用自己的数据训练SER，效果不好，都没有往我标注的地方画框，为啥呢？

gg22mm commented 1 year ago

一、训练：python tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.pretrained=True

二、我试着预测： python tools/infer_kie_token_ser.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy Global.infer_img=./train_data/XFUND/zh_train/image/IMG_20221026_093350.jpg

三、看看我自己的标注图如下（明显与上面识别出来的相差太远了）：

[{ "transcription": "江苏中烟工业有限责任公司", "label": "header", "points": [ [1765, 537], [2950, 565], [2947, 658], [1762, 630] ], "id": 0, "linking": [] }, { "transcription": "年　份", "label": "question", "points": [ [1363, 731], [1607, 731], [1607, 800], [1363, 800] ], "id": 1, "linking": [ [1, 2] ] }, { "transcription": "2020", "label": "answer", "points": [ [1946, 738], [2082, 738], [2082, 808], [1946, 808] ], "id": 2, "linking": [ [1, 2] ] }, { "transcription": "等级", "label": "question", "points": [ [2386, 838], [2688, 848], [2684, 977], [2381, 967] ], "id": 17, "linking": [ [17, 18] ] }, { "transcription": "产地", "label": "question", "points": [ [1359, 859], [1607, 859], [1607, 922], [1359, 922] ], "id": 3, "linking": [ [3, 4] ] }, { "transcription": "福建三明宁化", "label": "answer", "points": [ [1839, 871], [2193, 871], [2193, 937], [1839, 937] ], "id": 4, "linking": [ [3, 4] ] }, { "transcription": "BCCFAH", "label": "answer", "points": [ [2863, 866], [3216, 875], [3213, 972], [2861, 963] ], "id": 18, "linking": [ [17, 18] ] }, { "transcription": "主类型", "label": "question", "points": [ [197, 967], [299, 973], [293, 1015], [189, 1010] ], "id": 25, "linking": [ [25, 26] ] }, { "transcription": "2020", "label": "answer", "points": [ [731, 983], [819, 981], [819, 1019], [730, 1015] ], "id": 52, "linking": [ [51, 52] ] }, { "transcription": "类型", "label": "question", "points": [ [1359, 983], [1607, 983], [1607, 1059], [1359, 1059] ], "id": 5, "linking": [ [5, 6] ] }, { "transcription": "烤烟", "label": "answer", "points": [ [1953, 995], [2082, 995], [2082, 1069], [1953, 1069] ], "id": 6, "linking": [ [5, 6] ] }, { "transcription": "主品种", "label": "question", "points": [ [194, 1026], [289, 1026], [289, 1072], [194, 1072] ], "id": 27, "linking": [ [27, 28] ] }, { "transcription": "主等级", "label": "question", "points": [ [611, 1032], [708, 1038], [706, 1079], [609, 1075] ], "id": 53, "linking": [ [53, 54] ] }, { "transcription": "福建三明宁化", "label": "answer", "points": [ [304, 1086], [509, 1088], [509, 1131], [303, 1128] ], "id": 30, "linking": [ [29, 30] ] }, { "transcription": "品种", "label": "question", "points": [ [1355, 1123], [1607, 1123], [1607, 1192], [1355, 1192] ], "id": 7, "linking": [ [7, 8] ] }, { "transcription": "CB-1", "label": "answer", "points": [ [1957, 1126], [2088, 1136], [2083, 1198], [1952, 1188] ], "id": 8, "linking": [ [7, 8] ] }, { "transcription": "混打片烟", "label": "answer", "points": [ [298, 1152], [441, 1151], [440, 1194], [294, 1189] ], "id": 32, "linking": [ [31, 32] ] }, { "transcription": "A线-丙班", "label": "answer", "points": [ [720, 1158], [869, 1157], [865, 1205], [716, 1195] ], "id": 56, "linking": [ [55, 56] ] }, { "transcription": "箱号", "label": "question", "points": [ [2428, 1139], [2691, 1139], [2691, 1206], [2428, 1206] ], "id": 19, "linking": [ [19, 20] ] }, { "transcription": "0528", "label": "answer", "points": [ [2998, 1143], [3131, 1143], [3131, 1213], [2998, 1213] ], "id": 20, "linking": [ [19, 20] ] }, { "transcription": "202011JS008", "label": "answer", "points": [ [294, 1209], [498, 1216], [489, 1251], [289, 1248] ], "id": 34, "linking": [ [33, 34] ] }, { "transcription": "箱号", "label": "question", "points": [ [589, 1213], [688, 1213], [688, 1251], [589, 1251] ], "id": 57, "linking": [ [57, 58] ] }, { "transcription": "毛重", "label": "question", "points": [ [156, 1263], [263, 1267], [256, 1306], [156, 1306] ], "id": 35, "linking": [ [35, 36] ] }, { "transcription": "200.00KG", "label": "answer", "points": [ [710, 1270], [859, 1271], [858, 1314], [710, 1310] ], "id": 60, "linking": [ [59, 60] ] }, { "transcription": "毛重", "label": "question", "points": [ [1355, 1259], [1607, 1259], [1607, 1335], [1355, 1335] ], "id": 9, "linking": [ [9, 10] ] }, { "transcription": "净重", "label": "question", "points": [ [589, 1275], [688, 1275], [688, 1310], [589, 1310] ], "id": 59, "linking": [ [59, 60] ] }, { "transcription": "215. 9KG", "label": "answer", "points": [ [1911, 1267], [2139, 1267], [2139, 1333], [1911, 1333] ], "id": 10, "linking": [ [9, 10] ] }, { "transcription": "净重", "label": "question", "points": [ [2436, 1271], [2694, 1271], [2694, 1358], [2436, 1358] ], "id": 21, "linking": [ [21, 22] ] }, { "transcription": "200KG", "label": "answer", "points": [ [3006, 1282], [3158, 1282], [3158, 1341], [3006, 1341] ], "id": 22, "linking": [ [21, 22] ] }, { "transcription": "生产日期", "label": "question", "points": [ [151, 1328], [285, 1328], [285, 1372], [151, 1372] ], "id": 37, "linking": [ [37, 38] ] }, { "transcription": "班次", "label": "question", "points": [ [1349, 1399], [1607, 1399], [1607, 1469], [1349, 1469] ], "id": 11, "linking": [ [11, 12] ] }, { "transcription": "丙班", "label": "answer", "points": [ [1965, 1403], [2098, 1403], [2098, 1477], [1965, 1477] ], "id": 12, "linking": [ [11, 12] ] }, { "transcription": "加工日期", "label": "question", "points": [ [2445, 1398], [2687, 1408], [2685, 1482], [2443, 1472] ], "id": 23, "linking": [ [23, 24] ] }, { "transcription": "2020. 11.17", "label": "answer", "points": [ [2945, 1415], [3241, 1415], [3241, 1481], [2945, 1481] ], "id": 24, "linking": [ [23, 24] ] }, { "transcription": "类型", "label": "question", "points": [ [135, 1452], [210, 1452], [200, 1493], [128, 1492] ], "id": 39, "linking": [ [39, 40] ] }, { "transcription": "CB-1", "label": "answer", "points": [ [218, 1508], [304, 1508], [304, 1546], [218, 1546] ], "id": 42, "linking": [ [41, 42] ] }, { "transcription": "监打人员", "label": "question", "points": [ [1349, 1543], [1596, 1543], [1596, 1613], [1349, 1613] ], "id": 13, "linking": [ [13, 14] ] }, { "transcription": "王错　朱文战　栗云超　李明", "label": "answer", "points": [ [2139, 1543], [3018, 1543], [3018, 1625], [2139, 1625] ], "id": 14, "linking": [ [13, 14] ] }, { "transcription": "BCCFAH", "label": "answer", "points": [ [213, 1558], [331, 1558], [331, 1600], [213, 1600] ], "id": 44, "linking": [ [43, 44] ] }, { "transcription": "福建三明宁化", "label": "answer", "points": [ [213, 1613], [422, 1613], [422, 1654], [213, 1654] ], "id": 46, "linking": [ [45, 46] ] }, { "transcription": "混打片烟", "label": "answer", "points": [ [198, 1675], [350, 1675], [350, 1716], [198, 1716] ], "id": 48, "linking": [ [47, 48] ] }, { "transcription": "加工单位", "label": "question", "points": [ [1341, 1687], [1596, 1687], [1596, 1757], [1341, 1757] ], "id": 15, "linking": [ [15, 16] ] }, { "transcription": "福建省三明金叶复烤有限公司", "label": "answer", "points": [ [2196, 1691], [2968, 1691], [2968, 1757], [2196, 1757] ], "id": 16, "linking": [ [15, 16] ] }, { "transcription": "2020", "label": "answer", "points": [ [193, 1733], [277, 1733], [277, 1777], [193, 1777] ], "id": 50, "linking": [ [49, 50] ] }, { "transcription": "烤烟", "label": "answer", "points": [ [318, 969], [409, 969], [409, 1012], [318, 1012] ], "id": 26, "linking": [ [25, 26] ] }, { "transcription": "CB-1", "label": "answer", "points": [ [305, 1037], [401, 1037], [401, 1065], [305, 1065] ], "id": 28, "linking": [ [27, 28] ] }, { "transcription": "主年份", "label": "question", "points": [ [606, 973], [709, 973], [709, 1021], [606, 1021] ], "id": 51, "linking": [ [51, 52] ] }, { "transcription": "C3F", "label": "answer", "points": [ [730, 1040], [798, 1040], [798, 1077], [730, 1077] ], "id": 54, "linking": [ [53, 54] ] }, { "transcription": "主产地", "label": "question", "points": [ [174, 1084], [279, 1084], [279, 1132], [174, 1132] ], "id": 29, "linking": [ [29, 30] ] }, { "transcription": "主形态", "label": "question", "points": [ [169, 1147], [274, 1147], [274, 1194], [169, 1194] ], "id": 31, "linking": [ [31, 32] ] }, { "transcription": "生产线", "label": "question", "points": [ [590, 1157], [695, 1157], [695, 1196], [590, 1196] ], "id": 55, "linking": [ [55, 56] ] }, { "transcription": "批次", "label": "question", "points": [ [162, 1204], [267, 1204], [267, 1254], [162, 1254] ], "id": 33, "linking": [ [33, 34] ] }, { "transcription": "215.90KG", "label": "answer", "points": [ [285, 1264], [431, 1264], [431, 1314], [285, 1314] ], "id": 36, "linking": [ [35, 36] ] }, { "transcription": "528", "label": "answer", "points": [ [718, 1216], [780, 1216], [780, 1257], [718, 1257] ], "id": 58, "linking": [ [57, 58] ] }, { "transcription": "2020-11-17", "label": "answer", "points": [ [311, 1330], [493, 1330], [493, 1379], [311, 1379] ], "id": 38, "linking": [ [37, 38] ] }, { "transcription": "烤烟", "label": "answer", "points": [ [230, 1449], [313, 1449], [313, 1494], [230, 1494] ], "id": 40, "linking": [ [39, 40] ] }, { "transcription": "品种", "label": "question", "points": [ [121, 1506], [197, 1506], [197, 1552], [121, 1552] ], "id": 41, "linking": [ [41, 42] ] }, { "transcription": "等级", "label": "question", "points": [ [118, 1558], [192, 1558], [192, 1606], [118, 1606] ], "id": 43, "linking": [ [43, 44] ] }, { "transcription": "产地", "label": "question", "points": [ [110, 1612], [183, 1612], [183, 1658], [110, 1658] ], "id": 45, "linking": [ [45, 46] ] }, { "transcription": "形态", "label": "question", "points": [ [100, 1673], [177, 1673], [177, 1721], [100, 1721] ], "id": 47, "linking": [ [47, 48] ] }, { "transcription": "年份", "label": "question", "points": [ [94, 1732], [172, 1732], [172, 1784], [94, 1784] ], "id": 49, "linking": [ [49, 50] ] }]

andyjiang1116 commented 1 year ago

训练：python tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.pretrained=True

看报错应该是机器内存的问题

xiaocode337317439 commented 1 year ago

linking

使用PPOCRLabel kie 是如何标注 linking 的，老哥求分享？

gavinfaimdata commented 1 year ago

The space between those characters is really big. I've had worse results trying to do OCR on texts with awkward spacing like that. If you could get them to change the character spacing I think this problem would be resolved immediately, although that may not be an option.

gg22mm commented 1 year ago

训练：python tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.pretrained=True

看报错应该是机器内存的问题

不应该的， batch_size_per_card: 2 ，显存又那么大，机子肯定是没有问题的

22/12/07 14:21:09] ppocr INFO: cur metric, precision: 0.8890551917680075, recall: 0.96, hmean: 0.9231665857212238, fps: 7.39328005524394 [2022/12/07 14:21:09] ppocr INFO: best metric, hmean: 1.0, precision: 1.0, recall: 1.0, fps: 7.489523330123297, best_epoch: 2 Traceback (most recent call last): File "tools/train.py", line 208, in main(config, device, logger, vdl_writer) File "tools/train.py", line 183, in main amp_level, amp_custom_black_list) File "D:\dzkj\PaddleOCR26\tools\program.py", line 441, in train global_step=global_step) File "D:\dzkj\PaddleOCR26\ppocr\utils\save_load.py", line 198, in save_model paddle.save(optimizer.state_dict(), model_prefix + '.pdopt') File "C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\paddle\framework\io.py", line 732, in save _legacy_save(obj, path, protocol) File "C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\paddle\framework\io.py", line 785, in _legacy_save pickle.dump(saved_obj, f, protocol=protocol) MemoryError -----------------------我尝试修改--------------------------- vi C:\ProgramData\Anaconda3\envs\py37\lib\site-packages\paddle\framework\io.py

# When value of dict is lager than 4GB ,there is a Bug on 'MAC python3'
if _is_file_path(
        path) and sys.platform == 'darwin' and sys.version_info.major == 3:
    pickle_bytes = pickle.dumps(saved_obj, protocol=protocol)
    with open(path, 'wb') as f:
        max_bytes = 2**30
        for i in range(0, len(pickle_bytes), max_bytes):
            f.write(pickle_bytes[i:i + max_bytes])
else:
    with _open_file_buffer(path, 'wb') as f:
        import joblib            
        #pickle.dump(saved_obj, f, protocol=protocol) #禁用官方原报错代码，但是load估计也得改
        joblib.dump(saved_obj, f)

gavinfaimdata commented 1 year ago

try making the eval batch size=1

gg22mm commented 1 year ago

try making the eval batch size=1

这个估计是代码问题： batch_size_per_card: 1 也没用

gg22mm commented 1 year ago

我试了用 aistudio 运行没有问题

leoterry-ulrica commented 1 year ago

一、训练：python tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.pretrained=True

二、我试着预测： python tools/infer_kie_token_ser.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./output/ser_vi_layoutxlm_xfund_zh/best_accuracy Global.infer_img=./train_data/XFUND/zh_train/image/IMG_20221026_093350.jpg

三、看看我自己的标注图如下（明显与上面识别出来的相差太远了）：

[{ "transcription": "江苏中烟工业有限责任公司", "label": "header", "points": [ [1765, 537], [2950, 565], [2947, 658], [1762, 630] ], "id": 0, "linking": [] }, { "transcription": "年　份", "label": "question", "points": [ [1363, 731], [1607, 731], [1607, 800], [1363, 800] ], "id": 1, "linking": [ [1, 2] ] }, { "transcription": "2020", "label": "answer", "points": [ [1946, 738], [2082, 738], [2082, 808], [1946, 808] ], "id": 2, "linking": [ [1, 2] ] }, { "transcription": "等级", "label": "question", "points": [ [2386, 838], [2688, 848], [2684, 977], [2381, 967] ], "id": 17, "linking": [ [17, 18] ] }, { "transcription": "产地", "label": "question", "points": [ [1359, 859], [1607, 859], [1607, 922], [1359, 922] ], "id": 3, "linking": [ [3, 4] ] }, { "transcription": "福建三明宁化", "label": "answer", "points": [ [1839, 871], [2193, 871], [2193, 937], [1839, 937] ], "id": 4, "linking": [ [3, 4] ] }, { "transcription": "BCCFAH", "label": "answer", "points": [ [2863, 866], [3216, 875], [3213, 972], [2861, 963] ], "id": 18, "linking": [ [17, 18] ] }, { "transcription": "主类型", "label": "question", "points": [ [197, 967], [299, 973], [293, 1015], [189, 1010] ], "id": 25, "linking": [ [25, 26] ] }, { "transcription": "2020", "label": "answer", "points": [ [731, 983], [819, 981], [819, 1019], [730, 1015] ], "id": 52, "linking": [ [51, 52] ] }, { "transcription": "类型", "label": "question", "points": [ [1359, 983], [1607, 983], [1607, 1059], [1359, 1059] ], "id": 5, "linking": [ [5, 6] ] }, { "transcription": "烤烟", "label": "answer", "points": [ [1953, 995], [2082, 995], [2082, 1069], [1953, 1069] ], "id": 6, "linking": [ [5, 6] ] }, { "transcription": "主品种", "label": "question", "points": [ [194, 1026], [289, 1026], [289, 1072], [194, 1072] ], "id": 27, "linking": [ [27, 28] ] }, { "transcription": "主等级", "label": "question", "points": [ [611, 1032], [708, 1038], [706, 1079], [609, 1075] ], "id": 53, "linking": [ [53, 54] ] }, { "transcription": "福建三明宁化", "label": "answer", "points": [ [304, 1086], [509, 1088], [509, 1131], [303, 1128] ], "id": 30, "linking": [ [29, 30] ] }, { "transcription": "品种", "label": "question", "points": [ [1355, 1123], [1607, 1123], [1607, 1192], [1355, 1192] ], "id": 7, "linking": [ [7, 8] ] }, { "transcription": "CB-1", "label": "answer", "points": [ [1957, 1126], [2088, 1136], [2083, 1198], [1952, 1188] ], "id": 8, "linking": [ [7, 8] ] }, { "transcription": "混打片烟", "label": "answer", "points": [ [298, 1152], [441, 1151], [440, 1194], [294, 1189] ], "id": 32, "linking": [ [31, 32] ] }, { "transcription": "A线-丙班", "label": "answer", "points": [ [720, 1158], [869, 1157], [865, 1205], [716, 1195] ], "id": 56, "linking": [ [55, 56] ] }, { "transcription": "箱号", "label": "question", "points": [ [2428, 1139], [2691, 1139], [2691, 1206], [2428, 1206] ], "id": 19, "linking": [ [19, 20] ] }, { "transcription": "0528", "label": "answer", "points": [ [2998, 1143], [3131, 1143], [3131, 1213], [2998, 1213] ], "id": 20, "linking": [ [19, 20] ] }, { "transcription": "202011JS008", "label": "answer", "points": [ [294, 1209], [498, 1216], [489, 1251], [289, 1248] ], "id": 34, "linking": [ [33, 34] ] }, { "transcription": "箱号", "label": "question", "points": [ [589, 1213], [688, 1213], [688, 1251], [589, 1251] ], "id": 57, "linking": [ [57, 58] ] }, { "transcription": "毛重", "label": "question", "points": [ [156, 1263], [263, 1267], [256, 1306], [156, 1306] ], "id": 35, "linking": [ [35, 36] ] }, { "transcription": "200.00KG", "label": "answer", "points": [ [710, 1270], [859, 1271], [858, 1314], [710, 1310] ], "id": 60, "linking": [ [59, 60] ] }, { "transcription": "毛重", "label": "question", "points": [ [1355, 1259], [1607, 1259], [1607, 1335], [1355, 1335] ], "id": 9, "linking": [ [9, 10] ] }, { "transcription": "净重", "label": "question", "points": [ [589, 1275], [688, 1275], [688, 1310], [589, 1310] ], "id": 59, "linking": [ [59, 60] ] }, { "transcription": "215. 9KG", "label": "answer", "points": [ [1911, 1267], [2139, 1267], [2139, 1333], [1911, 1333] ], "id": 10, "linking": [ [9, 10] ] }, { "transcription": "净重", "label": "question", "points": [ [2436, 1271], [2694, 1271], [2694, 1358], [2436, 1358] ], "id": 21, "linking": [ [21, 22] ] }, { "transcription": "200KG", "label": "answer", "points": [ [3006, 1282], [3158, 1282], [3158, 1341], [3006, 1341] ], "id": 22, "linking": [ [21, 22] ] }, { "transcription": "生产日期", "label": "question", "points": [ [151, 1328], [285, 1328], [285, 1372], [151, 1372] ], "id": 37, "linking": [ [37, 38] ] }, { "transcription": "班次", "label": "question", "points": [ [1349, 1399], [1607, 1399], [1607, 1469], [1349, 1469] ], "id": 11, "linking": [ [11, 12] ] }, { "transcription": "丙班", "label": "answer", "points": [ [1965, 1403], [2098, 1403], [2098, 1477], [1965, 1477] ], "id": 12, "linking": [ [11, 12] ] }, { "transcription": "加工日期", "label": "question", "points": [ [2445, 1398], [2687, 1408], [2685, 1482], [2443, 1472] ], "id": 23, "linking": [ [23, 24] ] }, { "transcription": "2020. 11.17", "label": "answer", "points": [ [2945, 1415], [3241, 1415], [3241, 1481], [2945, 1481] ], "id": 24, "linking": [ [23, 24] ] }, { "transcription": "类型", "label": "question", "points": [ [135, 1452], [210, 1452], [200, 1493], [128, 1492] ], "id": 39, "linking": [ [39, 40] ] }, { "transcription": "CB-1", "label": "answer", "points": [ [218, 1508], [304, 1508], [304, 1546], [218, 1546] ], "id": 42, "linking": [ [41, 42] ] }, { "transcription": "监打人员", "label": "question", "points": [ [1349, 1543], [1596, 1543], [1596, 1613], [1349, 1613] ], "id": 13, "linking": [ [13, 14] ] }, { "transcription": "王错　朱文战　栗云超　李明", "label": "answer", "points": [ [2139, 1543], [3018, 1543], [3018, 1625], [2139, 1625] ], "id": 14, "linking": [ [13, 14] ] }, { "transcription": "BCCFAH", "label": "answer", "points": [ [213, 1558], [331, 1558], [331, 1600], [213, 1600] ], "id": 44, "linking": [ [43, 44] ] }, { "transcription": "福建三明宁化", "label": "answer", "points": [ [213, 1613], [422, 1613], [422, 1654], [213, 1654] ], "id": 46, "linking": [ [45, 46] ] }, { "transcription": "混打片烟", "label": "answer", "points": [ [198, 1675], [350, 1675], [350, 1716], [198, 1716] ], "id": 48, "linking": [ [47, 48] ] }, { "transcription": "加工单位", "label": "question", "points": [ [1341, 1687], [1596, 1687], [1596, 1757], [1341, 1757] ], "id": 15, "linking": [ [15, 16] ] }, { "transcription": "福建省三明金叶复烤有限公司", "label": "answer", "points": [ [2196, 1691], [2968, 1691], [2968, 1757], [2196, 1757] ], "id": 16, "linking": [ [15, 16] ] }, { "transcription": "2020", "label": "answer", "points": [ [193, 1733], [277, 1733], [277, 1777], [193, 1777] ], "id": 50, "linking": [ [49, 50] ] }, { "transcription": "烤烟", "label": "answer", "points": [ [318, 969], [409, 969], [409, 1012], [318, 1012] ], "id": 26, "linking": [ [25, 26] ] }, { "transcription": "CB-1", "label": "answer", "points": [ [305, 1037], [401, 1037], [401, 1065], [305, 1065] ], "id": 28, "linking": [ [27, 28] ] }, { "transcription": "主年份", "label": "question", "points": [ [606, 973], [709, 973], [709, 1021], [606, 1021] ], "id": 51, "linking": [ [51, 52] ] }, { "transcription": "C3F", "label": "answer", "points": [ [730, 1040], [798, 1040], [798, 1077], [730, 1077] ], "id": 54, "linking": [ [53, 54] ] }, { "transcription": "主产地", "label": "question", "points": [ [174, 1084], [279, 1084], [279, 1132], [174, 1132] ], "id": 29, "linking": [ [29, 30] ] }, { "transcription": "主形态", "label": "question", "points": [ [169, 1147], [274, 1147], [274, 1194], [169, 1194] ], "id": 31, "linking": [ [31, 32] ] }, { "transcription": "生产线", "label": "question", "points": [ [590, 1157], [695, 1157], [695, 1196], [590, 1196] ], "id": 55, "linking": [ [55, 56] ] }, { "transcription": "批次", "label": "question", "points": [ [162, 1204], [267, 1204], [267, 1254], [162, 1254] ], "id": 33, "linking": [ [33, 34] ] }, { "transcription": "215.90KG", "label": "answer", "points": [ [285, 1264], [431, 1264], [431, 1314], [285, 1314] ], "id": 36, "linking": [ [35, 36] ] }, { "transcription": "528", "label": "answer", "points": [ [718, 1216], [780, 1216], [780, 1257], [718, 1257] ], "id": 58, "linking": [ [57, 58] ] }, { "transcription": "2020-11-17", "label": "answer", "points": [ [311, 1330], [493, 1330], [493, 1379], [311, 1379] ], "id": 38, "linking": [ [37, 38] ] }, { "transcription": "烤烟", "label": "answer", "points": [ [230, 1449], [313, 1449], [313, 1494], [230, 1494] ], "id": 40, "linking": [ [39, 40] ] }, { "transcription": "品种", "label": "question", "points": [ [121, 1506], [197, 1506], [197, 1552], [121, 1552] ], "id": 41, "linking": [ [41, 42] ] }, { "transcription": "等级", "label": "question", "points": [ [118, 1558], [192, 1558], [192, 1606], [118, 1606] ], "id": 43, "linking": [ [43, 44] ] }, { "transcription": "产地", "label": "question", "points": [ [110, 1612], [183, 1612], [183, 1658], [110, 1658] ], "id": 45, "linking": [ [45, 46] ] }, { "transcription": "形态", "label": "question", "points": [ [100, 1673], [177, 1673], [177, 1721], [100, 1721] ], "id": 47, "linking": [ [47, 48] ] }, { "transcription": "年份", "label": "question", "points": [ [94, 1732], [172, 1732], [172, 1784], [94, 1784] ], "id": 49, "linking": [ [49, 50] ] }]

使用PPOCRLabel kie 是如何标注 linking 的？ @gg22mm @andyjpaddle

andyjiang1116 commented 1 year ago

暂不支持标注linking关系

gavinfaimdata commented 1 year ago

It's a memory error.. watch your RAM and disk usage

PaddlePaddle / PaddleOCR

用自己的数据训练SER，效果不好，都没有往我标注的地方画框，为啥呢？ #8456