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đ Introduction¶
PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePaddle framework. It integrates numerousready-to-use pre-trained models, enablingfull-process developmentfrom model training to inference, supportinga variety of mainstream hardware both domestic and international, and aiding AI developers in industrial practice.
đ ī¸ Installation¶
Warning
Before installing PaddleX, please ensure that you have a basic Python runtime environment (Note: Currently supports Python 3.8 to Python 3.12). The PaddleX 3.0-rc0 version depends on PaddlePaddle version 3.0.0rc0 and above.
Installing PaddlePaddle¶
Tip
There is no need to pay attention to the CUDA version on the physical machine; just focus on the GPU driver version. For more information on PaddlePaddle Wheel versions, please refer to the PaddlePaddle Official Website.
Installing PaddleX¶
â For more installation methods, please refer to the PaddleX Installation Guide
đģ Command Line Usage¶
A single command can quickly experience the pipeline effect, with a unified command line format as follows:
Each pipeline in PaddleX corresponds to specific parameters. You can find detailed parameter descriptions in the respective pipeline documentation. Each pipeline requires three essential parameters:
pipeline
: The name of the pipeline or the path to the pipeline configuration file.input
: The local path, directory, or URL of the input file to be processed (e.g., an image).device
: The hardware device and its index to be used (e.g.,gpu:0
indicates using the first GPU). You can also choose to use NPU (npu:0
), XPU (xpu:0
), CPU (cpu
), etc.
OCR-related Pipelines CLI
paddlex --pipeline OCR \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'general_ocr_002.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': False}, 'angle': 0},'dt_polys': [array([[ 3, 10],[82, 10],[82, 33],[ 3, 33]], dtype=int16), ...], 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': [-1, ...], 'text_rec_score_thresh': 0.0, 'rec_texts': ['www.99*', ...], 'rec_scores': [0.8980069160461426, ...], 'rec_polys': [array([[ 3, 10],[82, 10],[82, 33],[ 3, 33]], dtype=int16), ...], 'rec_boxes': array([[ 3, 10, 82, 33], ...], dtype=int16)}}
paddlex --pipeline table_recognition \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': '/root/.paddlex/predict_input/table_recognition.jpg', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_layout_detection': True, 'use_ocr_model': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 8, 'label': 'table', 'score': 0.9730289578437805, 'coordinate': [0.77032924, 0.0992564, 550.78864, 127.53717]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[234, 6],
...,
[234, 25]],
...,
[[448, 101],
...,
[448, 121]]], dtype=int16), 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0, 'rec_texts': ['CRuncover', 'Dres', 'čŋįģåˇĨäŊ3', 'ååēæĨæžå¨įŊä¸', 'æ˛Ąæŗ', 'æąãæ´æąįå
Ģ大', 'Abstr', 'rSrivi', '$709.', 'cludingGiv', '2.72', 'Ingcubic', '$744.78'], 'rec_scores': array([0.99612021, ..., 0.99815977]), 'rec_polys': array([[[234, 6],
...,
[234, 25]],
...,
[[448, 101],
...,
[448, 121]]], dtype=int16), 'rec_boxes': array([[234, ..., 25],
...,
[448, ..., 121]], dtype=int16)}, 'table_res_list': [{'cell_box_list': array([[ 3.77032924, ..., 27.0992564 ],
...,
[403.77032924, ..., 125.0992564 ]]), 'pred_html': '<html><body><table><tr><td colspan="4">CRuncover</td></tr><tr><td>Dres</td><td>čŋįģåˇĨäŊ3</td><td>ååēæĨæžå¨įŊä¸ æ˛Ąæŗ</td><td>æąãæ´æąįå
Ģ大</td></tr><tr><td>Abstr</td><td></td><td>rSrivi</td><td>$709.</td></tr><tr><td>cludingGiv</td><td>2.72</td><td>Ingcubic</td><td>$744.78</td></tr></table></body></html>', 'table_ocr_pred': {'rec_polys': array([[[234, 6],
...,
[234, 25]],
...,
[[448, 101],
...,
[448, 121]]], dtype=int16), 'rec_texts': ['CRuncover', 'Dres', 'čŋįģåˇĨäŊ3', 'ååēæĨæžå¨įŊä¸', 'æ˛Ąæŗ', 'æąãæ´æąįå
Ģ大', 'Abstr', 'rSrivi', '$709.', 'cludingGiv', '2.72', 'Ingcubic', '$744.78'], 'rec_scores': array([0.99612021, ..., 0.99815977]), 'rec_boxes': array([[234, ..., 25],
...,
[448, ..., 121]], dtype=int16)}}]}}
paddlex --pipeline table_recognition_v2 \
--use_doc_orientation_classify=False \
--use_doc_unwarping=False \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'table_recognition_v2.jpg', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_layout_detection': True, 'use_ocr_model': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 8, 'label': 'table', 'score': 0.8676343560218811, 'coordinate': [0.017525911, 0.4164088, 1281.396, 585.3947]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[ 9, 21],
...,
[ 9, 59]],
...,
[[1046, 536],
...,
[1046, 573]]], dtype=int16), 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.4, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0, 'rec_texts': ['é¨é¨', 'æĨéäēē', 'æĨéäēįą', 'æšåäēēīŧ', 'åæŽ', 'åŧ ', 'å莥ééĸ', 'å
', 'čŊĻč´šįĨ¨', 'å
ļ', 'įĢčŊĻč´šįĨ¨', 'éŖæēįĨ¨', 'ä¸', 'æ
äŊåŽŋč´š', 'å
ļäģ', 'čĄĨč´´'], 'rec_scores': array([0.9995774 , ..., 0.99309814]), 'rec_polys': array([[[ 9, 21],
...,
[ 9, 59]],
...,
[[1046, 536],
...,
[1046, 573]]], dtype=int16), 'rec_boxes': array([[ 9, ..., 59],
...,
[1046, ..., 573]], dtype=int16)}, 'table_res_list': [{'cell_box_list': [array([1.75259113e-02, ..., 7.36299403e+01]), array([104.53061795, ..., 73.56454113]), array([319.99210477, ..., 73.57040814]), array([424.35038114, ..., 73.77019909]), array([580.89661527, ..., 73.57633618]), array([722.90680814, ..., 73.23406628]), array([983.61304593, ..., 137.50552014]), array([1.75259113e-02, ..., 5.85416409e+02]), array([994., ..., 190.]), array([1245., ..., 186.]), array([211.09584928, ..., 257.55050305]), array([984.40485501, ..., 331.61166027]), array([1038.28327298, ..., 329.74648693]), array([1053., ..., 379.]), array([1038.92304349, ..., 585.03628185]), array([1000., ..., 444.]), array([1.75259113e-02, ..., 5.85416409e+02])], 'pred_html': '<html><body><table><tbody><tr><td>é¨é¨</td><td></td><td>æĨéäēē</td><td></td><td>æĨéäēįą</td><td></td><td colspan="2">æšåäēēīŧ åæŽ åŧ </td></tr><tr><td colspan="6" rowspan="8"></td><td colspan="2"></td></tr><tr><td colspan="2">å莥ééĸ</td></tr><tr><td rowspan="6"></td><td></td></tr><tr><td></td></tr><tr><td>éŖæēįĨ¨</td></tr><tr><td>æ
äŊåŽŋč´š å
ļäģ čĄĨč´´</td></tr><tr><td>ä¸</td></tr><tr><td></td></tr></tbody></table></body></html>', 'table_ocr_pred': {'rec_polys': array([[[ 9, 21],
...,
[ 9, 59]],
...,
[[1046, 536],
...,
[1046, 573]]], dtype=int16), 'rec_texts': ['é¨é¨', 'æĨéäēē', 'æĨéäēįą', 'æšåäēēīŧ', 'åæŽ', 'åŧ ', 'å莥ééĸ', 'å
', 'čŊĻč´šįĨ¨', 'å
ļ', 'įĢčŊĻč´šįĨ¨', 'éŖæēįĨ¨', 'ä¸', 'æ
äŊåŽŋč´š', 'å
ļäģ', 'čĄĨč´´'], 'rec_scores': array([0.9995774 , ..., 0.99309814]), 'rec_boxes': array([[ 9, ..., 59],
...,
[1046, ..., 573]], dtype=int16)}}]}}
paddlex --pipeline layout_parsing \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_parsing_demo.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
What's the result
```bash {'res': {'input_path': 'layout_parsing_demo.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_general_ocr': True, 'use_seal_recognition': True, 'use_table_recognition': True, 'use_formula_recognition': False}, 'parsing_res_list': [{'block_bbox': [133.36868, 40.128025, 1383.7496, 123.51852], 'block_label': 'text', 'block_content': 'åŠååæšäē¤åž\næåģēåč°æĄĨæĸ'}, {'block_bbox': [587.4096, 160.58267, 927.6319, 179.2817], 'block_label': 'figure_title', 'block_content': 'æŦæĨčŽ°č æ˛å°æäģģåŊĻéģåšæ'}, {'block_bbox': [773.8337, 200.6484, 1505.5646, 687.1228], 'block_label': 'image', 'block_content': ''}, {'block_bbox': [390.39642, 201.85085, 741.43414, 292.60092], 'block_label': 'text', 'block_content': 'åįĢįšéäēéĢįæč˛ä¸į įŠļéĸåäŊåģēįĢīŧåŧ\n莞äēä¸åŊč¯č¨č¯žį¨åä¸åŊæåč¯žį¨īŧæŗ¨ååĻ\nį2ä¸äŊäēēæŦĄã10äŊåš´æĨīŧåįšåéĸ厞æä¸ē\nåŊå°æ°äŧäēč§Ŗä¸åŊį䏿įĒåŖã'}, {'block_bbox': [9.714512, 202.68811, 359.62323, 340.3127], 'block_label': 'text', 'block_content': 'čēĢįä¸åŊäŧ įģæ°ææčŖ įåįĢįšéäēé\nåš´äžæŦĄįģå°čĄ¨æŧä¸åŊæ°æčãį°äģŖčãæåč\nįīŧæŧåĻįčå§ŋčĩĸåžį°åēč§äŧéĩéĩæåŖ°ãčŋ\næ¯æĨååįĢįšéäēéĢįæč˛ä¸į įŠļéĸåååĻ\néĸ(äģĨä¸įŽį§°"åįšåéĸ"丞åâåčŋæ°åš´"ä¸åŊ\næčæ¯čĩįå翝ã'}, {'block_bbox': [390.74124, 298.42255, 740.8009, 436.79193], 'block_label': 'text', 'block_content': 'éģ鏪éŖčĄ¨į¤ēīŧéįæĨåĻäš ä¸æįäēēæĨį\nåĸå¤īŧéŋæ¯éŠŦæå¤§åĻæåĻįšåˇ˛éžäģĨæģĄčļŗæåĻ\néčĻã2024åš´4æīŧįąä¸äŧčééåĸæåąå\nåˇčˇ¯æĄĨæŋåģēįåéĸæåĻæĨŧ饚įŽå¨éŋæ¯éŠŦæåŧ\nåˇĨåģē莞īŧéĸ莥äģåš´ä¸ååš´åŗģåˇĨīŧåģēæåå°ä¸ēå\nįšåéĸæäžå ¨æ°įååĻåēå°ã'}, {'block_bbox': [10.579921, 346.26508, 359.13733, 436.17682], 'block_label': 'text', 'block_content': 'ä¸åŊååįĢįšéäēäŧ įģåč°æˇąåãčŋåš´\næĨ,å¨éĢč´¨éå ąåģēâä¸å¸Ļä¸čˇ¯"æĄæļä¸īŧä¸å两\nåŊäēēæä礿ĩä¸ææˇąåīŧäēåŠåäŊįæ°æåēįĄ\næĨ῎ąåã'}, {'block_bbox': [410.51892, 457.0816, 722.768, 516.78217], 'block_label': 'text', 'block_content': 'âå¨ä¸åŊåĻäš įįģå\n莊æįå°æ´åšŋéįä¸įâ'}, {'block_bbox': [30.334734, 457.53757, 341.92758, 516.81995], 'block_label': 'paragraph_title', 'block_content': 'âåĻåĨŊ䏿īŧæäģŦį\næĒæĨ䏿¯æĸĻ"'}, {'block_bbox': [390.89084, 538.1777, 742.1934, 604.6777], 'block_label': 'text', 'block_content': 'å¤åš´æĨīŧåįĢįšéäēåšŋ大čĩ´åįåĻįå\nåščŽäēēå᧝ææčēĢåŊåŽļåģē莞īŧæä¸ēåŠåč¯ĨåŊ\nååąįäēēæååä¸ååĨŊįč§č¯č 忍å¨č ã'}, {'block_bbox': [9.884378, 538.3683, 359.43878, 652.03644], 'block_label': 'text', 'block_content': 'âé˛čąæžå蝿äŊ ææ ˇčĩ°čŋīŧ大å°įĨéäŊ \nåŋä¸įæ¯ä¸ä¸Ēč§čŊâĻâĻ"åįĢįšéäēéŋæ¯éŠŦæ\n大åĻįģŧåæĨŧäēåąīŧä¸éĩäŧįžįæåŖ°å¨čĩ°åģéå\nåãåžĒįįæįæåžčŊģčŊ쿍åŧä¸é´æåޤįé¨īŧ\nåĻįäģŦæŖčˇįčå¸åĻåąä¸æææ˛ãåä¸éĻæãã'}, {'block_bbox': [390.88583, 610.61304, 741.1856, 747.91656], 'block_label': 'text', 'block_content': 'å¨åįĢįšéäēå ¨åŊåĻåĨŗčįåˇĨäŊįįēĻįŋ°\nå¨ÂˇįšéĻå°åžˇÂˇå¯čąåĄå°ąæ¯å ļä¸ä¸äŊãåĨšæžå¨\nä¸ååĨŗååĻéĸæģč¯ģįĄåŖĢåĻäŊīŧį įŠļæšåæ¯åĨŗ\næ§éĸå¯ŧåä¸į¤žäŧååąãå ļé´īŧåĨšåŽå°čĩ°čŽŋä¸åŊ\nå¤ä¸Ēå°åēīŧčˇåžäēč§å¯ä¸åŊį¤žäŧååąįįŦŦä¸\næčĩæã'}, {'block_bbox': [10.115321, 658.7913, 359.40344, 771.3188], 'block_label': 'text', 'block_content': 'čŋæ¯åįšåéĸéŋæ¯éŠŦæå¤§åĻæåĻįšįä¸\nčä¸æææ˛č¯žãä¸ēäē莊åĻįäģŦæ´åĨŊå°įč§Ŗæ\nč¯å¤§æīŧčå¸å°¤æ¯æÂˇįŠįŊéģåžˇč¨å°Âˇäž¯čĩå é\nåįŋģč¯åč§Ŗéæč¯ãéįäŧ´åĨåŖ°åčĩˇīŧåĻįäģŦ\nčžšåąčžšéįčææå¨čēĢäŊīŧį°åēæ°æ°įįã'}, {'block_bbox': [809.6597, 705.4076, 1485.5686, 747.42346], 'block_label': 'figure_title', 'block_content': 'å¨åįĢįšéäēä¸äš å丞åįįŦŦå åąä¸åŊéŖįæåčä¸īŧåŊå°å°åĻįäŊéĒéŖįåļäŊã\nä¸åŊéŠģåįĢįšéäē大äŊŋéĻäžåž'}, {'block_bbox': [389.62894, 753.4464, 742.0593, 890.9599], 'block_label': 'text', 'block_content': 'č°čĩˇå¨ä¸åŊæąåĻįįģåīŧįēĻįŋ°å¨čްåŋįš\næ°īŧâä¸åŊįååąå¨åŊäģä¸įæ¯įŦ䏿 äēįã\næ˛ŋįä¸åŊįšč˛į¤žäŧä¸ģäšé莝ååŽåčĄīŧä¸åŊ\nåé äēååąåĨčŋšīŧčŋä¸åéŊįĻģä¸åŧä¸åŊå ąäē§å \nįéĸå¯ŧãä¸åŊįååąįģéĒåŧåžčޏå¤åŊåŽļåĻäš \nåé´ãâ'}, {'block_bbox': [9.867948, 777.38995, 360.40143, 843.43], 'block_label': 'text', 'block_content': 'âčŋæ¯ä¸æææ˛åįē§įīŧå ąæ32äēēãåĻ\nį大é¨åæĨčĒéĻéŊéŋæ¯éŠŦæįä¸å°åĻīŧåš´éž\næå°įäģ æ6å˛ã"å°¤æ¯æåč¯čްč ã'}, {'block_bbox': [9.780596, 850.09344, 359.62875, 1059.8483], 'block_label': 'text', 'block_content': 'å°¤æ¯æäģåš´23å˛īŧæ¯åįĢįšéäē䏿å ŦįĢ\nåĻæ Ąįč翝čå¸ãåĨš12å˛åŧå§å¨åįšåéĸåĻ\näš ä¸æīŧå¨2017åš´įŦŦååą"æąč¯æĄĨ"ä¸įä¸åĻį\n䏿æ¯čĩä¸čˇåžåįĢįšéäēčĩåēįŦŦä¸åīŧåšļå\nåäŧ´äģŖčĄ¨åįĢįšéäēååžä¸åŊåå åŗčĩīŧčˇåž\nåĸäŊäŧčåĨã2022åš´čĩˇīŧå°¤æ¯æåŧå§å¨åįšå\néĸå ŧčææä¸æææ˛īŧæ¯å¨æĢ两ä¸Ēč¯žæļãâä¸åŊ\næååå¤§į˛žæˇąīŧæå¸ææįåĻįäģŦčŊå¤éčŋä¸\næææ˛æ´åĨŊå°įč§Ŗä¸åŊæåã"åĨšč¯´ã'}, {'block_bbox': [771.98157, 777.02783, 1124.4025, 1059.2194], 'block_label': 'text', 'block_content': 'âä¸įŽĄčŋčŋéŊæ¯åŽĸäēēīŧ蝎ä¸į¨åŽĸæ°īŧį¸įēĻ\nåĨŊäēå¨ä¸čĩˇīŧæäģŦæŦĸčŋäŊ âĻ"å¨ä¸åēä¸åé\nåš´čč°æ´ģå¨ä¸īŧååˇčˇ¯æĄĨ䏿šååˇĨååŊå°å¤§\nåĻįååąãåäēŦæŦĸčŋäŊ ããåįĢįšéäēææ¯åĻ\néĸčŽĄįŽæēį§åĻä¸åˇĨį¨ä¸ä¸åĻįé˛å¤ĢåĄÂˇč°ĸæ\næ¯å ļä¸ä¸åæŧåąč īŧåĨšåžæŠäžŋå¨åéĸåĻäš ä¸\næīŧä¸į´å¨ä¸ēåģä¸åŊįåĻäŊåå¤ãâčŋåĨæč¯\næ¯æäģŦ两åŊäēēæ°åč°įįå¨åį §ãæ čŽēæ¯æ\nčēĢäēåįĢįšéäēåēįĄčŽžæŊåģē莞įä¸äŧååˇĨīŧ\nčŋæ¯å¨ä¸åŊįåĻįåįĢįšéäēåĻåīŧ两åŊäēē\næ°æēæåĒåīŧåŋ å°æ¨å¨ä¸¤åŊå ŗįŗģ䏿ååå\nåąã"é˛å¤ĢåĄč¯´ã'}, {'block_bbox': [1155.9126, 777.7057, 1331.4768, 795.6466], 'block_label': 'text', 'block_content': 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įåįĢįšéäēé', 'åįĢįšéäēéĢįæč˛ä¸į įŠļéĸåäŊåģēįĢīŧåŧ', 'åš´äžæŦĄįģå°čĄ¨æŧä¸åŊæ°æčãį°äģŖčãæåč', '莞äēä¸åŊč¯č¨č¯žį¨åä¸åŊæåč¯žį¨īŧæŗ¨ååĻ', 'įīŧæŧåĻįčå§ŋčĩĸåžį°åēč§äŧéĩéĩæåŖ°ãčŋ', 'į2ä¸äŊäēēæŦĄã10äŊåš´æĨīŧåįšåéĸ厞æä¸ē', 'æ¯æĨååįĢįšéäēéĢįæč˛ä¸į įŠļéĸåååĻ', 'åŊå°æ°äŧäēč§Ŗä¸åŊį䏿įĒåŖã', 'éĸ(äģĨä¸įŽį§°"åįšåéĸ"丞åâåčŋæ°åš´"ä¸åŊ', 'éģ鏪éŖčĄ¨į¤ēīŧéįæĨåĻäš ä¸æįäēēæĨį', 'æčæ¯čĩįå翝ã', 'åĸå¤īŧéŋæ¯éŠŦæå¤§åĻæåĻįšåˇ˛éžäģĨæģĄčļŗæåĻ', 'ä¸åŊååįĢįšéäēäŧ įģåč°æˇąåãčŋåš´', 'éčĻã2024åš´4æīŧįąä¸äŧčééåĸæåąå', 'æĨ,å¨éĢč´¨éå
ąåģēâä¸å¸Ļä¸čˇ¯"æĄæļä¸īŧä¸å两', 'åˇčˇ¯æĄĨæŋåģēįåéĸæåĻæĨŧ饚įŽå¨éŋæ¯éŠŦæåŧ', 'åŊäēēæä礿ĩä¸ææˇąåīŧäēåŠåäŊįæ°æåēįĄ', 'åˇĨåģē莞īŧéĸ莥äģåš´ä¸ååš´åŗģåˇĨīŧåģēæåå°ä¸ēå', 'æĨ῎ąåã', 'įšåéĸæäžå
¨æ°įååĻåēå°ã', 'âåĻåĨŊ䏿īŧæäģŦį', 'âå¨ä¸åŊåĻäš įįģå', 'æĒæĨ䏿¯æĸĻ"', '莊æįå°æ´åšŋéįä¸įâ', 'âé˛čąæžå蝿äŊ ææ ˇčĩ°čŋīŧ大å°įĨéäŊ ', 'å¤åš´æĨīŧåįĢįšéäēåšŋ大čĩ´åįåĻįå', 'åŋä¸įæ¯ä¸ä¸Ēč§čŊâĻâĻ"åįĢįšéäēéŋæ¯éŠŦæ', 'åščŽäēēå᧝ææčēĢåŊåŽļåģē莞īŧæä¸ēåŠåč¯ĨåŊ', '大åĻįģŧåæĨŧäēåąīŧä¸éĩäŧįžįæåŖ°å¨čĩ°åģéå', 'ååąįäēēæååä¸ååĨŊįč§č¯č
忍å¨č
ã', 'åãåžĒįįæįæåžčŊģčŊ쿍åŧä¸é´æåޤįé¨īŧ', 'å¨åįĢįšéäēå
¨åŊåĻåĨŗčįåˇĨäŊįįēĻįŋ°', 'åĻįäģŦæŖčˇįčå¸åĻåąä¸æææ˛ãåä¸éĻæãã', 'å¨ÂˇįšéĻå°åžˇÂˇå¯čąåĄå°ąæ¯å
ļä¸ä¸äŊãåĨšæžå¨', 'čŋæ¯åįšåéĸéŋæ¯éŠŦæå¤§åĻæåĻįšįä¸', 'ä¸ååĨŗååĻéĸæģč¯ģįĄåŖĢåĻäŊīŧį įŠļæšåæ¯åĨŗ', 'čä¸æææ˛č¯žãä¸ēäē莊åĻįäģŦæ´åĨŊå°įč§Ŗæ', 'æ§éĸå¯ŧåä¸į¤žäŧååąãå
ļé´īŧåĨšåŽå°čĩ°čŽŋä¸åŊ', 'č¯å¤§æīŧčå¸å°¤æ¯æÂˇįŠįŊéģåžˇč¨å°Âˇäž¯čĩå é', 'å¤ä¸Ēå°åēīŧčˇåžäēč§å¯ä¸åŊį¤žäŧååąįįŦŦä¸', 'å¨åįĢįšéäēä¸äš
å丞åįįŦŦå
åąä¸åŊéŖįæåčä¸īŧåŊå°å°åĻįäŊéĒéŖįåļäŊã', 'åįŋģč¯åč§Ŗéæč¯ãéįäŧ´åĨåŖ°åčĩˇīŧåĻįäģŦ', 'æčĩæã', 'ä¸åŊéŠģåįĢįšéäē大äŊŋéĻäžåž', 'čžšåąčžšéįčææå¨čēĢäŊīŧį°åēæ°æ°įįã', 'č°čĩˇå¨ä¸åŊæąåĻįįģåīŧįēĻįŋ°å¨čްåŋįš', 'âčŋæ¯ä¸æææ˛åįē§įīŧå
ąæ32äēēãåĻ', 'æ°īŧâä¸åŊįååąå¨åŊäģä¸įæ¯įŦ䏿 äēįã', 'âä¸įŽĄčŋčŋéŊæ¯åŽĸäēēīŧ蝎ä¸į¨åŽĸæ°īŧį¸įēĻ', 'įĻįåįēĸæĩˇįåįŠéĻã', 'į大é¨åæĨčĒéĻéŊéŋæ¯éŠŦæįä¸å°åĻīŧåš´éž', 'æ˛ŋįä¸åŊįšč˛į¤žäŧä¸ģäšé莝ååŽåčĄīŧä¸åŊ', 'åĨŊäēå¨ä¸čĩˇīŧæäģŦæŦĸčŋäŊ âĻâĻâĻ"å¨ä¸åēä¸åé', 'åįŠéĻäēåąéåįä¸ä¸ĒåæčĒéŋæåŠ', 'æå°įäģ
æ6å˛ã"å°¤æ¯æåč¯čްč
ã', 'åé äēååąåĨčŋšīŧčŋä¸åéŊįĻģä¸åŧä¸åŊå
ąäē§å
', 'åš´čč°æ´ģå¨ä¸īŧååˇčˇ¯æĄĨ䏿šååˇĨååŊå°å¤§', 'æ¯å¤åįä¸åŊå¤äģŖéļåļé
å¨īŧįŊčēĢä¸åį', 'å°¤æ¯æäģåš´23å˛īŧæ¯åįĢįšéäē䏿å
ŦįĢ', 'įéĸå¯ŧãä¸åŊįååąįģéĒåŧåžčޏå¤åŊåŽļåĻäš ', 'åĻįååąãåäēŦæŦĸčŋäŊ ããåįĢįšéäēææ¯åĻ', 'âä¸ââåââįĻ
ââåąą"įæąåãâčŋäģļæįŠč¯', 'åĻæ Ąįč翝čå¸ãåĨš12å˛åŧå§å¨åįšåéĸåĻ', 'åé´ãâ', 'éĸčŽĄįŽæēį§åĻä¸åˇĨį¨ä¸ä¸åĻįé˛å¤ĢåĄÂˇč°ĸæ', 'æīŧåžæŠäģĨåæäģŦå°ąéčŋæĩˇä¸ä¸įģ¸äščˇ¯čŋčĄ', 'äš ä¸æīŧå¨2017åš´įŦŦååą"æąč¯æĄĨ"ä¸įä¸åĻį', 'æŖå¨čĨŋå大åĻåĻäš įåįĢįšéäēååŖĢį', 'æ¯å
ļä¸ä¸åæŧåąč
īŧåĨšåžæŠäžŋå¨åéĸåĻäš ä¸', 'č´¸æåžæĨ䏿åä礿ĩãčŋäšæ¯åįĢįšéäē', '䏿æ¯čĩä¸čˇåžåįĢįšéäēčĩåēįŦŦä¸åīŧåšļå', 'įŠåĸįåĄÂˇæŗŊįŠäŧ寚ä¸åŊæææˇąåææ
ã8', 'æīŧä¸į´å¨ä¸ēåģä¸åŊįåĻäŊåå¤ãâčŋåĨæč¯', 'ä¸ä¸åŊååĨŊäē¤åžåå˛įæå蝿ã"åįēĸæĩˇ', 'åäŧ´äģŖčĄ¨åįĢįšéäēååžä¸åŊåå åŗčĩīŧčˇåž', 'æ¯æäģŦ两åŊäēēæ°åč°įįå¨åį
§ãæ čŽēæ¯æ', 'įåįŠéĻį įŠļ䏿įŽé¨č´č´Ŗäēēäŧč¨äēæ¯Âˇįš', 'åĸäŊäŧčåĨã2022åš´čĩˇīŧå°¤æ¯æåŧå§å¨åįšå', 'įåĄå¨į¤žäē¤åĒäŊä¸åä¸čŋæ ˇä¸æŽĩč¯īŧâčŋæ¯æ', 'čēĢäēåįĢįšéäēåēįĄčŽžæŊåģē莞įä¸äŧååˇĨīŧ', 'æ¯æŗå
šå蝴ã', 'éĸå
ŧčææä¸æææ˛īŧæ¯å¨æĢ两ä¸Ēč¯žæļãâä¸åŊ', 'äēēįįéčĻ䏿ĨīŧčĒæ¤ææĨæäēä¸åååēį', 'čŋæ¯å¨ä¸åŊįåĻįåįĢįšéäēåĻåīŧ两åŊäēē', 'åįĢįšéäēåŊåŽļåįŠéĻčå¤åĻåäēēįąģåĻ', 'æååå¤§į˛žæˇąīŧæå¸ææįåĻįäģŦčŊå¤éčŋä¸', 'éåīŧčĩäēæįŠŋčļčæŖįåéã"', 'æ°æēæåĒåīŧåŋ
å°æ¨å¨ä¸¤åŊå
ŗįŗģ䏿ååå', 'į įŠļåč˛å°č¡įšéĻå°åžˇåååįąä¸åŊæ', 'æææ˛æ´åĨŊå°įč§Ŗä¸åŊæåã"åĨšč¯´ã', 'įŠåĸįåĄå¯åå
ŗæŗ¨ä¸åŊå¨įģæĩãį§æãæ', 'åąã"é˛å¤ĢåĄč¯´ã', 'åãäģ襨į¤ēīŧâåĻäš åŊŧæ¤įč¯č¨åæåīŧå°å¸Ž', 'âå§å§īŧäŊ æŗåģä¸åŊåīŧ"âé常æŗīŧææŗ', 'č˛įéĸåįååąīŧâä¸åŊå¨į§į įæšéĸįåŽå', 'åįĢįšéäēéĢįæč˛å§åäŧä¸ģäģģåŠįč¨', 'åŠåä¸ä¸¤åŊäēēæ°æ´åĨŊå°įč§ŖåŊŧæ¤īŧåŠååæš', 'åģįæ
åŽĢãįŦéŋåã"å°¤æ¯æįåĻį䏿ä¸å¯š', '䏿Ĩäŋąåĸãå¨ä¸åŊåĻäš įįģå莊æįå°æ´åšŋ', 'éŠŦį襨į¤ēīŧâæ¯åš´æäģŦéŊäŧįģįģåĻįå°ä¸åŊčŽŋ', 'äē¤åžīŧæåģēåč°æĄĨæĸã"', 'čŊæåčįå§åĻšīŧå§å§é˛å¨
äģåš´15å˛īŧåĻšåĻš', 'éįä¸įīŧäģä¸åįåĒæĩ
ãâ', 'éŽåĻäš īŧįŽåæčļ
čŋ5000ååįĢįšéäēåĻį', 'åįĢįšéäēåŊåŽļåįŠéĻéĻéŋåĄåä¸ÂˇåĒ', 'čå¨
14å˛īŧ两äēēéŊ厞å¨åįšåéĸåĻäš å¤åš´īŧ', '23å˛įččŋĒäēÂˇåæ¯čæŗč¯ēæ¯åˇ˛å¨åįš', 'å¨ä¸åŊįåĻãåĻäš ä¸åŊįæč˛įģéĒīŧæåŠäē', 'éčžžå§Âˇäŧį´ įĻæžå¤æŦĄčŽŋéŽä¸åŊīŧ寚ä¸åææ', 'ä¸æč¯´åžæ ŧ夿ĩåŠã', 'åéĸåĻäš 3åš´īŧå¨ä¸åŊäšĻæŗãä¸åŊįģįæšéĸ襨', 'æååįĢįšéäēįæč˛æ°´åšŗã"', 'įäŧ æŋä¸åæ°ãį°äģŖååįŠéĻįåģē莞ä¸ååą', 'é˛å¨
å¯ščŽ°č
蝴īŧâčŋäēåš´æĨīŧæįå¯šä¸æ', 'į°ååäŧį§īŧå¨2024åš´åįĢįšéäēčĩåēį', 'å°čąĄæˇąåģãâä¸åŊåįŠéĻä¸äģ
æčޏå¤äŋååŽåĨŊ', 'âå
ąååä¸įåąį¤ēé', 'åä¸åŊæåįįįąīŧæäģŦå§åĻšäŋŠå§įģį¸äēéŧ', 'âæąč¯æĄĨ"æ¯čĩä¸čˇåžä¸įåĨãččŋĒäē蝴īŧâåĻ', 'įæįŠīŧčŋå
åčŋį¨å
čŋį§æææŽĩčŋčĄåąį¤ēīŧ', 'åąīŧä¸čĩˇåĻäš ãæäģŦį䏿ä¸å¤Šæ¯ä¸å¤ŠåĨŊīŧčŋ', 'äš ä¸åŊäšĻæŗčŽŠæįå
åŋååžåŽåŽåįē¯į˛šãæ', 'æ´˛åäēæ´˛įįŋįææâ', '帎åŠäēēäģŦæ´åĨŊįč§Ŗä¸åææã"åĄåä¸č¯´īŧâå', 'åĻäŧäē䏿æåä¸åŊčãæäģŦä¸åŽčĻå°ä¸åŊ', 'äšåæŦĸä¸åŊįæéĨ°īŧ叿æĒæĨčŊåģä¸åŊåĻäš īŧ', 'įĢįšéäēä¸ä¸åŊéŊæĨææ äš
įææīŧå§įģį¸', 'åģãåĻåĨŊ䏿īŧæäģŦįæĒæĨ䏿¯æĸĻīŧâ', 'æä¸åŊä¸åæ°æå
į´ čå
ĨæčŖ
莞莥ä¸īŧåäŊ', 'äģéŋæ¯éŠŦæåēåīŧæ˛ŋįčŋ连æįįåąą', 'äēįč§Ŗãį¸äēå°éãæå¸ææĒæĨä¸ä¸åŊåčĄ', 'æŽåįšåéĸ䏿šéĸéŋéģ鏪éŖäģįģīŧčŋæ', 'åēæ´å¤į˛žįžäŊåīŧäšæåįšæååäēĢįģæ´å¤', 'å
Ŧ莝ä¸čˇ¯åä¸å¯ģæžä¸čˇ¯å°čŋšã銹čŊĻ两ä¸Ēå°', 'å åŧēåäŊīŧå
ąååä¸įåąį¤ēéæ´˛åäēæ´˛įįŋ', 'åéĸæįĢäē2013åš´3æīŧįąč´ĩåˇč´ĸįģ大åĻå', 'įä¸åŊæåãâ', 'æļīŧ莰č
æĨå°äŊäēåįĢįšéä翏¯åŖåå¸éŠŦč¨', 'įææãâ'], 'rec_scores': array([0.99982357, ..., 0.93637466]), 'rec_polys': array([[[ 122, 28],
...,
[ 122, 135]],
...,
[[1156, 1330],
...,
[1156, 1351]]], dtype=int16), 'rec_boxes': array([[ 122, ..., 135],
...,
[1156, ..., 1351]], dtype=int16)}}}
```
paddlex --pipeline PP-StructureV3 \
--input pp_structure_v3_demo.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'pp_structure_v3_demo.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_general_ocr': True, 'use_seal_recognition': True, 'use_table_recognition': True, 'use_formula_recognition': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 2, 'label': 'text', 'score': 0.9853514432907104, 'coordinate': [770.9531, 776.6814, 1122.6057, 1058.7322]}, {'cls_id': 1, 'label': 'image', 'score': 0.9848673939704895, 'coordinate': [775.7434, 202.27979, 1502.8113, 686.02136]}, {'cls_id': 2, 'label': 'text', 'score': 0.983731746673584, 'coordinate': [1152.3197, 1113.3275, 1503.3029, 1346.586]}, {'cls_id': 2, 'label': 'text', 'score': 0.9832221865653992, 'coordinate': [1152.5602, 801.431, 1503.8436, 986.3563]}, {'cls_id': 2, 'label': 'text', 'score': 0.9829439520835876, 'coordinate': [9.549545, 849.5713, 359.1173, 1058.7488]}, {'cls_id': 2, 'label': 'text', 'score': 0.9811657667160034, 'coordinate': [389.58298, 1137.2659, 740.66235, 1346.7488]}, {'cls_id': 2, 'label': 'text', 'score': 0.9775941371917725, 'coordinate': [9.1302185, 201.85, 359.0409, 339.05692]}, {'cls_id': 2, 'label': 'text', 'score': 0.9750366806983948, 'coordinate': [389.71454, 752.96924, 740.544, 889.92456]}, {'cls_id': 2, 'label': 'text', 'score': 0.9738152027130127, 'coordinate': [389.94565, 298.55988, 740.5585, 435.5124]}, {'cls_id': 2, 'label': 'text', 'score': 0.9737328290939331, 'coordinate': [771.50256, 1065.4697, 1122.2582, 1178.7324]}, {'cls_id': 2, 'label': 'text', 'score': 0.9728517532348633, 'coordinate': [1152.5154, 993.3312, 1503.2349, 1106.327]}, {'cls_id': 2, 'label': 'text', 'score': 0.9725610017776489, 'coordinate': [9.372787, 1185.823, 359.31738, 1298.7227]}, {'cls_id': 2, 'label': 'text', 'score': 0.9724331498146057, 'coordinate': [389.62848, 610.7389, 740.83234, 746.2377]}, {'cls_id': 2, 'label': 'text', 'score': 0.9720287322998047, 'coordinate': [389.29898, 897.0936, 741.41516, 1034.6616]}, {'cls_id': 2, 'label': 'text', 'score': 0.9713053703308105, 'coordinate': [10.323685, 1065.4663, 359.6786, 1178.8872]}, {'cls_id': 2, 'label': 'text', 'score': 0.9689728021621704, 'coordinate': [9.336395, 537.6609, 359.2901, 652.1881]}, {'cls_id': 2, 'label': 'text', 'score': 0.9684857130050659, 'coordinate': [10.7608185, 345.95068, 358.93616, 434.64087]}, {'cls_id': 2, 'label': 'text', 'score': 0.9681928753852844, 'coordinate': [9.674866, 658.89075, 359.56528, 770.4319]}, {'cls_id': 2, 'label': 'text', 'score': 0.9634978175163269, 'coordinate': [770.9464, 1281.1785, 1122.6522, 1346.7156]}, {'cls_id': 2, 'label': 'text', 'score': 0.96304851770401, 'coordinate': [390.0113, 201.28055, 740.1684, 291.53073]}, {'cls_id': 2, 'label': 'text', 'score': 0.962053120136261, 'coordinate': [391.21393, 1040.952, 740.5046, 1130.32]}, {'cls_id': 2, 'label': 'text', 'score': 0.9565253853797913, 'coordinate': [10.113251, 777.1482, 359.439, 842.437]}, {'cls_id': 2, 'label': 'text', 'score': 0.9497362375259399, 'coordinate': [390.31357, 537.86285, 740.47595, 603.9285]}, {'cls_id': 2, 'label': 'text', 'score': 0.9371236562728882, 'coordinate': [10.2034, 1305.9753, 359.5958, 1346.7295]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.9338151216506958, 'coordinate': [791.6062, 1200.8479, 1103.3257, 1259.9324]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.9326773285865784, 'coordinate': [408.0737, 457.37024, 718.9509, 516.63464]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.9274250864982605, 'coordinate': [29.448685, 456.6762, 340.99194, 515.6999]}, {'cls_id': 2, 'label': 'text', 'score': 0.8742568492889404, 'coordinate': [1154.7095, 777.3624, 1330.3086, 794.5853]}, {'cls_id': 2, 'label': 'text', 'score': 0.8442489504814148, 'coordinate': [586.49316, 160.15454, 927.468, 179.64203]}, {'cls_id': 11, 'label': 'doc_title', 'score': 0.8332607746124268, 'coordinate': [133.80017, 37.41908, 1380.8601, 124.1429]}, {'cls_id': 6, 'label': 'figure_title', 'score': 0.6770150661468506, 'coordinate': [812.1718, 705.1199, 1484.6973, 747.1692]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[133, 35],
...,
[133, 131]],
...,
[[ 13, 754],
...,
[ 13, 777]]], dtype=int16), 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0.0, 'rec_texts': ['åŠååæšäē¤åž', 'æåģēåč°æĄĨæĸ', 'æŦæĨ莰č
', 'æ˛å°æ', 'äģģ', 'åŊĻ', 'éģåšæ', 'čēĢįä¸åŊäŧ įģæ°ææčŖ
įåįĢįšéäēé', 'åįĢįšéäēéĢįæč˛ä¸į įŠļéĸåäŊåģēįĢīŧåŧ', 'åš´äžæŦĄįģå°čĄ¨æŧä¸åŊæ°æčãį°äģŖčãæåč', '莞äēä¸åŊč¯č¨č¯žį¨åä¸åŊæåč¯žį¨īŧæŗ¨ååĻ', 'į,æŧåĻįčå§ŋčĩĸåžį°åēč§äŧéĩéĩæåŖ°ãčŋ', 'į2ä¸äŊäēēæŦĄã10äŊåš´æĨīŧåįšåéĸ厞æä¸ē', 'æ¯æĨååįĢįšéäēéĢįæč˛ä¸į įŠļéĸåååĻ', 'åŊå°æ°äŧäēč§Ŗä¸åŊį䏿įĒåŖã', 'éĸ(äģĨä¸įŽį§°"åįšåéĸ")丞å"åčŋæ°åš´"ä¸åŊ', 'éģ鏪éŖčĄ¨į¤ē,éįæĨåĻäš ä¸æįäēēæĨį', 'æčæ¯čĩįå翝ã', 'åĸå¤īŧéŋæ¯éŠŦæå¤§åĻæåĻįšåˇ˛éžäģĨæģĄčļŗæåĻ', 'ä¸åŊååįĢįšéäēäŧ įģåč°æˇąåãčŋåš´', 'éčĻã2024åš´4æīŧįąä¸äŧčééåĸæåąå', 'æĨ,å¨éĢč´¨éå
ąåģē"ä¸å¸Ļä¸čˇ¯"æĄæļä¸īŧä¸å两', 'åˇčˇ¯æĄĨæŋåģēįåéĸæåĻæĨŧ饚įŽå¨éŋæ¯éŠŦæåŧ', 'åŊäēēæä礿ĩä¸ææˇąåīŧäēåŠåäŊįæ°æåēįĄ', 'åˇĨåģē莞,éĸ莥äģåš´ä¸ååš´įĢŖåˇĨ,åģēæåå°ä¸ēå', 'æĨ῎ąåã', 'įšåéĸæäžå
¨æ°įååĻåēå°ã', 'âåĻåĨŊ䏿īŧæäģŦį', 'âå¨ä¸åŊåĻäš įįģå', 'æĒæĨ䏿¯æĸĻâ', '莊æįå°æ´åšŋéįä¸įâ', 'å¤åš´æĨ,åįĢįšéäēåšŋ大čĩ´åįåĻįå', 'åščŽäēēå᧝ææčēĢåŊåŽļåģē莞,æä¸ēåŠåč¯ĨåŊ', 'ååąįäēēæååä¸ååĨŊįč§č¯č
忍å¨č
ã', 'å¨åįĢįšéäēå
¨åŊåĻåĨŗčįåˇĨäŊįįēĻįŋ°', 'å¨ÂˇįšéĻå°åžˇÂˇå¯čąåĄå°ąæ¯å
ļä¸ä¸äŊãåĨšæžå¨', 'ä¸ååĨŗååĻéĸæģč¯ģįĄåŖĢåĻäŊ,į įŠļæšåæ¯åĨŗ', 'æ§éĸå¯ŧåä¸į¤žäŧååąãå
ļé´īŧåĨšåŽå°čĩ°čŽŋä¸åŊ', 'å¤ä¸Ēå°åēīŧčˇåžäēč§å¯ä¸åŊį¤žäŧååąįįŦŦä¸', 'å¨åįĢįšéäēä¸äš
å丞åįįŦŦå
åąä¸åŊéŖįæåčä¸īŧåŊå°å°åĻįäŊéĒéŖįåļäŊã', 'æčĩæã', 'ä¸åŊéŠģåįĢįšéäē大äŊŋéĻäžåž', 'âčŋæ¯ä¸æææ˛åįē§įīŧå
ąæ32äēēãåĻ', 'âä¸įŽĄčŋčŋéŊæ¯åŽĸäēēīŧ蝎ä¸į¨åŽĸæ°;į¸įēĻ', 'įĻįåįēĸæĩˇįåįŠéĻã', 'į大é¨åæĨčĒéĻéŊéŋæ¯éŠŦæįä¸å°åĻīŧåš´éž', 'åĨŊäēå¨ä¸čĩˇ,æäģŦæŦĸčŋäŊ "å¨ä¸åēä¸åé', 'åįŠéĻäēåąéåįä¸ä¸ĒåæčĒéŋæåŠ', 'æå°įäģ
æ6å˛ã"å°¤æ¯æåč¯čްč
ã', 'åš´čč°æ´ģå¨ä¸,ååˇčˇ¯æĄĨ䏿šååˇĨååŊå°å¤§', 'æ¯å¤åįä¸åŊå¤äģŖéļåļé
å¨,įŊčēĢä¸åį', 'å°¤æ¯æäģåš´23å˛īŧæ¯åįĢįšéäē䏿å
ŦįĢ', 'åĻįååąãåäēŦæŦĸčŋäŊ ããåįĢįšéäēææ¯åĻ', 'âä¸ââå""įĻ
ââåąą"įæąåãâčŋäģļæįŠč¯', 'åĻæ Ąįč翝čå¸ãåĨš12å˛åŧå§å¨åįšåéĸåĻ', 'éĸčŽĄįŽæēį§åĻä¸åˇĨį¨ä¸ä¸åĻįé˛å¤ĢåĄÂˇč°ĸæ', 'æ,åžæŠäģĨåæäģŦå°ąéčŋæĩˇä¸ä¸įģ¸äščˇ¯čŋčĄ', 'äš ä¸æ,å¨2017åš´įŦŦååą"æąč¯æĄĨ"ä¸įä¸åĻį', 'æ¯å
ļä¸ä¸åæŧåąč
,åĨšåžæŠäžŋå¨åéĸåĻäš ä¸', 'č´¸æåžæĨ䏿åä礿ĩãčŋäšæ¯åįĢįšéäē', '䏿æ¯čĩä¸čˇåžåįĢįšéäēčĩåēįŦŦä¸å,åšļå', 'æīŧä¸į´å¨ä¸ēåģä¸åŊįåĻäŊåå¤ãâčŋåĨæč¯', 'ä¸ä¸åŊååĨŊäē¤åžåå˛įæå蝿ã"åįēĸæĩˇ', 'åäŧ´äģŖčĄ¨åįĢįšéäēååžä¸åŊåå åŗčĩ,čˇåž', 'æ¯æäģŦ两åŊäēēæ°åč°įįå¨åį
§ãæ čŽēæ¯æ', 'įåįŠéĻį įŠļ䏿įŽé¨č´č´Ŗäēēäŧč¨äēæ¯Âˇįš', 'åĸäŊäŧčåĨã2022åš´čĩˇīŧå°¤æ¯æåŧå§å¨åįšå', 'čēĢäēåįĢįšéäēåēįĄčŽžæŊåģē莞įä¸äŧååˇĨīŧ', 'æ¯æŗå
šå蝴ã', 'éĸå
ŧčææä¸æææ˛,æ¯å¨æĢ两ä¸Ēč¯žæļãä¸åŊ', 'čŋæ¯å¨ä¸åŊįåĻįåįĢįšéäēåĻå,两åŊäēē', 'åįĢįšéäēåŊåŽļåįŠéĻčå¤åĻåäēēįąģåĻ', 'æååå¤§į˛žæˇą,æå¸ææįåĻįäģŦčŊå¤éčŋä¸', 'æ°æēæåĒå,åŋ
å°æ¨å¨ä¸¤åŊå
ŗįŗģ䏿ååå', 'į įŠļåč˛å°č¡įšéĻå°åžˇåååįąä¸åŊæ', 'æææ˛æ´åĨŊå°įč§Ŗä¸åŊæåã"åĨšč¯´ã', 'įŠåĸįåĄå¯åå
ŗæŗ¨ä¸åŊå¨įģæĩãį§æãæ', 'åąã"é˛å¤ĢåĄč¯´ã', 'åãäģ襨į¤ēīŧâåĻäš åŊŧæ¤įč¯č¨åæåīŧå°å¸Ž', 'âå§å§,äŊ æŗåģä¸åŊå?"âé常æŗīŧææŗ', 'č˛įéĸåįååąīŧâä¸åŊå¨į§į įæšéĸįåŽå', 'åįĢįšéäēéĢįæč˛å§åäŧä¸ģäģģåŠįč¨', 'åŠåä¸ä¸¤åŊäēēæ°æ´åĨŊå°įč§ŖåŊŧæ¤īŧåŠååæš', 'åģįæ
åŽĢãįŦéŋåã"å°¤æ¯æįåĻį䏿ä¸å¯š', '䏿Ĩäŋąåĸãå¨ä¸åŊåĻäš įįģå莊æįå°æ´åšŋ', 'éŠŦį襨į¤ēīŧâæ¯åš´æäģŦéŊäŧįģįģåĻįå°ä¸åŊčŽŋ', 'äē¤åž,æåģēåč°æĄĨæĸã"', 'čŊæåčįå§åĻš,å§å§é˛å¨
äģåš´15å˛īŧåĻšåĻš', 'éįä¸įīŧäģä¸åįåĒæĩ
ã', 'éŽåĻäš īŧįŽåæčļ
čŋ5000ååįĢįšéäēåĻį', 'åįĢįšéäēåŊåŽļåįŠéĻéĻéŋåĄåä¸ÂˇåĒ', 'čå¨
14å˛īŧ两äēēéŊ厞å¨åįšåéĸåĻäš å¤åš´īŧ', '23å˛įččŋĒäēÂˇåæ¯čæŗč¯ēæ¯åˇ˛å¨åįš', 'å¨ä¸åŊįåĻãåĻäš ä¸åŊįæč˛įģéĒ,æåŠäē', 'éčžžå§Âˇäŧį´ įĻæžå¤æŦĄčŽŋéŽä¸åŊīŧ寚ä¸åææ', 'ä¸æč¯´åžæ ŧ夿ĩåŠã', 'åéĸåĻäš 3åš´īŧå¨ä¸åŊäšĻæŗãä¸åŊįģįæšéĸ襨', 'æååįĢįšéäēįæč˛æ°´åšŗãâ', 'įäŧ æŋä¸åæ°ãį°äģŖååįŠéĻįåģē莞ä¸ååą', 'é˛å¨
å¯ščŽ°č
蝴īŧâčŋäēåš´æĨ,æįå¯šä¸æ', 'į°ååäŧį§īŧå¨2024åš´åįĢįšéäēčĩåēį', 'âå
ąååä¸įåąį¤ēé', 'å°čąĄæˇąåģãâä¸åŊåįŠéĻä¸äģ
æčޏå¤äŋååŽåĨŊ', 'åä¸åŊæåįįįą,æäģŦå§åĻšäŋŠå§įģį¸äēéŧ', 'âæąč¯æĄĨ"æ¯čĩä¸čˇåžä¸įåĨãččŋĒäē蝴īŧâåĻ', 'įæįŠ,čŋå
åčŋį¨å
čŋį§æææŽĩčŋčĄåąį¤ēīŧ', 'åą,ä¸čĩˇåĻäš ãæäģŦį䏿ä¸å¤Šæ¯ä¸å¤ŠåĨŊ,čŋ', 'äš ä¸åŊäšĻæŗčŽŠæįå
åŋååžåŽåŽåįē¯į˛šãæ', 'æ´˛åäēæ´˛įįŋįææâ', '帎åŠäēēäģŦæ´åĨŊįč§Ŗä¸åææã"åĄåä¸č¯´īŧå', 'åĻäŧäē䏿æåä¸åŊčãæäģŦä¸åŽčĻå°ä¸åŊ', 'äšåæŦĸä¸åŊįæéĨ°,叿æĒæĨčŊåģä¸åŊåĻäš īŧ', 'įĢįšéäēä¸ä¸åŊéŊæĨææ äš
įææ,å§įģį¸', 'åģãåĻåĨŊ䏿,æäģŦįæĒæĨ䏿¯æĸĻ!"', 'æä¸åŊä¸åæ°æå
į´ čå
ĨæčŖ
莞莥ä¸īŧåäŊ', 'äģéŋæ¯éŠŦæåēå,æ˛ŋįčŋ连æįįåąą', 'äēįč§Ŗãį¸äēå°éãæå¸ææĒæĨä¸ä¸åŊåčĄ', 'æŽåįšåéĸ䏿šéĸéŋéģ鏪éŖäģįģ,čŋæ', 'åēæ´å¤į˛žįžäŊåīŧäšæåįšæååäēĢįģæ´å¤', 'å
Ŧ莝ä¸čˇ¯åä¸å¯ģæžä¸čˇ¯å°čŋšã銹čŊĻ两ä¸Ēå°', 'å åŧēåäŊ,å
ąååä¸įåąį¤ēéæ´˛åäēæ´˛įįŋ', 'åéĸæįĢäē2013åš´3æīŧįąč´ĩåˇč´ĸįģ大åĻå', 'įä¸åŊæåãâ', 'æļ,莰č
æĨå°äŊäēåįĢįšéä翏¯åŖåå¸éŠŦč¨', 'įææãâ', 'č°čĩˇå¨ä¸åŊæąåĻįįģå,įēĻįŋ°å¨čްåŋįš', 'æ°īŧâä¸åŊįååąå¨åŊäģä¸įæ¯įŦ䏿 äēįã', 'æ˛ŋįä¸åŊįšč˛į¤žäŧä¸ģäšé莝ååŽåčĄīŧä¸åŊ', 'åé äēååąåĨčŋš,čŋä¸åéŊįĻģä¸åŧä¸åŊå
ąäē§å
', 'įéĸå¯ŧãä¸åŊįååąįģéĒåŧåžčޏå¤åŊåŽļåĻäš ', 'åé´īŧâ', 'æŖå¨čĨŋå大åĻåĻäš įåįĢįšéäēååŖĢį', 'įŠåĸįåĄÂˇæŗŊįŠäŧ寚ä¸åŊæææˇąåææ
ã8', 'åš´åīŧå¨åäēŦå¸č大åĻčˇåžįĄåŖĢåĻäŊåīŧįŠåĸ', 'įåĄå¨į¤žäē¤åĒäŊä¸åä¸čŋæ ˇä¸æŽĩč¯īŧâčŋæ¯æ', 'äēēįįéčĻ䏿ĨīŧčĒæ¤ææĨæäēä¸åååēį', 'éå.čĩäēæįŠŋčļčæŖįåéãâ', 'âé˛čąæžå蝿äŊ ææ ˇčĩ°čŋīŧ大å°įĨéäŊ ', 'åŋä¸įæ¯ä¸ä¸Ēč§čŊ"åįĢįšéäēéŋæ¯éŠŦæ', '大åĻįģŧåæĨŧäēåąīŧä¸éĩäŧįžįæåŖ°å¨čĩ°åģéå', 'åãåžĒįįæįæåžčŊģčŊ쿍åŧä¸é´æåޤįé¨īŧ', 'åĻįäģŦæŖčˇįčå¸åĻåąä¸æææ˛ãåä¸éĻæãã', 'čŋæ¯åįšåéĸéŋæ¯éŠŦæå¤§åĻæåĻįšįä¸', 'čä¸æææ˛č¯žãä¸ēäē莊åĻįäģŦæ´åĨŊå°įč§Ŗæ', 'č¯å¤§æīŧčå¸å°¤æ¯æÂˇįŠįŊéģåžˇč¨å°Âˇäž¯čĩå é', 'åįŋģč¯åč§Ŗéæč¯ãéįäŧ´åĨåŖ°åčĩˇīŧåĻįäģŦ', 'čžšåąčžšéįčææå¨čēĢäŊīŧį°åēæ°æ°įįã'], 'rec_scores': array([0.99972075, ..., 0.96241361]), 'rec_polys': array([[[133, 35],
...,
[133, 131]],
...,
[[ 13, 754],
...,
[ 13, 777]]], dtype=int16), 'rec_boxes': array([[133, ..., 131],
...,
[ 13, ..., 777]], dtype=int16)}}}
paddlex --pipeline formula_recognition \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/pipelines/general_formula_recognition_001.png \
--use_layout_detection True \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--layout_threshold 0.5 \
--layout_nms True \
--layout_unclip_ratio 1.0 \
--layout_merge_bboxes_mode "'large'"\
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'general_formula_recognition.png', 'model_settings': {'use_doc_preprocessor': False,'use_layout_detection': True}, 'layout_det_res': {'input_path': None, 'boxes': [{'cls_id': 2, 'label': 'text', 'score': 0.9778407216072083, 'coordinate': [271.257, 648.50824, 1040.2291, 774.8482]}, ...]}, 'formula_res_list': [{'rec_formula': '\\small\\begin{aligned}{p(\\mathbf{x})=c(\\mathbf{u})\\prod_{i}p(x_{i}).}\\\\ \\end{aligned}', 'formula_region_id': 1, 'dt_polys': ([553.0718, 802.0996, 758.75635, 853.093],)}, ...]}}
paddlex --pipeline seal_recognition \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--device gpu:0 \
--save_path ./output
What's the result
bash
{'res': {'input_path': 'seal_text_det.png', 'model_settings': {'use_doc_preprocessor': False, 'use_layout_detection': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 16, 'label': 'seal', 'score': 0.975529670715332, 'coordinate': [6.191284, 0.16680908, 634.39325, 628.85345]}]}, 'seal_res_list': [{'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': [array([[320, 38],
[479, 92],
[483, 94],
[486, 97],
[579, 226],
[582, 230],
[582, 235],
[584, 383],
[584, 388],
[582, 392],
[578, 396],
[573, 398],
[566, 398],
[502, 380],
[497, 377],
[494, 374],
[491, 369],
[491, 366],
[488, 259],
[424, 172],
[318, 136],
[251, 154],
[200, 174],
[137, 260],
[133, 366],
[132, 370],
[130, 375],
[126, 378],
[123, 380],
[ 60, 398],
[ 55, 398],
[ 49, 397],
[ 45, 394],
[ 43, 390],
[ 41, 383],
[ 43, 236],
[ 44, 230],
[ 45, 227],
[141, 96],
[144, 93],
[148, 90],
[311, 38],
[315, 38]]), array([[461, 347],
[465, 350],
[468, 354],
[470, 360],
[470, 425],
[469, 429],
[467, 433],
[462, 437],
[456, 439],
[169, 439],
[165, 439],
[160, 436],
[157, 432],
[155, 426],
[154, 360],
[155, 356],
[158, 352],
[161, 348],
[168, 346],
[456, 346]]), array([[439, 445],
[441, 447],
[443, 451],
[444, 453],
[444, 497],
[443, 502],
[440, 504],
[437, 506],
[434, 507],
[189, 505],
[184, 504],
[182, 502],
[180, 498],
[179, 496],
[181, 453],
[182, 449],
[184, 446],
[188, 444],
[434, 444]]), array([[158, 468],
[199, 502],
[242, 522],
[299, 534],
[339, 532],
[373, 526],
[417, 508],
[459, 475],
[462, 474],
[467, 474],
[472, 476],
[502, 507],
[503, 510],
[504, 515],
[503, 518],
[501, 521],
[452, 559],
[450, 560],
[391, 584],
[390, 584],
[372, 590],
[370, 590],
[305, 596],
[302, 596],
[224, 581],
[221, 580],
[164, 553],
[162, 551],
[114, 509],
[112, 507],
[111, 503],
[112, 498],
[114, 496],
[146, 468],
[149, 466],
[154, 466]])], 'text_det_params': {'limit_side_len': 736, 'limit_type': 'min', 'thresh': 0.2, 'box_thresh': 0.6, 'unclip_ratio': 0.5}, 'text_type': 'seal', 'textline_orientation_angles': [-1, -1, -1, -1], 'text_rec_score_thresh': 0, 'rec_texts': ['夊æ´Ĩååįŧåč´¸æéå
Ŧå¸', 'åįĨ¨ä¸į¨įĢ ', 'åįšįŠ', '5263647368706'], 'rec_scores': [0.9934046268463135, 0.9999403953552246, 0.998250424861908, 0.9913849234580994], 'rec_polys': [array([[320, 38],
[479, 92],
[483, 94],
[486, 97],
[579, 226],
[582, 230],
[582, 235],
[584, 383],
[584, 388],
[582, 392],
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Computer Vision Pipelines CLI
paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0
What's the result
{'res': {'input_path': 'general_image_classification_001.jpg', 'page_index': None, 'class_ids': array([296, 170, 356, 258, 248], dtype=int32), 'scores': array([0.62736, 0.03752, 0.03256, 0.0323 , 0.03194], dtype=float32), 'label_names': ['ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', 'Irish wolfhound', 'weasel', 'Samoyed, Samoyede', 'Eskimo dog, husky']}}
paddlex --pipeline object_detection \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png \
--threshold 0.5 \
--save_path ./output/ \
--device gpu:0
What's the result
{'res': {'input_path': 'general_object_detection_002.png', 'page_index': None, 'boxes': [{'cls_id': 49, 'label': 'orange', 'score': 0.8188614249229431, 'coordinate': [661.3518, 93.05823, 870.75903, 305.93713]}, {'cls_id': 47, 'label': 'apple', 'score': 0.7745078206062317, 'coordinate': [76.80911, 274.74905, 330.5422, 520.0428]}, {'cls_id': 47, 'label': 'apple', 'score': 0.7271787524223328, 'coordinate': [285.32645, 94.3175, 469.73645, 297.40344]}, {'cls_id': 46, 'label': 'banana', 'score': 0.5576589703559875, 'coordinate': [310.8041, 361.43625, 685.1869, 712.59155]}, {'cls_id': 47, 'label': 'apple', 'score': 0.5490103363990784, 'coordinate': [764.6252, 285.76096, 924.8153, 440.92892]}, {'cls_id': 47, 'label': 'apple', 'score': 0.515821635723114, 'coordinate': [853.9831, 169.41423, 987.803, 303.58615]}, {'cls_id': 60, 'label': 'dining table', 'score': 0.514293372631073, 'coordinate': [0.53089714, 0.32445717, 1072.9534, 720]}, {'cls_id': 47, 'label': 'apple', 'score': 0.510750949382782, 'coordinate': [57.368027, 23.455347, 213.39601, 176.45612]}]}}
paddlex --pipeline instance_segmentation \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png \
--threshold 0.5 \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'general_instance_segmentation_004.png', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'person', 'score': 0.8695873022079468, 'coordinate': [339.83426, 0, 639.8651, 575.22003]}, {'cls_id': 0, 'label': 'person', 'score': 0.8572642803192139, 'coordinate': [0.09976959, 0, 195.07274, 575.358]}, {'cls_id': 0, 'label': 'person', 'score': 0.8201770186424255, 'coordinate': [88.24664, 113.422424, 401.23077, 574.70197]}, {'cls_id': 0, 'label': 'person', 'score': 0.7110118269920349, 'coordinate': [522.54065, 21.457964, 767.5007, 574.2464]}, {'cls_id': 27, 'label': 'tie', 'score': 0.5543721914291382, 'coordinate': [247.38776, 312.4094, 355.2685, 574.1264]}], 'masks': '...'}}
paddlex --pipeline small_object_detection \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg \
--threshold 0.5 \
--save_path ./output \
--device gpu:0 \
What's the result
{'res': {'input_path': 'small_object_detection.jpg', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'pedestrian', 'score': 0.8182944655418396, 'coordinate': [203.60147, 701.3809, 224.2007, 743.8429]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.8150849342346191, 'coordinate': [185.01398, 710.8665, 201.76335, 744.9308]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.7748839259147644, 'coordinate': [295.1978, 500.2161, 309.33438, 532.0253]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.7688254714012146, 'coordinate': [851.5233, 436.13293, 863.2146, 466.8981]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.689735472202301, 'coordinate': [802.1584, 460.10693, 815.6586, 488.85086]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.6697502136230469, 'coordinate': [479.947, 309.43323, 489.1534, 332.5485]}, ...]}}
paddlex --pipeline human_keypoint_detection \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/keypoint_detection_001.jpg \
--det_threshold 0.5 \
--save_path ./output/ \
--device gpu:0
What's the result
{'res': {'input_path': 'keypoint_detection_001.jpg', 'boxes': [{'coordinate': [325.65088, 74.46718, 391.5512, 209.46529], 'det_score': 0.9316536784172058, 'keypoints': array([[351.6419 , 84.80058 , 0.79337054],
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[349.12946 , 83.09801 , 0.7885327 ],
[359.24466 , 83.369225 , 0.80503 ],
[347.46167 , 84.1535 , 0.8710606 ],
[368.82172 , 101.33514 , 0.88625187],
[339.8064 , 99.65537 , 0.8432633 ],
[371.2092 , 123.35563 , 0.7728337 ],
[337.78214 , 121.36371 , 0.9310819 ],
[368.81366 , 142.71593 , 0.79723483],
[337.53455 , 139.85892 , 0.877297 ],
[363.0265 , 141.82988 , 0.7964988 ],
[345.3075 , 141.98972 , 0.7532031 ],
[374.60806 , 171.42578 , 0.7530604 ],
[339.11694 , 167.98814 , 0.7255032 ],
[382.67047 , 197.82553 , 0.73685765],
[336.79745 , 196.5194 , 0.626142 ]], dtype=float32), 'kpt_score': 0.7961825}, {'coordinate': [271.96713, 69.02892, 336.77832, 217.54662], 'det_score': 0.9304604530334473, 'keypoints': array([[294.48553 , 84.144104 , 0.74851245],
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[292.39313 , 81.7721 , 0.74603605],
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[293.70663 , 204.9227 , 0.72405976]], dtype=float32), 'kpt_score': 0.77107316}, {'coordinate': [293.55933, 188.65804, 419.47382, 305.4712], 'det_score': 0.9179267883300781, 'keypoints': array([[3.3565637e+02, 2.0941801e+02, 8.1438643e-01],
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[3.8441351e+02, 2.4341478e+02, 6.4083064e-01],
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[139.57607 , 122.08866 , 0.26570275]], dtype=float32), 'kpt_score': 0.67882234}, {'coordinate': [112.50212, 64.127, 150.35353, 125.85529], 'det_score': 0.5013833045959473, 'keypoints': array([[1.35197662e+02, 7.29378281e+01, 5.58694899e-01],
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[1.34379593e+02, 8.23366165e+01, 1.67876005e-01],
[1.37895874e+02, 1.08476562e+02, 1.58305198e-01],
[1.30837265e+02, 1.07525513e+02, 1.45044222e-01],
[1.31290604e+02, 1.02961494e+02, 7.68775940e-02],
[1.17951675e+02, 1.07433502e+02, 2.09531561e-01],
[1.29175934e+02, 1.14402641e+02, 1.46551579e-01],
[1.27901909e+02, 1.16773926e+02, 2.08665460e-01]], dtype=float32), 'kpt_score': 0.3005561}]}}
paddlex --pipeline open_vocabulary_segmentation \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_segmentation.jpg \
--prompt_type box \
--prompt "[[112.9,118.4,513.8,382.1],[4.6,263.6,92.2,336.6],[592.4,260.9,607.2,294.2]]" \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'open_vocabulary_segmentation.jpg', 'prompts': {'box_prompt': [[112.9, 118.4, 513.8, 382.1], [4.6, 263.6, 92.2, 336.6], [592.4, 260.9, 607.2, 294.2]]}, 'masks': '...', 'mask_infos': [{'label': 'box_prompt', 'prompt': [112.9, 118.4, 513.8, 382.1]}, {'label': 'box_prompt', 'prompt': [4.6, 263.6, 92.2, 336.6]}, {'label': 'box_prompt', 'prompt': [592.4, 260.9, 607.2, 294.2]}]}}
paddlex --pipeline open_vocabulary_detection \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_detection.jpg \
--prompt "bus . walking man . rearview mirror ." \
--thresholds "{'text_threshold': 0.25, 'box_threshold': 0.3}" \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'open_vocabulary_detection.jpg', 'page_index': None, 'boxes': [{'coordinate': [112.10542297363281, 117.93667602539062, 514.35693359375, 382.10150146484375], 'label': 'bus', 'score': 0.9348853230476379}, {'coordinate': [264.1828918457031, 162.6674346923828, 286.8844909667969, 201.86187744140625], 'label': 'rearview mirror', 'score': 0.6022508144378662}, {'coordinate': [606.1133422851562, 254.4973907470703, 622.56982421875, 293.7867126464844], 'label': 'walking man', 'score': 0.4384709894657135}, {'coordinate': [591.8192138671875, 260.2451171875, 607.3953247070312, 294.2210388183594], 'label': 'man', 'score': 0.3573091924190521}]}}
paddlex --pipeline pedestrian_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --device gpu:0
What's the result
{'res': {'input_path': 'pedestrian_attribute_002.jpg', 'boxes': [{'labels': ['Trousers(éŋčŖ¤)', 'Age18-60(åš´éžå¨18-60å˛äšé´)', 'LongCoat(éŋå¤åĨ)', 'Side(äž§éĸ)'], 'cls_scores': array([0.99965, 0.99963, 0.98866, 0.9624 ]), 'det_score': 0.9795178771018982, 'coordinate': [87.24581, 322.5872, 546.2697, 1039.9852]}, {'labels': ['Trousers(éŋčŖ¤)', 'LongCoat(éŋå¤åĨ)', 'Front(éĸæå)', 'Age18-60(åš´éžå¨18-60å˛äšé´)'], 'cls_scores': array([0.99996, 0.99872, 0.93379, 0.71614]), 'det_score': 0.967143177986145, 'coordinate': [737.91626, 306.287, 1150.5961, 1034.2979]}, {'labels': ['Trousers(éŋčŖ¤)', 'LongCoat(éŋå¤åĨ)', 'Age18-60(åš´éžå¨18-60å˛äšé´)', 'Side(äž§éĸ)'], 'cls_scores': array([0.99996, 0.99514, 0.98726, 0.96224]), 'det_score': 0.9645745754241943, 'coordinate': [399.45944, 281.9107, 869.5312, 1038.9962]}]}}
paddlex --pipeline vehicle_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --device gpu:0
What's the result
{'res': {'input_path': 'vehicle_attribute_002.jpg', 'boxes': [{'labels': ['red(įēĸč˛)', 'sedan(čŊŋčŊĻ)'], 'cls_scores': array([0.96375, 0.94025]), 'det_score': 0.9774094820022583, 'coordinate': [196.32553, 302.3847, 639.3131, 655.57904]}, {'labels': ['suv(SUV)', 'brown(æŖč˛)'], 'cls_scores': array([0.99968, 0.99317]), 'det_score': 0.9705657958984375, 'coordinate': [769.4419, 278.8417, 1401.0217, 641.3569]}]}}
paddlex --pipeline rotated_object_detection \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/rotated_object_detection_001.png \
--threshold 0.5 \
--save_path ./output \
--device gpu:0
What's the result
{'res': {'input_path': 'rotated_object_detection_001.png', 'page_index': None, 'boxes': [{'cls_id': 4, 'label': 'small-vehicle', 'score': 0.7409099340438843, 'coordinate': [92.88687, 763.1569, 85.163124, 749.5868, 116.07975, 731.99414, 123.8035, 745.5643]}, {'cls_id': 4, 'label': 'small-vehicle', 'score': 0.7393015623092651, 'coordinate': [348.2332, 177.55974, 332.77704, 150.24973, 345.2183, 143.21028, 360.67444, 170.5203]}, {'cls_id': 11, 'label': 'roundabout', 'score': 0.8101699948310852, 'coordinate': [537.1732, 695.5475, 204.4297, 612.9735, 286.71338, 281.48022, 619.4569, 364.05426]}]}}
Time Series-related CLI
paddlex --pipeline ts_forecast --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --save_path ./output --device gpu:0
What's the result
{'input_path': 'ts_fc.csv', 'forecast': OT
date
2018-06-26 20:00:00 9.586131
2018-06-26 21:00:00 9.379762
2018-06-26 22:00:00 9.252275
2018-06-26 23:00:00 9.249993
2018-06-27 00:00:00 9.164998
... ...
2018-06-30 15:00:00 8.830340
2018-06-30 16:00:00 9.291553
2018-06-30 17:00:00 9.097666
2018-06-30 18:00:00 8.905430
2018-06-30 19:00:00 8.993793
[96 rows x 1 columns]}
Speech-related Pipelines CLI
paddlex --pipeline multilingual_speech_recognition \
--input https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav \
--save_path ./output \
--device gpu:0
What's the result
{'input_path': 'zh.wav', 'result': {'text': 'æčޤä¸ē莿ĨæéčĻįå°ąæ¯įģæå¸ĻæĨäēčēĢäŊåĨåēˇ', 'segments': [{'id': 0, 'seek': 0, 'start': 0.0, 'end': 2.0, 'text': 'æčޤä¸ē莿ĨæéčĻįå°ąæ¯', 'tokens': [50364, 1654, 7422, 97, 13992, 32585, 31429, 8661, 24928, 1546, 5620, 50464, 50464, 49076, 4845, 99, 34912, 19847, 29485, 44201, 6346, 115, 50564], 'temperature': 0, 'avg_logprob': -0.22779104113578796, 'compression_ratio': 0.28169014084507044, 'no_speech_prob': 0.026114309206604958}, {'id': 1, 'seek': 200, 'start': 2.0, 'end': 31.0, 'text': 'įģæå¸ĻæĨäēčēĢäŊåĨåēˇ', 'tokens': [50364, 49076, 4845, 99, 34912, 19847, 29485, 44201, 6346, 115, 51814], 'temperature': 0, 'avg_logprob': -0.21976988017559052, 'compression_ratio': 0.23684210526315788, 'no_speech_prob': 0.009023111313581467}], 'language': 'zh'}}
Video-related Pipelines CLI
paddlex --pipeline video_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/HorseRiding.avi --device gpu:0 --save_path output
What's the result
{'input_path': 'HorseRiding.avi', 'result': [[[[110, 40, 170, 171], 0.8385784886274905, 'HorseRiding']], [[[112, 31, 168, 167], 0.8587647461352432, 'HorseRiding']], [[[106, 28, 164, 165], 0.8579590929730969, 'HorseRiding']], [[[106, 24, 165, 171], 0.8743957465404151, 'HorseRiding']], [[[107, 22, 165, 172], 0.8488322619908999, 'HorseRiding']], [[[112, 22, 173, 171], 0.8446755521458691, 'HorseRiding']], [[[115, 23, 177, 176], 0.8454028365262367, 'HorseRiding']], [[[117, 22, 178, 179], 0.8484261880748285, 'HorseRiding']], [[[117, 22, 181, 181], 0.8319480115446183, 'HorseRiding']], [[[117, 39, 182, 183], 0.820551099084625, 'HorseRiding']], [[[117, 41, 183, 185], 0.8202395831914338, 'HorseRiding']], [[[121, 47, 185, 190], 0.8261058921745246, 'HorseRiding']], [[[123, 46, 188, 196], 0.8307278306829033, 'HorseRiding']], [[[125, 44, 189, 197], 0.8259781361122833, 'HorseRiding']], [[[128, 47, 191, 195], 0.8227593229866699, 'HorseRiding']], [[[127, 44, 192, 193], 0.8205373129456817, 'HorseRiding']], [[[129, 39, 192, 185], 0.8223318812628619, 'HorseRiding']], [[[127, 31, 196, 179], 0.8501208612019866, 'HorseRiding']], [[[128, 22, 193, 171], 0.8315708410681566, 'HorseRiding']], [[[130, 22, 192, 169], 0.8318588228062005, 'HorseRiding']], [[[132, 18, 193, 170], 0.8310494469100611, 'HorseRiding']], [[[132, 18, 194, 172], 0.8302132445350239, 'HorseRiding']], [[[133, 18, 194, 176], 0.8339063714162727, 'HorseRiding']], [[[134, 26, 200, 183], 0.8365876380675275, 'HorseRiding']], [[[133, 16, 198, 182], 0.8395230321418268, 'HorseRiding']], [[[133, 17, 199, 184], 0.8198139782724922, 'HorseRiding']], [[[140, 28, 204, 189], 0.8344166596681291, 'HorseRiding']], [[[139, 27, 204, 187], 0.8412694521771158, 'HorseRiding']], [[[139, 28, 204, 185], 0.8500098862888805, 'HorseRiding']], [[[135, 19, 199, 179], 0.8506627974981384, 'HorseRiding']], [[[132, 15, 201, 178], 0.8495054272547193, 'HorseRiding']], [[[136, 14, 199, 173], 0.8451630721500223, 'HorseRiding']], [[[136, 12, 200, 167], 0.8366456814214907, 'HorseRiding']], [[[133, 8, 200, 168], 0.8457252233401213, 'HorseRiding']], [[[131, 7, 197, 162], 0.8400586356358062, 'HorseRiding']], [[[131, 8, 195, 163], 0.8320492682901985, 'HorseRiding']], [[[129, 4, 194, 159], 0.8298043752822792, 'HorseRiding']], [[[127, 5, 194, 162], 0.8348390851948722, 'HorseRiding']], [[[125, 7, 190, 164], 0.8299688814865505, 'HorseRiding']], [[[125, 6, 191, 164], 0.8303107088154711, 'HorseRiding']], [[[123, 8, 190, 168], 0.8348342187965798, 'HorseRiding']], [[[125, 14, 189, 170], 0.8356523950497134, 'HorseRiding']], [[[127, 18, 191, 171], 0.8392671764931521, 'HorseRiding']], [[[127, 30, 193, 178], 0.8441704160826191, 'HorseRiding']], [[[128, 18, 190, 181], 0.8438125326146775, 'HorseRiding']], [[[128, 12, 189, 186], 0.8390128962093542, 'HorseRiding']], [[[129, 15, 190, 185], 0.8471056476788448, 'HorseRiding']], [[[129, 16, 191, 184], 0.8536121834731034, 'HorseRiding']], [[[129, 16, 192, 185], 0.8488154629800881, 'HorseRiding']], [[[128, 15, 194, 184], 0.8417711698421471, 'HorseRiding']], [[[129, 13, 195, 187], 0.8412510238991473, 'HorseRiding']], [[[129, 14, 191, 187], 0.8404350980083457, 'HorseRiding']], [[[129, 13, 190, 189], 0.8382891279858882, 'HorseRiding']], [[[129, 11, 187, 191], 0.8318282305903217, 'HorseRiding']], [[[128, 8, 188, 195], 0.8043430817880264, 'HorseRiding']], [[[131, 25, 193, 199], 0.826184954516826, 'HorseRiding']], [[[124, 35, 191, 203], 0.8270462809459467, 'HorseRiding']], [[[121, 38, 191, 206], 0.8350931715324705, 'HorseRiding']], [[[124, 41, 195, 212], 0.8331239341053625, 'HorseRiding']], [[[128, 42, 194, 211], 0.8343046153103657, 'HorseRiding']], [[[131, 40, 192, 203], 0.8309784496027532, 'HorseRiding']], [[[130, 32, 195, 202], 0.8316640083647542, 'HorseRiding']], [[[135, 30, 196, 197], 0.8272172409555161, 'HorseRiding']], [[[131, 16, 197, 186], 0.8388410406147955, 'HorseRiding']], [[[134, 15, 202, 184], 0.8485738297037244, 'HorseRiding']], [[[136, 15, 209, 182], 0.8529430205135213, 'HorseRiding']], [[[134, 13, 218, 182], 0.8601191479922718, 'HorseRiding']], [[[144, 10, 213, 183], 0.8591963099263467, 'HorseRiding']], [[[151, 12, 219, 184], 0.8617965108346937, 'HorseRiding']], [[[151, 10, 220, 186], 0.8631923599955371, 'HorseRiding']], [[[145, 10, 216, 186], 0.8800860885204287, 'HorseRiding']], [[[144, 10, 216, 186], 0.8858840451538228, 'HorseRiding']], [[[146, 11, 214, 190], 0.8773644144886106, 'HorseRiding']], [[[145, 24, 214, 193], 0.8605544385867248, 'HorseRiding']], [[[146, 23, 214, 193], 0.8727294882672254, 'HorseRiding']], [[[148, 22, 212, 198], 0.8713131467067079, 'HorseRiding']], [[[146, 29, 213, 197], 0.8579099324651196, 'HorseRiding']], [[[154, 29, 217, 199], 0.8547794072847914, 'HorseRiding']], [[[151, 26, 217, 203], 0.8641733722316758, 'HorseRiding']], [[[146, 24, 212, 199], 0.8613466257602624, 'HorseRiding']], [[[142, 25, 210, 194], 0.8492670944810214, 'HorseRiding']], [[[134, 24, 204, 192], 0.8428117300203049, 'HorseRiding']], [[[136, 25, 204, 189], 0.8486779356971397, 'HorseRiding']], [[[127, 21, 199, 179], 0.8513896296400709, 'HorseRiding']], [[[125, 10, 192, 192], 0.8510201771386576, 'HorseRiding']], [[[124, 8, 191, 192], 0.8493999019508465, 'HorseRiding']], [[[121, 8, 192, 193], 0.8487097098892171, 'HorseRiding']], [[[119, 6, 187, 193], 0.847543279648022, 'HorseRiding']], [[[118, 12, 190, 190], 0.8503535936620565, 'HorseRiding']], [[[122, 22, 189, 194], 0.8427901493276977, 'HorseRiding']], [[[118, 24, 188, 195], 0.8418835400352087, 'HorseRiding']], [[[120, 25, 188, 205], 0.847192725785284, 'HorseRiding']], [[[122, 25, 189, 207], 0.8444105420674433, 'HorseRiding']], [[[120, 23, 189, 208], 0.8470784016639392, 'HorseRiding']], [[[121, 23, 188, 205], 0.843428111269418, 'HorseRiding']], [[[117, 23, 186, 206], 0.8420809714166708, 'HorseRiding']], [[[119, 5, 199, 197], 0.8288265053231356, 'HorseRiding']], [[[121, 8, 192, 195], 0.8197548738023599, 'HorseRiding']]]}
đ Python Usage¶
A few lines of code can complete the quick inference of the pipeline, with a unified Python script format as follows:
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline=[pipeline name])
output = pipeline.predict([input image name])
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
create_pipeline()
instantiates the pipeline object- Pass in the image and call the
predict
method of the pipeline object for inference prediction - Process the prediction results
OCR-related Python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="OCR")
output = pipeline.predict(
input="./general_ocr_002.png",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
use_textline_orientation=False,
)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="table_recognition")
output = pipeline.predict(
input="table_recognition.jpg",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
)
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_xlsx("./output/")
res.save_to_html("./output/")
res.save_to_json("./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="table_recognition_v2")
output = pipeline.predict(
input="table_recognition_v2.jpg",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
)
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_xlsx("./output/")
res.save_to_html("./output/")
res.save_to_json("./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="layout_parsing")
output = pipeline.predict(
input="./layout_parsing_demo.png",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
use_textline_orientation=False,
)
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_img(save_path="./output/") ## Save the result in img format
res.save_to_json(save_path="./output/") ## Save the result in json format
res.save_to_xlsx(save_path="./output/") ## Save the result in table format
res.save_to_html(save_path="./output/") ## Save the result in html format
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="PP-StructureV3")
output = pipeline.predict(
input="./pp_structure_v3_demo.png",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
use_textline_orientation=False,
)
for res in output:
res.print()
res.save_to_json(save_path="output")
res.save_to_markdown(save_path="output")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="formula_recognition")
output = pipeline.predict(
input="./general_formula_recognition_001.png",
use_layout_detection=True,
use_doc_orientation_classify=False,
use_doc_unwarping=False,
layout_threshold=0.5,
layout_nms=True,
layout_unclip_ratio=1.0,
layout_merge_bboxes_mode="large"
)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="seal_recognition")
output = pipeline.predict(
"seal_text_det.png",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
)
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_img("./output/") ## Save the visualization result
res.save_to_json("./output/") ## Save the visualization result
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="doc_preprocessor")
output = pipeline.predict(
input="doc_test_rotated.jpg",
use_doc_orientation_classify=True,
use_doc_unwarping=True,
)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
Computer Vision Pipeline Command-Line Usage
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="image_classification")
output = pipeline.predict("general_image_classification_001.jpg")
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_img(save_path="./output/") ## Save the visualized result image
res.save_to_json(save_path="./output/") ## Save the structured output of the prediction
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="semantic_segmentation")
output = pipeline.predict(input="general_semantic_segmentation_002.png", target_size = -1)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="image_multilabel_classification")
output = pipeline.predict("general_image_classification_001.jpg")
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_img("./output/") ## Save the visualized result image
res.save_to_json("./output/") ## Save the structured output of the prediction
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="anomaly_detection")
output = pipeline.predict(input="uad_grid.png")
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_img(save_path="./output/") ## Save the visualized result image
res.save_to_json(save_path="./output/") ## Save the structured output of the prediction
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="3d_bev_detection")
output = pipeline.predict("./data/nuscenes_demo/nuscenes_infos_val.pkl")
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_json("./output/") ## Save the result to a JSON file
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="open_vocabulary_segmentation")
output = pipeline.predict(input="open_vocabulary_segmentation.jpg", prompt_type="box", prompt=[[112.9,118.4,513.8,382.1],[4.6,263.6,92.2,336.6],[592.4,260.9,607.2,294.2]])
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="open_vocabulary_detection")
output = pipeline.predict(input="open_vocabulary_detection.jpg", prompt="bus . walking man . rearview mirror .")
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="pedestrian_attribute_recognition")
output = pipeline.predict("pedestrian_attribute_002.jpg")
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_img("./output/") ## Save the visualized result image
res.save_to_json("./output/") ## Save the structured output of the prediction
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="vehicle_attribute_recognition")
output = pipeline.predict("vehicle_attribute_002.jpg")
for res in output:
res.print() ## Print the structured output of the prediction
res.save_to_img("./output/") ## Save the visualized result image
res.save_to_json("./output/") ## Save the structured output of the prediction
Command Line Usage for Time Series pipelines
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="ts_forecast")
output = pipeline.predict(input="ts_fc.csv")
for res in output:
res.print() ## Print the structured prediction output
res.save_to_csv(save_path="./output/") ## Save results in CSV format
res.save_to_json(save_path="./output/") ## Save results in JSON format
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="ts_anomaly_detection")
output = pipeline.predict("ts_ad.csv")
for res in output:
res.print() ## Print the structured prediction output
res.save_to_csv(save_path="./output/") ## Save results in CSV format
res.save_to_json(save_path="./output/") ## Save results in JSON format
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="ts_classification")
output = pipeline.predict("ts_cls.csv")
for res in output:
res.print() ## Print the structured prediction output
res.save_to_csv(save_path="./output/") ## Save results in CSV format
res.save_to_json(save_path="./output/") ## Save results in JSON format
Command Line Usage for Speech pipelines
Command Line Usage for Video pipelines
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="video_detection")
output = pipeline.predict(input="HorseRiding.avi")
for res in output:
res.print() ## Print the structured prediction output
res.save_to_video(save_path="./output/") ## Save the visualized video results
res.save_to_json(save_path="./output/") ## Save the structured prediction output
đ Detailed Tutorials¶
-
PP-ChatOCRv4-doc
Document Scene Information Extraction v4 (PP-ChatOCRv4-doc) is a PaddlePaddle-based intelligent document and image analysis solution that integrates LLM (Large Language Model), MLLM (Multimodal Large Language Model), and OCR (Optical Character Recognition) technologies. It provides a one-stop solution for common challenges in complex document information extraction, such as layout analysis, rare character recognition, multi-page PDFs, table extraction, and seal detection.
-
OCR
The general OCR pipeline is used to solve text recognition tasks, extract text information from images, and output it in text form. Based on the end-to-end OCR system, it can achieve millisecond-level precise text content prediction on CPUs and reach open-source SOTA in general scenarios.
-
PP-StructureV3
The PP-StructureV3 pipeline enhances the capabilities of layout area detection, table recognition, and formula recognition based on the General Layout Parsing v1 pipeline. It also adds the ability to restore multi-column reading order and convert results to Markdown files. It performs well on various document datasets and can handle more complex document data.
-
General Table Recognition Pipeline v2
General Table Recognition Pipeline v2 is designed to solve table recognition tasks by identifying tables in images and outputting them in HTML format. This pipeline enables precise table prediction and is applicable across various fields, including general, manufacturing, finance, and transportation.
-
Small Object Detection
Small object detection is a technology specifically designed to recognize smaller objects in images, widely used in surveillance, unmanned driving, and satellite image analysis fields. It can accurately locate and classify small-sized objects such as pedestrians, traffic signs, or small animals from complex scenes.
-
Time Series Forecasting
Time series forecasting is a technique that uses historical data to predict future trends by analyzing the patterns of change in time series data. It is widely used in financial markets, weather forecasting, and sales forecasting fields.
đŦ Discussion¶
We warmly welcome and encourage community members to raise questions, share ideas, and feedback in the Discussions section. Whether you want to report a bug, discuss a feature request, seek help, or just want to keep up with the latest project news, this is a great platform.