
-  name: Adam
-  lr: 0.000325 # 4gpus 128bs/gpu
-  weight_decay: 0.
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: MGPSTR
-  Transform:
-  Encoder:
-    name: ViT
-    img_size: [32,128]
-    patch_size: [4, 4]
-    embed_dim: 768
-    depth: 12
-    num_heads: 12
-    mlp_ratio: 4
-    qkv_bias: True
-  Decoder:
-    name: MGPDecoder
-    only_char: &only_char True
-
-Loss:
-  name: MGPLoss
-  only_char: *only_char
-
-PostProcess:
-  name: MPGLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-  only_char: *only_char
-
-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:
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    batch_size_per_card: 128
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation/
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 256
-    num_workers: 2

configs/rec/mgpstr/vit_large_mgpstr_only_char.yml

@@ -1,110 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/vit_base_mgpstr_only_char/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: False
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  use_amp: True
-  save_res_path: ./output/rec/u14m_filter/predicts_vit_mgpstr_only_char.txt
-  grad_clip_val: 5
-
-Optimizer:
-  name: Adam
-  lr: 0.000325 # 4gpus 128bs/gpu
-  weight_decay: 0.
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: MGPSTR
-  Transform:
-  Encoder:
-    name: ViT
-    img_size: [32,128]
-    patch_size: [4, 4]
-    embed_dim: 1024
-    depth: 24
-    num_heads: 16
-    mlp_ratio: 4
-    qkv_bias: True
-  Decoder:
-    name: MGPDecoder
-    only_char: &only_char True
-
-Loss:
-  name: MGPLoss
-  only_char: *only_char
-
-PostProcess:
-  name: MPGLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-  only_char: *only_char
-
-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:
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    batch_size_per_card: 128
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation/
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 256
-    num_workers: 2

configs/rec/mgpstr/vit_mgpstr.yml

@@ -1,110 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/vit_mgpstr/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [100000, 2000]
-  cal_metric_during_train: False
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  use_amp: True
-  save_res_path: ./output/rec/u14m_filter/predicts_vit_mgpstr.txt
-  grad_clip_val: 5
-
-Optimizer:
-  name: Adam
-  lr: 0.000325 # 4gpus 128bs/gpu
-  weight_decay: 0.
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: MGPSTR
-  Transform:
-  Encoder:
-    name: ViT
-    img_size: [32,128]
-    patch_size: [4, 4]
-    embed_dim: 384
-    depth: 12
-    num_heads: 6
-    mlp_ratio: 4
-    qkv_bias: True
-  Decoder:
-    name: MGPDecoder
-    only_char: &only_char False
-
-Loss:
-  name: MGPLoss
-  only_char: *only_char
-
-PostProcess:
-  name: MPGLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-  only_char: *only_char
-
-Metric:
-  name: RecMPGMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'bpe_label', 'wp_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    batch_size_per_card: 128
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation/
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'bpe_label', 'wp_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 256
-    num_workers: 2

configs/rec/mgpstr/vit_mgpstr_only_char.yml

@@ -1,110 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/vit_mgpstr_only_char/
-  eval_epoch_step: [0, 1]
-  eval_batch_step: [0, 500]
-  cal_metric_during_train: False
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  use_amp: True
-  save_res_path: ./output/rec/u14m_filter/predicts_vit_mgpstr_only_char.txt
-  grad_clip_val: 5
-
-Optimizer:
-  name: Adam
-  lr: 0.000325 # 4gpus 128bs/gpu
-  weight_decay: 0.
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: MGPSTR
-  Transform:
-  Encoder:
-    name: ViT
-    img_size: [32,128]
-    patch_size: [4, 4]
-    embed_dim: 384
-    depth: 12
-    num_heads: 6
-    mlp_ratio: 4
-    qkv_bias: True
-  Decoder:
-    name: MGPDecoder
-    only_char: &only_char True
-
-Loss:
-  name: MGPLoss
-  only_char: *only_char
-
-PostProcess:
-  name: MPGLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-  only_char: *only_char
-
-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:
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    batch_size_per_card: 128
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation/
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - MGPLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-          only_char: *only_char
-      - RecTVResize:
-          image_shape: [32, 128]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'char_label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size_per_card: 256
-    num_workers: 2

configs/rec/moran/resnet31_lstm_moran.yml

@@ -1,92 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/resnet31_lstm_moran
-  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/predicts_moran.txt
-  use_amp: True
-  grad_clip_val: 1.0
-
-Optimizer:
-  name: Adam
-  lr: 0.002 # for 1gpus bs1024/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: False
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: MORAN
-  Transform:
-    name: MORN
-    target_shape: [32, 128]
-  Encoder:
-    name: ResNet_ASTER
-  Decoder:
-    name: ASTERDecoder
-
-Loss:
-  name: ARLoss
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-PostProcess:
-  name: ARLabelDecode
-
-Train:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../Union14M-L-LMDB-Filtered
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - PARSeqAugPIL:
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          padding: False
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    batch_size_per_card: 1024
-    drop_last: True
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: LMDBDataSet
-    data_dir: ../evaluation
-    transforms:
-      - DecodeImagePIL: # load image
-          img_mode: RGB
-      - ARLabelEncode: # Class handling label
-      - RecTVResize:
-          image_shape: [64, 256]
-          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

configs/rec/nrtr/focalsvtr_nrtr_maxraio12.yml

@@ -1,145 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/focalsvtr_nrtr_maxrtio12
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img: ../ltb/img
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_focalsvtr_nrtr_maxrtio12.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: NRTR
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: FocalSVTR
-    img_size: [32, 128]
-    depths: [6, 6, 6]
-    embed_dim: 96
-    sub_k: [[1, 1], [2, 1], [1, 1]]
-    focal_levels: [3, 3, 3]
-    last_stage: False
-  Decoder:
-    name: NRTRDecoder
-    num_encoder_layers: -1
-    beam_size: 0
-    num_decoder_layers: 2
-    nhead: 12
-    max_len: *max_text_length
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-  is_filter: True
-
-Train:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    padding: &padding True
-    padding_rand: True
-    padding_doub: True
-    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
-    '../Union14M-L-LMDB-Filtered/filter_train_hard',
-    '../Union14M-L-LMDB-Filtered/filter_train_medium',
-    '../Union14M-L-LMDB-Filtered/filter_train_normal',
-    '../Union14M-L-LMDB-Filtered/filter_train_easy',
-    ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - PARSeqAug:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: &bs 256
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: True
-    batch_size_per_card: *bs
-    drop_last: True
-    max_ratio: &max_ratio 12
-    num_workers: 4
-
-Eval:
-  dataset:
-    name: RatioDataSet
-    ds_width: True
-    padding: False
-    padding_rand: False
-    data_dir_list: [
-      '../evaluation/CUTE80',
-      '../evaluation/IC13_857',
-      '../evaluation/IC15_1811',
-      '../evaluation/IIIT5k',
-      '../evaluation/SVT',
-      '../evaluation/SVTP',
-      ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - KeepKeys:
-          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
-  sampler:
-    name: RatioSampler
-    scales: [[128, 32]] # w, h
-    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-    first_bs: 128
-    fix_bs: false
-    divided_factor: [4, 16] # w, h
-    is_training: False
-  loader:
-    shuffle: False
-    drop_last: False
-    max_ratio: *max_ratio
-    batch_size_per_card: 128
-    num_workers: 4

configs/rec/nrtr/nrtr.yml

@@ -1,107 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/nrtr/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_nrtr.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: BGPD
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: NRTREncoder
-  Decoder:
-    name: NRTRDecoder
-    num_encoder_layers: 6
-    beam_size: 0
-    num_decoder_layers: 6
-    nhead: 8
-    max_len: *max_text_length
-
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-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:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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

configs/rec/nrtr/svtr_base_nrtr.yml

@@ -1,118 +0,0 @@
-Global:
-  device: gpu
-  epoch_num: 20
-  log_smooth_window: 20
-  print_batch_step: 10
-  output_dir: ./output/rec/u14m_filter/svtr_base_nrtr/
-  save_epoch_step: 1
-  # evaluation is run every 2000 iterations
-  eval_batch_step: [0, 500]
-  eval_epoch_step: [0, 1]
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  use_tensorboard: false
-  infer_img:
-  # for data or label process
-  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
-  # ./tools/utils/ppocr_keys_v1.txt  # ch
-  max_text_length: &max_text_length 25
-  use_space_char: &use_space_char False
-  save_res_path: ./output/rec/u14m_filter/predicts_svtr_base_nrtr.txt
-  use_amp: True
-
-Optimizer:
-  name: AdamW
-  lr: 0.00065 # for 4gpus bs256/gpu
-  weight_decay: 0.05
-  filter_bias_and_bn: True
-
-LRScheduler:
-  name: OneCycleLR
-  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
-  cycle_momentum: False
-
-Architecture:
-  model_type: rec
-  algorithm: NRTR
-  in_channels: 3
-  Transform:
-  Encoder:
-    name: SVTRNet
-    img_size: [32, 128]
-    out_char_num: 25
-    out_channels: 256
-    patch_merging: 'Conv'
-    embed_dim: [128, 256, 384]
-    depth: [6, 6, 6]
-    num_heads: [4, 8, 12]
-    mixer: ['Conv','Conv','Conv','Conv','Conv','Conv', 'Conv','Conv', 'Global','Global','Global','Global','Global','Global','Global','Global','Global','Global']
-    local_mixer: [[5, 5], [5, 5], [5, 5]]
-    last_stage: False
-    prenorm: True
-  Decoder:
-    name: NRTRDecoder
-    num_encoder_layers: -1
-    beam_size: 0
-    num_decoder_layers: 2
-    nhead: 12
-    max_len: *max_text_length
-
-
-Loss:
-  name: ARLoss
-
-PostProcess:
-  name: ARLabelDecode
-  character_dict_path: *character_dict_path
-  use_space_char: *use_space_char
-
-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:
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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
-      - ARLabelEncode: # Class handling label
-          character_dict_path: *character_dict_path
-          use_space_char: *use_space_char
-          max_text_length: *max_text_length
-      - 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