Hugging Face's logo

pk5ls20
/
PaddleModel

ONNX
Model card Files
xet
 Community
PaddleModel
File size: 3,357 Bytes
5d46e17
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
Global:
  use_gpu: true
  epoch_num: 400
  log_smooth_window: 20
  print_batch_step: 5
  output_dir: ./output/table_mv3/
  save_epoch_step: 400
  # evaluation is run every 400 iterations after the 0th iteration
  eval_batch_step: [0, 400]
  cal_metric_during_train: True
  pretrained_model:
  checkpoints:
  save_inference_dir:
  use_visualdl: False
  infer_img: ppstructure/docs/table/table.jpg
  save_res_path: output/table_mv3
  # for data or label process
  character_dict_path: ppocr/utils/dict/table_structure_dict.txt
  character_type: en
  max_text_length: &max_text_length 500
  box_format: &box_format 'xyxy' # 'xywh', 'xyxy', 'xyxyxyxy'
  infer_mode: False
  amp_custom_black_list: ['matmul_v2','elementwise_add']

Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  clip_norm: 5.0
  lr:
    learning_rate: 0.001
  regularizer:
    name: 'L2'
    factor: 0.00000

Architecture:
  model_type: table
  algorithm: TableAttn
  Backbone:
    name: MobileNetV3
    scale: 1.0
    model_name: small
    disable_se: true
  Head:
    name: TableAttentionHead
    hidden_size: 256
    max_text_length: *max_text_length
    loc_reg_num: &loc_reg_num 4

Loss:
  name: TableAttentionLoss
  structure_weight: 100.0
  loc_weight: 10000.0

PostProcess:
  name: TableLabelDecode

Metric:
  name: TableMetric
  main_indicator: acc
  compute_bbox_metric: false # cost many time, set False for training

Train:
  dataset:
    name: PubTabDataSet
    data_dir: train_data/table/pubtabnet/train/
    label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - TableLabelEncode:
          learn_empty_box: False
          merge_no_span_structure: False
          replace_empty_cell_token: False
          loc_reg_num: *loc_reg_num
          max_text_length: *max_text_length
      - TableBoxEncode:
      - ResizeTableImage:
          max_len: 488
      - NormalizeImage:
          scale: 1./255.
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
          order: 'hwc'
      - PaddingTableImage:
          size: [488, 488]
      - ToCHWImage:
      - KeepKeys:
          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
  loader:
    shuffle: True
    batch_size_per_card: 48
    drop_last: True
    num_workers: 1

Eval:
  dataset:
    name: PubTabDataSet
    data_dir: train_data/table/pubtabnet/val/
    label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - TableLabelEncode:
          learn_empty_box: False
          merge_no_span_structure: False
          replace_empty_cell_token: False
          loc_reg_num: *loc_reg_num
          max_text_length: *max_text_length
      - TableBoxEncode:
      - ResizeTableImage:
          max_len: 488
      - NormalizeImage:
          scale: 1./255.
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
          order: 'hwc'
      - PaddingTableImage:
          size: [488, 488]
      - ToCHWImage:
      - KeepKeys:
          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 48
    num_workers: 1