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PaddleX

🔍 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.

Image Classification Multi-label Image Classification Object Detection Instance Segmentation
Semantic Segmentation Image Anomaly Detection OCR Table Recognition
PP-ChatOCRv3-doc Time Series Forecasting Time Series Anomaly Detection Time Series Classification

đŸ› 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

python -m pip install paddlepaddle==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/

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

pip install paddlex==3.0rc0

❗ For more installation methods, please refer to the PaddleX Installation Guide

đŸ’ģ Command Line Usage

A single command can quickly experience the pipeline effect, with a unified command line format as follows:

paddlex --pipeline [pipeline name] --input [input image] --device [running device]

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', 'čŋžįģ­åˇĨäŊœ3', '取å‡ēæĨ攞在įŊ‘上', 'æ˛Ąæƒŗ', 'æąŸã€æ•´æąŸį­‰å…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>čŋžįģ­åˇĨäŊœ3</td><td>取å‡ēæĨ攞在įŊ‘上 æ˛Ąæƒŗ</td><td>æąŸã€æ•´æąŸį­‰å…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', 'čŋžįģ­åˇĨäŊœ3', '取å‡ēæĨ攞在įŊ‘上', 'æ˛Ąæƒŗ', 'æąŸã€æ•´æąŸį­‰å…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': ['部门', 'æŠĨ销äēē', 'æŠĨ销äē‹į”ą', '扚准äēēīŧš', '单捎', 'åŧ ', 'åˆčŽĄé‡‘éĸ', '元', 'čŊĻč´šįĨ¨', 'å…ļ', 'įĢčŊĻč´šįĨ¨', 'éŖžæœēįĨ¨', '中', '旅äŊåŽŋč´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>部门</td><td></td><td>æŠĨ销äēē</td><td></td><td>æŠĨ销äē‹į”ą</td><td></td><td colspan="2">扚准äēēīŧš 单捎 åŧ </td></tr><tr><td colspan="6" rowspan="8"></td><td colspan="2"></td></tr><tr><td colspan="2">åˆčŽĄé‡‘éĸ</td></tr><tr><td rowspan="6"></td><td></td></tr><tr><td></td></tr><tr><td>éŖžæœēįĨ¨</td></tr><tr><td>旅äŊåŽŋč´š å…ļäģ– čĄĨč´´</td></tr><tr><td>中</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': ['部门', 'æŠĨ销äēē', 'æŠĨ销äē‹į”ą', '扚准äēēīŧš', '单捎', 'åŧ ', 'åˆčŽĄé‡‘éĸ', '元', 'čŊĻč´šįĨ¨', 'å…ļ', 'įĢčŊĻč´šįĨ¨', 'éŖžæœēįĨ¨', '中', '旅äŊåŽŋč´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': '劊力双斚äē¤åž€\n搭åģēå‹č°ŠæĄĨæĸ'}, {'block_bbox': [587.4096, 160.58267, 927.6319, 179.2817], 'block_label': 'figure_title', 'block_content': 'æœŦæŠĨčŽ°č€…æ˛ˆå°æ™“äģģåŊĻéģ„埚昭'}, {'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': '厄įĢ‹į‰šé‡ŒäēšéĢ˜į­‰æ•™č‚˛ä¸Žį ”įŠļé™ĸ合äŊœåģēįĢ‹īŧŒåŧ€\n莞äē†ä¸­å›Ŋč¯­č¨€č¯žį¨‹å’Œä¸­å›Ŋæ–‡åŒ–č¯žį¨‹īŧŒæŗ¨å†Œå­Ļ\nį”Ÿ2万äŊ™äēēæŦĄã€‚10äŊ™åš´æĨīŧŒåŽ„į‰šå­”é™ĸåˇ˛æˆä¸ē\nåŊ“地民äŧ—äē†č§Ŗä¸­å›Ŋįš„ä¸€æ‰‡įĒ—åŖã€‚'}, {'block_bbox': [9.714512, 202.68811, 359.62323, 340.3127], 'block_label': 'text', 'block_content': 'čēĢį€ä¸­å›Ŋäŧ į쟿°‘æ—æœčŖ…įš„åŽ„įĢ‹į‰šé‡Œäēšé’\n嚴䞝æŦĄį™ģå°čĄ¨æŧ”中å›Ŋæ°‘æ—čˆžã€įŽ°äģŖčˆžã€æ‰‡å­čˆž\nį­‰īŧŒæ›ŧåĻ™įš„čˆžå§ŋčĩĸåž—įŽ°åœē观äŧ—é˜ĩé˜ĩæŽŒåŖ°ã€‚čŋ™\n是æ—Ĩ前厄įĢ‹į‰šé‡ŒäēšéĢ˜į­‰æ•™č‚˛ä¸Žį ”įŠļé™ĸ孔子å­Ļ\né™ĸ(äģĨä¸‹įŽ€į§°"åŽ„į‰šå­”é™ĸ"丞办“喜čŋŽæ–°åš´"中å›Ŋ\næ­Œčˆžæ¯”čĩ›įš„åœē景。'}, {'block_bbox': [390.74124, 298.42255, 740.8009, 436.79193], 'block_label': 'text', 'block_content': 'éģ„é¸ŖéŖžčĄ¨į¤ēīŧŒéšį€æĨå­Ļäš ä¸­æ–‡įš„äēēæ—Ĩį›Š\nåĸžå¤šīŧŒé˜ŋ斯éŠŦ拉大å­Ļ教å­Ļį‚šåˇ˛éšžäģĨæģĄčļŗæ•™å­Ļ\n需čĻã€‚2024åš´4月īŧŒį”ąä¸­äŧčœ€é“集å›ĸæ‰€åąžå››\nåˇčˇ¯æĄĨæ‰ŋåģēįš„å­”é™ĸ教å­ĻæĨŧéĄšį›Žåœ¨é˜ŋ斯éŠŦ拉åŧ€\nåˇĨåģē莞īŧŒéĸ„莥äģŠåš´ä¸ŠåŠåš´åŗģåˇĨīŧŒåģ翈åŽå°†ä¸ē厄\nį‰šå­”é™ĸæäž›å…¨æ–°įš„åŠžå­Ļåœē地。'}, {'block_bbox': [10.579921, 346.26508, 359.13733, 436.17682], 'block_label': 'text', 'block_content': '中å›Ŋ和厄įĢ‹į‰šé‡Œäēšäŧ įģŸå‹č°ŠæˇąåŽšã€‚čŋ‘åš´\næĨ,在éĢ˜č´¨é‡å…ąåģē“一å¸Ļ䏀莝"æĄ†æžļ下īŧŒä¸­åބ䏤\nå›Ŋäēēæ–‡ä礿ĩä¸æ–­æˇąåŒ–īŧŒäē’刊合äŊœįš„æ°‘意åŸēįĄ€\næ—Ĩį›ŠæˇąåŽšã€‚'}, {'block_bbox': [410.51892, 457.0816, 722.768, 516.78217], 'block_label': 'text', 'block_content': '“在中å›Ŋå­Ļäš įš„įģåކ\nčŽŠæˆ‘įœ‹åˆ°æ›´åšŋé˜”įš„ä¸–į•Œâ€'}, {'block_bbox': [30.334734, 457.53757, 341.92758, 516.81995], 'block_label': 'paragraph_title', 'block_content': '“å­ĻåĨŊ中文īŧŒæˆ‘äģŦįš„\næœĒæĨ不是æĸĻ"'}, {'block_bbox': [390.89084, 538.1777, 742.1934, 604.6777], 'block_label': 'text', 'block_content': '多嚴æĨīŧŒåŽ„įĢ‹į‰šé‡Œäēšåšŋ大čĩ´åŽį•™å­Ļį”Ÿå’Œ\nåŸščŽ­äēēå‘˜į§¯æžæŠ•čēĢå›ŊåŽļåģē莞īŧŒæˆä¸ē劊力č¯Ĩå›Ŋ\nå‘åą•įš„äē翉å’ŒåŽ„ä¸­å‹åĨŊįš„č§č¯č€…å’ŒæŽ¨åŠ¨č€…ã€‚'}, {'block_bbox': [9.884378, 538.3683, 359.43878, 652.03644], 'block_label': 'text', 'block_content': 'â€œé˛œčŠąæ›žå‘Šč¯‰æˆ‘äŊ æ€Žæ ˇčĩ°čŋ‡īŧŒå¤§åœ°įŸĨ道äŊ \nåŋƒä¸­įš„æ¯ä¸€ä¸Ē角čŊâ€Ļâ€Ļ"厄įĢ‹į‰šé‡Œäēšé˜ŋ斯éŠŦ拉\n大å­Ļįģŧ合æĨŧäēŒåą‚īŧŒä¸€é˜ĩäŧ˜įžŽįš„æ­ŒåŖ°åœ¨čĩ°åģŠé‡Œå›ž\n响。åžĒį€į†Ÿæ‚‰įš„æ—‹åž‹čŊģčŊģæŽ¨åŧ€ä¸€é—´æ•™åŽ¤įš„é—¨īŧŒ\nå­Ļį”ŸäģŦæ­ŖčˇŸį€č€å¸ˆå­Ļå”ąä¸­æ–‡æ­Œæ›˛ã€ŠåŒä¸€éĻ–æ­Œã€‹ã€‚'}, {'block_bbox': [390.88583, 610.61304, 741.1856, 747.91656], 'block_label': 'text', 'block_content': '在厄įĢ‹į‰šé‡Œäēšå…¨å›ŊåχåĨŗč”į›ŸåˇĨäŊœįš„įēĻįŋ°\nå¨œÂˇį‰šéŸĻå°”åžˇÂˇå‡¯čŽąåĄ”å°ąæ˜¯å…ļ中一äŊã€‚åĨšæ›žåœ¨\n中华åĨŗå­å­Ļé™ĸæ”ģč¯ģįĄ•åŖĢå­ĻäŊīŧŒį ”įŠļ斚向是åĨŗ\n性éĸ†å¯ŧåŠ›ä¸Žį¤žäŧšå‘åą•ã€‚å…ļ间īŧŒåĨšåŽžåœ°čĩ°čŽŋ中å›Ŋ\n多ä¸Ē地åŒēīŧŒčŽˇåž—äē†č§‚察中å›Ŋį¤žäŧšå‘åą•įš„įŦŦ一\n手čĩ„料。'}, {'block_bbox': [10.115321, 658.7913, 359.40344, 771.3188], 'block_label': 'text', 'block_content': 'čŋ™æ˜¯åŽ„į‰šå­”é™ĸé˜ŋ斯éŠŦ拉大å­Ļ教å­Ļį‚šįš„ä¸€\nčŠ‚ä¸­æ–‡æ­Œæ›˛č¯žã€‚ä¸ēäē†čŽŠå­Ļį”ŸäģŦ更åĨŊåœ°į†č§Ŗæ­Œ\nč¯å¤§æ„īŧŒč€å¸ˆå°¤æ–¯æ‹‰ÂˇįІįŊ•éģ˜åžˇč¨å°”Âˇäž¯čĩ›å› é€\n字įŋģč¯‘å’Œč§Ŗé‡Šæ­Œč¯ã€‚éšį€äŧ´åĨåŖ°å“čĩˇīŧŒå­Ļį”ŸäģŦ\nčžšå”ąčžšéšį€čŠ‚æ‹æ‘‡åŠ¨čēĢäŊ“īŧŒįްåœēæ°”æ°›įƒ­įƒˆã€‚'}, {'block_bbox': [809.6597, 705.4076, 1485.5686, 747.42346], 'block_label': 'figure_title', 'block_content': '在厄įĢ‹į‰šé‡Œäēšä¸äš…å‰ä¸žåŠžįš„įŦŦå…­åąŠä¸­å›ŊéŖŽį­æ–‡åŒ–čŠ‚ä¸ŠīŧŒåŊ“地小å­Ļį”ŸäŊ“éĒŒéŖŽį­åˆļäŊœã€‚\n中å›ŊéŠģ厄įĢ‹į‰šé‡Œäēšå¤§äŊŋéĻ†äž›å›ž'}, {'block_bbox': [389.62894, 753.4464, 742.0593, 890.9599], 'block_label': 'text', 'block_content': '谈čĩˇåœ¨ä¸­å›Ŋæą‚å­Ļįš„įģåކīŧŒįēĻįŋ°å¨œčްåŋ†įŠš\n新īŧšâ€œä¸­å›Ŋįš„å‘åą•åœ¨åŊ“äģŠä¸–į•Œæ˜¯į‹Ŧ一无äēŒįš„。\næ˛ŋį€ä¸­å›Ŋį‰šč‰˛į¤žäŧšä¸ģäš‰é“čˇ¯åšåŽšå‰čĄŒīŧŒä¸­å›Ŋ\n创造äē†å‘åą•åĨ‡čŋšīŧŒčŋ™ä¸€åˆ‡éƒŊįĻģ不åŧ€ä¸­å›Ŋå…ąäē§å…š\nįš„éĸ†å¯ŧ。中å›Ŋįš„å‘åą•įģénjå€ŧåž—čŽ¸å¤šå›ŊåŽļå­Ļäš \n借鉴。”'}, {'block_bbox': [9.867948, 777.38995, 360.40143, 843.43], 'block_label': 'text', 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'æ˛ŋį€ä¸­å›Ŋį‰šč‰˛į¤žäŧšä¸ģäš‰é“čˇ¯åšåŽšå‰čĄŒīŧŒä¸­å›Ŋ', 'åĨŊäē†åœ¨ä¸€čĩˇīŧŒæˆ‘äģŦæŦĸčŋŽäŊ â€Ļâ€Ļâ€Ļ"在一åœē中厄青', 'åšį‰ŠéφäēŒåą‚é™ˆåˆ—į€ä¸€ä¸Ē发掘č‡Ēé˜ŋ杜刊', 'æœ€å°įš„äģ…æœ‰6å˛ã€‚"å°¤æ–¯æ‹‰å‘Šč¯‰čŽ°č€…ã€‚', '创造äē†å‘åą•åĨ‡čŋšīŧŒčŋ™ä¸€åˆ‡éƒŊįĻģ不åŧ€ä¸­å›Ŋå…ąäē§å…š', 'åš´č”č°Šæ´ģ动上īŧŒå››åˇčˇ¯æĄĨ中斚员åˇĨ同åŊ“地大', 'æ–¯å¤åŸŽįš„ä¸­å›Ŋ古äģŖé™ļåˆļ酒器īŧŒįŊčēĢä¸Šå†™į€', '尤斯拉äģŠåš´23垁īŧŒæ˜¯åŽ„įĢ‹į‰šé‡Œäēšä¸€æ‰€å…ŦįĢ‹', 'įš„éĸ†å¯ŧ。中å›Ŋįš„å‘åą•įģénjå€ŧåž—čŽ¸å¤šå›ŊåŽļå­Ļäš ', 'å­Ļį”Ÿåˆå”ąã€ŠåŒ—äēŦæŦĸčŋŽäŊ ã€‹ã€‚厄įĢ‹į‰šé‡Œä皿Š€æœ¯å­Ļ', '“万”“和”“įĻ…â€â€œåąą"į­‰æą‰å­—ã€‚â€œčŋ™äģļæ–‡į‰Šč¯', 'å­Ļæ Ąįš„č‰ēæœ¯č€å¸ˆã€‚åĨš12垁åŧ€å§‹åœ¨åŽ„į‰šå­”é™ĸå­Ļ', '借鉴。”', 'é™ĸčŽĄįŽ—æœēį§‘å­Ļ与åˇĨį¨‹ä¸“ä¸šå­Ļį”Ÿé˛å¤ĢåĄ”Âˇč°ĸ拉', '明īŧŒåžˆæ—ŠäģĨ前我äģŦå°ąé€ščŋ‡æĩˇä¸Šä¸įģ¸äš‹čˇ¯čŋ›čĄŒ', '䚠中文īŧŒåœ¨2017åš´įŦŦååąŠ"æą‰č¯­æĄĨ"ä¸–į•Œä¸­å­Ļį”Ÿ', 'æ­Ŗåœ¨čĨŋ南大å­Ļå­Ļäš įš„åŽ„įĢ‹į‰šé‡ŒäēšåšåŖĢį”Ÿ', '是å…ļ中一名æŧ”å”ąč€…īŧŒåĨšåžˆæ—Šäžŋ在孔é™ĸå­Ļ䚠中', 'č´¸æ˜“åž€æĨ与文化ä礿ĩã€‚čŋ™ä🿘¯åŽ„įĢ‹į‰šé‡Œäēš', '中文比čĩ›ä¸­čŽˇåž—åŽ„įĢ‹į‰šé‡Œäēščĩ›åŒēįŦŦ一名īŧŒåšļ和', 'įŠ†åĸį›–åĄ”ÂˇæŗŊįŠ†äŧŠå¯šä¸­å›Ŋæ€€æœ‰æˇąåŽšæ„Ÿæƒ…ã€‚8', '文īŧŒä¸€į›´åœ¨ä¸ēåŽģ中å›Ŋį•™å­ĻäŊœå‡†å¤‡ã€‚“čŋ™åĨæ­Œč¯', '与中å›Ŋ友åĨŊäē¤åž€åŽ†å˛įš„æœ‰åŠ›č¯æ˜Žã€‚"北įēĸæĩˇ', '同äŧ´äģŖčĄ¨åŽ„įĢ‹į‰šé‡Œäēšå‰åž€ä¸­å›Ŋå‚åŠ å†ŗčĩ›īŧŒčŽˇåž—', '是我äģŦ两å›Ŋäēēæ°‘å‹č°Šįš„į”ŸåŠ¨å†™į…§ã€‚æ— čŽē是投', 'įœåšį‰Šéφ᠔įŠļä¸Žæ–‡įŒŽéƒ¨č´Ÿč´ŖäēēäŧŠč¨ä皿–¯Âˇį‰š', 'å›ĸäŊ“äŧ˜čƒœåĨ–。2022åš´čĩˇīŧŒå°¤æ–¯æ‹‰åŧ€å§‹åœ¨åŽ„į‰šå­”', 'į›–åĄ”åœ¨į¤žäē¤åĒ’äŊ“上写下čŋ™æ ˇä¸€æŽĩč¯īŧšâ€œčŋ™æ˜¯æˆ‘', 'čēĢäēŽåŽ„įĢ‹į‰šé‡ŒäēšåŸēįĄ€čŽžæ–ŊåģēčŽžįš„ä¸­äŧå‘˜åˇĨīŧŒ', 'æ–¯æŗ•å…šå‰č¯´ã€‚', 'é™ĸå…ŧčŒæ•™æŽˆä¸­æ–‡æ­Œæ›˛īŧŒæ¯å‘¨æœĢ两ä¸Ēč¯žæ—ļ。“中å›Ŋ', 'äēēį”Ÿįš„é‡čρ䏀æ­ĨīŧŒč‡Ē此我æ‹Ĩ有äē†ä¸€åŒåšå›ēįš„', 'čŋ˜æ˜¯åœ¨ä¸­å›Ŋį•™å­Ļįš„åŽ„įĢ‹į‰šé‡Œäēšå­Ļ子īŧŒä¸¤å›Ŋäēē', '厄įĢ‹į‰šé‡Œäēšå›ŊåŽļåšį‰ŠéĻ†č€ƒå¤å­Ļ和äēēįąģå­Ļ', 'æ–‡åŒ–åšå¤§į˛žæˇąīŧŒæˆ‘å¸Œæœ›æˆ‘įš„å­Ļį”ŸäģŦčƒŊ够通čŋ‡ä¸­', '鞋子īŧŒčĩ‹ä爿ˆ‘įŠŋčļŠč†æŖ˜įš„力量。"', '民æē手åŠĒ力īŧŒåŋ…将推动两å›Ŋå…ŗįŗģ不断向前发', 'į ”įŠļå‘˜č˛å°”č’™Âˇį‰šéŸĻå°”åžˇååˆ†å–œįˆąä¸­å›Ŋ文', 'æ–‡æ­Œæ›˛æ›´åĨŊåœ°į†č§Ŗä¸­å›Ŋ文化。"åĨšč¯´ã€‚', 'įŠ†åĸį›–åĄ”å¯†åˆ‡å…ŗæŗ¨ä¸­å›Ŋ在įģæĩŽã€į§‘技、教', 'åą•ã€‚"鲁å¤ĢåĄ”č¯´ã€‚', '化。äģ–襨į¤ēīŧšâ€œå­Ļäš åŊŧæ­¤įš„č¯­č¨€å’Œæ–‡åŒ–īŧŒå°†å¸Ž', '“姐姐īŧŒäŊ æƒŗåŽģ中å›Ŋ吗īŧŸ"â€œéžå¸¸æƒŗīŧæˆ‘æƒŗ', '肞ᭉéĸ†åŸŸįš„å‘åą•īŧŒâ€œä¸­å›Ŋåœ¨į§‘į ”į­‰æ–šéĸįš„åŽžåŠ›', '厄įĢ‹į‰šé‡ŒäēšéĢ˜į­‰æ•™č‚˛å§”å‘˜äŧšä¸ģäģģåŠŠį†č¨', '劊厄中两å›Ŋäēēæ°‘æ›´åĨŊåœ°į†č§ŖåŊŧæ­¤īŧŒåŠŠåŠ›åŒæ–š', 'åŽģįœ‹æ•…åŽĢ、įˆŦé•ŋ城。"å°¤æ–¯æ‹‰įš„å­Ļį”Ÿä¸­æœ‰ä¸€å¯š', '与æ—Ĩäŋąåĸžã€‚在中å›Ŋå­Ļäš įš„įģåŽ†čŽŠæˆ‘įœ‹åˆ°æ›´åšŋ', 'éŠŦį‘žčĄ¨į¤ēīŧšâ€œæ¯å𴿈‘äģŦéƒŊäŧšįģ„įģ‡å­Ļį”Ÿåˆ°ä¸­å›ŊčŽŋ', 'äē¤åž€īŧŒæ­åģēå‹č°ŠæĄĨæĸã€‚"', 'čƒŊæ­Œå–„čˆžįš„å§åĻšīŧŒå§å§éœ˛å¨…äģŠåš´15垁īŧŒåĻšåĻš', 'é˜”įš„ä¸–į•ŒīŧŒäģŽä¸­å—į›ŠåŒĒæĩ…。”', '问å­Ļäš īŧŒį›Žå‰æœ‰čļ…čŋ‡5000名厄įĢ‹į‰šé‡Œäēšå­Ļį”Ÿ', '厄įĢ‹į‰šé‡Œäēšå›ŊåŽļåšį‰Šéφéφé•ŋåĄ”å‰ä¸ÂˇåŠĒ', 'čŽ‰å¨…14垁īŧŒä¸¤äēēéƒŊåˇ˛åœ¨åŽ„į‰šå­”é™ĸå­Ļ䚠多嚴īŧŒ', '23å˛įš„čŽ‰čŋĒäēšÂˇåŸƒæ–¯č’‚æŗ•č¯ēæ–¯åˇ˛åœ¨åŽ„į‰š', '在中å›Ŋį•™å­Ļ。å­Ļ䚠中å›Ŋįš„æ•™č‚˛įģénjīŧŒæœ‰åŠŠäēŽ', 'é‡Œčžžå§†Âˇäŧ˜į´ įĻæ›žå¤šæŦĄčŽŋ问中å›ŊīŧŒå¯šä¸­åŽæ–‡æ˜Ž', 'ä¸­æ–‡č¯´åž—æ ŧ外æĩåˆŠã€‚', '孔é™ĸå­Ļäš 3åš´īŧŒåœ¨ä¸­å›ŊäšĻæŗ•ã€ä¸­å›Ŋį”ģį­‰æ–šéĸ襨', '提升厄įĢ‹į‰šé‡Œäēšįš„æ•™č‚˛æ°´åšŗã€‚"', 'įš„äŧ æ‰ŋä¸Žåˆ›æ–°ã€įŽ°äģŖåŒ–åšį‰ŠéĻ†įš„åģēčŽžä¸Žå‘åą•', 'éœ˛å¨…å¯ščŽ°č€…č¯´īŧšâ€œčŋ™äē›åš´æĨīŧŒæ€€į€å¯šä¸­æ–‡', 'įŽ°ååˆ†äŧ˜į§€īŧŒåœ¨2024嚴厄įĢ‹į‰šé‡Œäēščĩ›åŒēįš„', 'å°čąĄæˇąåˆģ。“中å›Ŋåšį‰ŠéĻ†ä¸äģ…æœ‰čޏ多äŋå­˜åތåĨŊ', 'â€œå…ąåŒå‘ä¸–į•Œåą•į¤ē非', '和中å›Ŋæ–‡åŒ–įš„įƒ­įˆąīŧŒæˆ‘äģŦ姐åĻšäŋŠå§‹įģˆį›¸äē’éŧ“', 'â€œæą‰č¯­æĄĨ"比čĩ›ä¸­čŽˇåž—ä¸€į­‰åĨ–ã€‚čŽ‰čŋĒäēšč¯´īŧšâ€œå­Ļ', 'įš„æ–‡į‰ŠīŧŒčŋ˜å……分čŋį”¨å…ˆčŋ›į§‘技手æŽĩčŋ›čĄŒåą•į¤ēīŧŒ', 'åŠąīŧŒä¸€čĩˇå­Ļ䚠。我äģŦįš„ä¸­æ–‡ä¸€å¤Šæ¯”ä¸€å¤ŠåĨŊīŧŒčŋ˜', '䚠中å›ŊäšĻæŗ•čŽŠæˆ‘įš„å†…åŋƒå˜åž—厉厁和įē¯į˛šã€‚我', 'æ´˛å’Œä皿´˛įš„įŋįƒ‚æ–‡æ˜Žâ€', '帎劊äēēäģŦ更åĨŊį†č§Ŗä¸­åŽæ–‡æ˜Žã€‚"åĄ”å‰ä¸č¯´īŧŒâ€œåŽ„', 'å­Ļäŧšäē†ä¸­æ–‡æ­Œå’Œä¸­å›Ŋčˆžã€‚æˆ‘äģŦ一厚čρ到䏭å›Ŋ', '䚟喜æŦĸ中å›Ŋįš„æœéĨ°īŧŒå¸Œæœ›æœĒæĨčƒŊåŽģ中å›Ŋå­Ļäš īŧŒ', 'įĢ‹į‰šé‡Œäēšä¸Žä¸­å›ŊéƒŊæ‹Ĩæœ‰æ‚ äš…įš„æ–‡æ˜ŽīŧŒå§‹įģˆį›¸', 'åŽģ。å­ĻåĨŊ中文īŧŒæˆ‘äģŦįš„æœĒæĨ不是æĸĻīŧâ€', '把中å›Ŋä¸åŒæ°‘æ—å…ƒį´ čžå…ĨæœčŖ…čŽžčŽĄä¸­īŧŒåˆ›äŊœ', 'äģŽé˜ŋ斯éŠŦ拉å‡ē发īŧŒæ˛ŋį€čœŋčœ“æ›˛æŠ˜įš„į›˜åąą', 'äē’į†č§Ŗã€į›¸äē’尊重。我希望æœĒæĨ与中å›ŊåŒčĄŒ', 'æŽåŽ„į‰šå­”é™ĸ中斚é™ĸé•ŋéģ„é¸ŖéŖžäģ‹įģīŧŒčŋ™æ‰€', 'å‡ēæ›´å¤šį˛žįžŽäŊœå“īŧŒä🿊ŠåŽ„į‰šæ–‡åŒ–åˆ†äēĢį왿›´å¤š', 'å…Ŧčˇ¯ä¸€čˇ¯å‘ä¸œå¯ģæ‰žä¸čˇ¯å°čŋšã€‚銹čŊĻ两ä¸Ē小', '加åŧē合äŊœīŧŒå…ąåŒå‘ä¸–į•Œåą•į¤ēéžæ´˛å’Œä皿´˛įš„įŋ', '孔é™ĸ成įĢ‹äēŽ2013åš´3月īŧŒį”ąč´ĩåˇžč´ĸįģå¤§å­Ļ和', 'įš„ä¸­å›Ŋ朋友。”', 'æ—ļīŧŒčް者æĨ到äŊäēŽåŽ„įĢ‹į‰šé‡Œä皿¸¯åŖåŸŽå¸‚éŠŦ萨', 'įƒ‚æ–‡æ˜Žã€‚â€'], '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],
        ...,
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'肞ᭉéĸ†åŸŸįš„å‘åą•īŧŒâ€œä¸­å›Ŋåœ¨į§‘į ”į­‰æ–šéĸįš„åŽžåŠ›', '厄įĢ‹į‰šé‡ŒäēšéĢ˜į­‰æ•™č‚˛å§”å‘˜äŧšä¸ģäģģåŠŠį†č¨', '劊厄中两å›Ŋäēēæ°‘æ›´åĨŊåœ°į†č§ŖåŊŧæ­¤īŧŒåŠŠåŠ›åŒæ–š', 'åŽģįœ‹æ•…åŽĢ、įˆŦé•ŋ城。"å°¤æ–¯æ‹‰įš„å­Ļį”Ÿä¸­æœ‰ä¸€å¯š', '与æ—Ĩäŋąåĸžã€‚在中å›Ŋå­Ļäš įš„įģåŽ†čŽŠæˆ‘įœ‹åˆ°æ›´åšŋ', 'éŠŦį‘žčĄ¨į¤ēīŧšâ€œæ¯å𴿈‘äģŦéƒŊäŧšįģ„įģ‡å­Ļį”Ÿåˆ°ä¸­å›ŊčŽŋ', 'äē¤åž€,搭åģēå‹č°ŠæĄĨæĸã€‚"', 'čƒŊæ­Œå–„čˆžįš„å§åĻš,å§å§éœ˛å¨…äģŠåš´15垁īŧŒåĻšåĻš', 'é˜”įš„ä¸–į•ŒīŧŒäģŽä¸­å—į›ŠåŒĒæĩ…。', '问å­Ļäš īŧŒį›Žå‰æœ‰čļ…čŋ‡5000名厄įĢ‹į‰šé‡Œäēšå­Ļį”Ÿ', '厄įĢ‹į‰šé‡Œäēšå›ŊåŽļåšį‰Šéφéφé•ŋåĄ”å‰ä¸ÂˇåŠĒ', 'čŽ‰å¨…14垁īŧŒä¸¤äēēéƒŊåˇ˛åœ¨åŽ„į‰šå­”é™ĸå­Ļ䚠多嚴īŧŒ', '23å˛įš„čŽ‰čŋĒäēšÂˇåŸƒæ–¯č’‚æŗ•č¯ēæ–¯åˇ˛åœ¨åŽ„į‰š', '在中å›Ŋį•™å­Ļ。å­Ļ䚠中å›Ŋįš„æ•™č‚˛įģénj,有劊äēŽ', 'é‡Œčžžå§†Âˇäŧ˜į´ įĻæ›žå¤šæŦĄčŽŋ问中å›ŊīŧŒå¯šä¸­åŽæ–‡æ˜Ž', 'ä¸­æ–‡č¯´åž—æ ŧ外æĩåˆŠã€‚', '孔é™ĸå­Ļäš 3åš´īŧŒåœ¨ä¸­å›ŊäšĻæŗ•ã€ä¸­å›Ŋį”ģį­‰æ–šéĸ襨', '提升厄įĢ‹į‰šé‡Œäēšįš„æ•™č‚˛æ°´åšŗã€‚”', 'įš„äŧ æ‰ŋä¸Žåˆ›æ–°ã€įŽ°äģŖåŒ–åšį‰ŠéĻ†įš„åģēčŽžä¸Žå‘åą•', 'éœ˛å¨…å¯ščŽ°č€…č¯´īŧšâ€œčŋ™äē›åš´æĨ,æ€€į€å¯šä¸­æ–‡', 'įŽ°ååˆ†äŧ˜į§€īŧŒåœ¨2024嚴厄įĢ‹į‰šé‡Œäēščĩ›åŒēįš„', 'â€œå…ąåŒå‘ä¸–į•Œåą•į¤ē非', 'å°čąĄæˇąåˆģ。“中å›Ŋåšį‰ŠéĻ†ä¸äģ…æœ‰čޏ多äŋå­˜åތåĨŊ', '和中å›Ŋæ–‡åŒ–įš„įƒ­įˆą,我äģŦ姐åĻšäŋŠå§‹įģˆį›¸äē’éŧ“', 'â€œæą‰č¯­æĄĨ"比čĩ›ä¸­čŽˇåž—ä¸€į­‰åĨ–ã€‚čŽ‰čŋĒäēšč¯´īŧšâ€œå­Ļ', 'įš„æ–‡į‰Š,čŋ˜å……分čŋį”¨å…ˆčŋ›į§‘技手æŽĩčŋ›čĄŒåą•į¤ēīŧŒ', 'åŠą,一čĩˇå­Ļ䚠。我äģŦįš„ä¸­æ–‡ä¸€å¤Šæ¯”ä¸€å¤ŠåĨŊ,čŋ˜', '䚠中å›ŊäšĻæŗ•čŽŠæˆ‘įš„å†…åŋƒå˜åž—厉厁和įē¯į˛šã€‚我', 'æ´˛å’Œä皿´˛įš„įŋįƒ‚æ–‡æ˜Žâ€', '帎劊äēēäģŦ更åĨŊį†č§Ŗä¸­åŽæ–‡æ˜Žã€‚"åĄ”å‰ä¸č¯´īŧŒåŽ„', 'å­Ļäŧšäē†ä¸­æ–‡æ­Œå’Œä¸­å›Ŋčˆžã€‚æˆ‘äģŦ一厚čρ到䏭å›Ŋ', '䚟喜æŦĸ中å›Ŋįš„æœéĨ°,希望æœĒæĨčƒŊåŽģ中å›Ŋå­Ļäš īŧŒ', 'įĢ‹į‰šé‡Œäēšä¸Žä¸­å›ŊéƒŊæ‹Ĩæœ‰æ‚ äš…įš„æ–‡æ˜Ž,始įģˆį›¸', 'åŽģ。å­ĻåĨŊ中文,我äģŦįš„æœĒæĨ不是æĸĻ!"', '把中å›Ŋä¸åŒæ°‘æ—å…ƒį´ čžå…ĨæœčŖ…čŽžčŽĄä¸­īŧŒåˆ›äŊœ', 'äģŽé˜ŋ斯éŠŦ拉å‡ē发,æ˛ŋį€čœŋčœ’æ›˛æŠ˜įš„į›˜åąą', 'äē’į†č§Ŗã€į›¸äē’尊重。我希望æœĒæĨ与中å›ŊåŒčĄŒ', 'æŽåŽ„į‰šå­”é™ĸ中斚é™ĸé•ŋéģ„é¸ŖéŖžäģ‹įģ,čŋ™æ‰€', 'å‡ēæ›´å¤šį˛žįžŽäŊœå“īŧŒä🿊ŠåŽ„į‰šæ–‡åŒ–åˆ†äēĢį왿›´å¤š', 'å…Ŧčˇ¯ä¸€čˇ¯å‘ä¸œå¯ģæ‰žä¸čˇ¯å°čŋšã€‚銹čŊĻ两ä¸Ē小', '加åŧē合äŊœ,å…ąåŒå‘ä¸–į•Œåą•į¤ēéžæ´˛å’Œä皿´˛įš„įŋ', '孔é™ĸ成įĢ‹äēŽ2013åš´3月īŧŒį”ąč´ĩåˇžč´ĸįģå¤§å­Ļ和', 'įš„ä¸­å›Ŋ朋友。”', 'æ—ļ,记者æĨ到äŊäēŽåŽ„įĢ‹į‰šé‡Œä皿¸¯åŖåŸŽå¸‚éŠŦ萨', 'įƒ‚æ–‡æ˜Žã€‚â€', '谈čĩˇåœ¨ä¸­å›Ŋæą‚å­Ļįš„įģåކ,įēĻįŋ°å¨œčްåŋ†įŠš', '新īŧšâ€œä¸­å›Ŋįš„å‘åą•åœ¨åŊ“äģŠä¸–į•Œæ˜¯į‹Ŧ一无äēŒįš„。', 'æ˛ŋį€ä¸­å›Ŋį‰šč‰˛į¤žäŧšä¸ģäš‰é“čˇ¯åšåŽšå‰čĄŒīŧŒä¸­å›Ŋ', '创造äē†å‘åą•åĨ‡čŋš,čŋ™ä¸€åˆ‡éƒŊįĻģ不åŧ€ä¸­å›Ŋå…ąäē§å…š', 'įš„éĸ†å¯ŧ。中å›Ŋįš„å‘åą•įģénjå€ŧåž—čŽ¸å¤šå›ŊåŽļå­Ļäš ', '借鉴īŧŒâ€', 'æ­Ŗåœ¨čĨŋ南大å­Ļå­Ļäš įš„åŽ„įĢ‹į‰šé‡ŒäēšåšåŖĢį”Ÿ', 'įŠ†åĸį›–åĄ”ÂˇæŗŊįŠ†äŧŠå¯šä¸­å›Ŋæ€€æœ‰æˇąåŽšæ„Ÿæƒ…ã€‚8', '嚴前īŧŒåœ¨åŒ—äēŦ又范大å­ĻčŽˇåž—įĄ•åŖĢå­ĻäŊåŽīŧŒįІåĸ', 'į›–åĄ”åœ¨į¤žäē¤åĒ’äŊ“上写下čŋ™æ ˇä¸€æŽĩč¯īŧšâ€œčŋ™æ˜¯æˆ‘', 'äēēį”Ÿįš„é‡čρ䏀æ­ĨīŧŒč‡Ē此我æ‹Ĩ有äē†ä¸€åŒåšå›ēįš„', '鞋子.čĩ‹ä爿ˆ‘įŠŋčļŠč†æŖ˜įš„力量。”', 'â€œé˛œčŠąæ›žå‘Šč¯‰æˆ‘äŊ æ€Žæ ˇčĩ°čŋ‡īŧŒå¤§åœ°įŸĨ道äŊ ', 'åŋƒä¸­įš„æ¯ä¸€ä¸Ē角čŊ"厄įĢ‹į‰šé‡Œäēšé˜ŋ斯éŠŦ拉', '大å­Ļįģŧ合æĨŧäēŒåą‚īŧŒä¸€é˜ĩäŧ˜įžŽįš„æ­ŒåŖ°åœ¨čĩ°åģŠé‡Œå›ž', '响。åžĒį€į†Ÿæ‚‰įš„æ—‹åž‹čŊģčŊģæŽ¨åŧ€ä¸€é—´æ•™åŽ¤įš„é—¨īŧŒ', 'å­Ļį”ŸäģŦæ­ŖčˇŸį€č€å¸ˆå­Ļå”ąä¸­æ–‡æ­Œæ›˛ã€ŠåŒä¸€éĻ–æ­Œã€‹ã€‚', 'čŋ™æ˜¯åŽ„į‰šå­”é™ĸé˜ŋ斯éŠŦ拉大å­Ļ教å­Ļį‚šįš„ä¸€', 'čŠ‚ä¸­æ–‡æ­Œæ›˛č¯žã€‚ä¸ēäē†čŽŠå­Ļį”ŸäģŦ更åĨŊåœ°į†č§Ŗæ­Œ', 'č¯å¤§æ„īŧŒč€å¸ˆå°¤æ–¯æ‹‰ÂˇįІįŊ•éģ˜åžˇč¨å°”Âˇäž¯čĩ›å› é€', '字įŋģč¯‘å’Œč§Ŗé‡Šæ­Œč¯ã€‚éšį€äŧ´åĨåŖ°å“čĩˇīŧŒå­Ļį”ŸäģŦ', 'čžšå”ąčžšéšį€čŠ‚æ‹æ‘‡åŠ¨čēĢäŊ“īŧŒįްåœēæ°”æ°›įƒ­įƒˆã€‚'], '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´Ĩ君和įŧ˜å•†č´¸æœ‰é™å…Ŧ司', '发įĨ¨ä¸“ᔍįĢ ', 'å—įšį‰Š', '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], [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]])], 'rec_boxes': array([], dtype=float64)}]}}

paddlex --pipeline doc_preprocessor \
        --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/doc_test_rotated.jpg \
        --use_doc_orientation_classify True \
        --use_doc_unwarping True \
        --save_path ./output \
        --device gpu:0
What's the result
{'res': {'input_path': 'doc_test_rotated.jpg', 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': True}, 'angle': 180}}

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 semantic_segmentation \
        --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png \
        --target_size -1 \
        --save_path ./output \
        --device gpu:0
What's the result
{'res': {'input_path': 'makassaridn-road_demo.png', 'page_index': None, 'pred': '...'}}

paddlex --pipeline image_multilabel_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': 'test_imgs/general_image_classification_001.jpg', 'page_index': None, 'class_ids': array([21]), 'scores': array([0.99962]), 'label_names': ['bear']}}

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 anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --device gpu:0  --save_path ./output
What's the result
{'input_path': 'uad_grid.png', 'pred': '...'}

paddlex --pipeline 3d_bev_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/data/nuscenes_demo.tar --device gpu:0
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],
        [353.9377    ,  82.47209   ,   0.7778817 ],
        [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],
        [297.09854   ,  80.97825   ,   0.7341483 ],
        [292.39313   ,  81.7721    ,   0.74603605],
        [302.3231    ,  81.528275  ,   0.7586238 ],
        [290.6292    ,  83.26544   ,   0.7514231 ],
        [313.32928   ,  98.40588   ,   0.83778954],
        [286.23532   , 101.702194  ,   0.91927457],
        [321.99515   , 120.05991   ,   0.90197486],
        [282.39294   , 122.16547   ,   0.74502975],
        [327.164     , 141.25995   ,   0.8172762 ],
        [279.1632    , 133.16023   ,   0.59161717],
        [311.02557   , 142.31526   ,   0.82111686],
        [294.72357   , 143.42067   ,   0.71559554],
        [313.98828   , 174.17151   ,   0.7495116 ],
        [291.76605   , 174.39961   ,   0.7645517 ],
        [321.4924    , 202.4499    ,   0.7817023 ],
        [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],
        [3.3636591e+02, 2.0724442e+02, 7.7529407e-01],
        [3.3486487e+02, 2.0653752e+02, 8.3719862e-01],
        [3.4387805e+02, 2.0405179e+02, 7.9793924e-01],
        [3.4104437e+02, 2.0354083e+02, 6.7090714e-01],
        [3.5167136e+02, 2.1253050e+02, 5.9533423e-01],
        [3.5493774e+02, 2.1316977e+02, 5.1632988e-01],
        [3.2814764e+02, 2.1943013e+02, 5.3697169e-01],
        [3.2577945e+02, 2.2027420e+02, 1.6555195e-01],
        [3.1541614e+02, 2.2199020e+02, 5.2568728e-01],
        [3.1139435e+02, 2.2925937e+02, 2.2075935e-01],
        [3.8441351e+02, 2.4341478e+02, 6.4083064e-01],
        [3.8714008e+02, 2.4532764e+02, 6.4894527e-01],
        [3.5143246e+02, 2.5615021e+02, 7.7424920e-01],
        [3.7133820e+02, 2.7552402e+02, 5.8704698e-01],
        [3.6274625e+02, 2.8303183e+02, 6.1670756e-01],
        [4.0358893e+02, 2.9351334e+02, 4.2383862e-01]], dtype=float32), 'kpt_score': 0.5969399}, {'coordinate': [238.98825, 182.67476, 372.81628, 307.61395], 'det_score': 0.914400041103363, 'keypoints': array([[282.9012    , 208.31485   ,   0.6685285 ],
        [282.95908   , 204.36131   ,   0.66104335],
        [280.90683   , 204.54018   ,   0.7281709 ],
        [274.7831    , 204.04141   ,   0.54541856],
        [270.97324   , 203.04889   ,   0.73486483],
        [269.43472   , 217.63014   ,   0.6707946 ],
        [256.871     , 216.546     ,   0.89603853],
        [277.03226   , 238.2196    ,   0.4412233 ],
        [262.29578   , 241.33434   ,   0.791063  ],
        [292.90753   , 251.69914   ,   0.4993091 ],
        [285.6907    , 252.71925   ,   0.7215052 ],
        [279.36578   , 261.8949    ,   0.6626504 ],
        [270.43402   , 268.07068   ,   0.80625033],
        [311.96924   , 261.36716   ,   0.67315185],
        [309.32407   , 262.97354   ,   0.72746485],
        [345.22446   , 285.02255   ,   0.60142016],
        [334.69235   , 291.57108   ,   0.7674925 ]], dtype=float32), 'kpt_score': 0.6821406}, {'coordinate': [66.23172, 93.531204, 124.48463, 217.99655], 'det_score': 0.9086756110191345, 'keypoints': array([[ 91.04524   , 108.79487   ,   0.8234256 ],
        [ 92.67917   , 106.63517   ,   0.79848343],
        [ 88.41122   , 106.8017    ,   0.8122996 ],
        [ 95.353096  , 106.96488   ,   0.85210425],
        [ 84.35098   , 107.85205   ,   0.971826  ],
        [ 99.92103   , 119.87272   ,   0.853371  ],
        [ 79.69138   , 121.08684   ,   0.8854925 ],
        [103.019554  , 135.00996   ,   0.73513967],
        [ 72.38997   , 136.8782    ,   0.7727014 ],
        [104.561935  , 146.01869   ,   0.8377464 ],
        [ 72.70636   , 151.44576   ,   0.67577386],
        [ 98.69484   , 151.30742   ,   0.8381225 ],
        [ 85.946     , 152.07056   ,   0.7904873 ],
        [106.64397   , 175.77159   ,   0.8179414 ],
        [ 84.6963    , 178.4353    ,   0.8094256 ],
        [111.30463   , 201.2306    ,   0.74394226],
        [ 80.08708   , 204.05814   ,   0.8457697 ]], dtype=float32), 'kpt_score': 0.8155325}, {'coordinate': [160.1294, 78.35935, 212.01868, 153.2241], 'det_score': 0.8295672535896301, 'keypoints': array([[1.89240387e+02, 9.08055573e+01, 7.36447990e-01],
        [1.91318649e+02, 8.84640198e+01, 7.86390483e-01],
        [1.87943207e+02, 8.88532104e+01, 8.23230743e-01],
        [1.95832245e+02, 8.76751480e+01, 6.76276207e-01],
        [1.86741409e+02, 8.96744080e+01, 7.87400603e-01],
        [2.04019852e+02, 9.83068924e+01, 7.34004617e-01],
        [1.85355087e+02, 9.81262970e+01, 6.23330474e-01],
        [2.01501678e+02, 1.12709480e+02, 2.93740422e-01],
        [1.80446320e+02, 1.11967369e+02, 5.50001860e-01],
        [1.95137482e+02, 9.73322601e+01, 4.24658984e-01],
        [1.74287552e+02, 1.21760696e+02, 3.51236403e-01],
        [1.97997589e+02, 1.24219963e+02, 3.45360219e-01],
        [1.83250824e+02, 1.22610085e+02, 4.38733459e-01],
        [1.96233871e+02, 1.22864418e+02, 5.36903977e-01],
        [1.66795364e+02, 1.25634903e+02, 3.78726840e-01],
        [1.80727753e+02, 1.42604034e+02, 2.78717279e-01],
        [1.75880920e+02, 1.41181213e+02, 1.70833692e-01]], dtype=float32), 'kpt_score': 0.5256467}, {'coordinate': [52.482475, 59.36664, 96.47121, 135.45993], 'det_score': 0.7726763486862183, 'keypoints': array([[ 73.98227   ,  74.01257   ,   0.71940714],
        [ 75.44208   ,  71.73432   ,   0.6955297 ],
        [ 72.20365   ,  71.9637    ,   0.6138198 ],
        [ 77.7856    ,  71.665825  ,   0.73568064],
        [ 69.342285  ,  72.25549   ,   0.6311799 ],
        [ 83.1019    ,  77.65522   ,   0.7037722 ],
        [ 64.89729   ,  78.846565  ,   0.56623787],
        [ 85.16928   ,  88.88764   ,   0.5665537 ],
        [ 61.65655   ,  89.35312   ,   0.4463089 ],
        [ 80.01986   ,  91.51777   ,   0.30305162],
        [ 70.90767   ,  89.90153   ,   0.48063472],
        [ 78.70658   ,  97.33488   ,   0.39359188],
        [ 68.3219    ,  97.67902   ,   0.41903985],
        [ 80.69448   , 109.193985  ,   0.14496553],
        [ 65.57641   , 105.08109   ,   0.27744702],
        [ 79.44859   , 122.69015   ,   0.17710638],
        [ 64.03736   , 120.170425  ,   0.46565098]], dtype=float32), 'kpt_score': 0.4905869}, {'coordinate': [7.081953, 80.3705, 46.81927, 161.72012], 'det_score': 0.6587498784065247, 'keypoints': array([[ 29.51531   ,  91.49908   ,   0.75517464],
        [ 31.225754  ,  89.82169   ,   0.7765606 ],
        [ 27.376017  ,  89.71614   ,   0.80448   ],
        [ 33.515877  ,  90.82257   ,   0.7093001 ],
        [ 23.521307  ,  90.84212   ,   0.777707  ],
        [ 37.539314  , 101.381516  ,   0.6913692 ],
        [ 18.340288  , 102.41546   ,   0.7203535 ],
        [ 39.826218  , 113.37301   ,   0.5913918 ],
        [ 16.857304  , 115.10882   ,   0.5492331 ],
        [ 28.826103  , 121.861855  ,   0.39205936],
        [ 22.47133   , 120.69003   ,   0.6120081 ],
        [ 34.177963  , 126.15756   ,   0.5601723 ],
        [ 21.39047   , 125.30078   ,   0.5064371 ],
        [ 27.961575  , 133.33154   ,   0.54826814],
        [ 22.303364  , 129.8608    ,   0.2293001 ],
        [ 31.242027  , 153.047     ,   0.36292207],
        [ 21.80127   , 153.78947   ,   0.30531448]], dtype=float32), 'kpt_score': 0.58188534}, {'coordinate': [126.131096, 30.263107, 168.5759, 134.09885], 'det_score': 0.6441988348960876, 'keypoints': array([[149.89236   ,  43.87846   ,   0.75441885],
        [151.99484   ,  41.95912   ,   0.82070917],
        [148.18002   ,  41.775055  ,   0.8453321 ],
        [155.37967   ,  42.06968   ,   0.83349544],
        [145.38167   ,  41.69159   ,   0.8233239 ],
        [159.26329   ,  53.284737  ,   0.86246717],
        [142.35178   ,  51.206886  ,   0.6940705 ],
        [157.3975    ,  71.31917   ,   0.7624757 ],
        [136.59795   ,  66.40522   ,   0.55612797],
        [142.90988   ,  78.28269   ,   0.779243  ],
        [135.43607   ,  73.9765    ,   0.5737738 ],
        [155.7851    ,  82.44225   ,   0.6966109 ],
        [143.4588    ,  80.91763   ,   0.60589534],
        [153.45274   , 102.84818   ,   0.62720954],
        [131.59738   ,  87.54947   ,   0.4976839 ],
        [155.56401   , 125.58888   ,   0.5414401 ],
        [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],
        [1.36285202e+02, 7.16439133e+01, 6.38598502e-01],
        [1.33776855e+02, 7.16437454e+01, 6.36756659e-01],
        [1.37833389e+02, 7.24015121e+01, 4.13749218e-01],
        [1.31340057e+02, 7.30362549e+01, 5.70683837e-01],
        [1.42542435e+02, 8.28875885e+01, 2.30803847e-01],
        [1.29773300e+02, 8.52729874e+01, 4.94463116e-01],
        [1.41332916e+02, 9.43963928e+01, 9.36751068e-02],
        [1.28858521e+02, 9.95147858e+01, 2.72373617e-01],
        [1.44981277e+02, 7.83604965e+01, 8.68032947e-02],
        [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(é•ŋčŖ¤)', 'Age18-60(嚴鞄在18-60å˛äš‹é—´)', 'LongCoat(é•ŋ外åĨ—)', 'Side(äž§éĸ)'], '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(é•ŋčŖ¤)', 'LongCoat(é•ŋ外åĨ—)', 'Front(éĸ朝前)', 'Age18-60(嚴鞄在18-60å˛äš‹é—´)'], '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(é•ŋčŖ¤)', 'LongCoat(é•ŋ外åĨ—)', 'Age18-60(嚴鞄在18-60å˛äš‹é—´)', 'Side(äž§éĸ)'], '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(įēĸ色)', '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(æŖ•č‰˛)'], '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]}
paddlex --pipeline ts_anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0 --save_path ./output
What's the result
    {'input_path': 'ts_ad.csv', 'anomaly':            label
    timestamp
    220226         0
    220227         0
    220228         0
    220229         0
    220230         0
    ...          ...
    220317         1
    220318         1
    220319         1
    220320         1
    220321         0

    [96 rows x 1 columns]}
paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0
What's the result
    {'input_path': 'ts_cls.csv', 'classification':         classid     score
    sample
    0             0  0.617688}

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': 'æˆ‘čŽ¤ä¸ē跑æ­Ĩ最重čĻįš„å°ąæ˜¯į왿ˆ‘å¸ĻæĨäē†čēĢäŊ“åĨåēˇ', 'segments': [{'id': 0, 'seek': 0, 'start': 0.0, 'end': 2.0, 'text': 'æˆ‘čŽ¤ä¸ē跑æ­Ĩ最重čĻįš„å°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': 'į왿ˆ‘å¸ĻæĨäē†čēĢäŊ“åĨåēˇ', '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_classification \
        --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/general_video_classification_001.mp4 \
        --topk 5 \
        --save_path ./output \
        --device gpu:0
What's the result
{'res': {'input_path': 'general_video_classification_001.mp4', 'class_ids': array([  0, 278,  68, 272, 162], dtype=int32), 'scores': [0.91996, 0.07055, 0.00235, 0.00215, 0.00158], 'label_names': ['abseiling', 'rock_climbing', 'climbing_tree', 'riding_mule', 'ice_climbing']}}

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']]]}

📝 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/")
The following steps were executed:

  • 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="object_detection")

output = pipeline.predict("general_object_detection_002.png", threshold=0.5)
for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_json("./output/")
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="instance_segmentation")
output = pipeline.predict(input="general_instance_segmentation_004.png", threshold=0.5)
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="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_name="small_object_detection")
output = pipeline.predict(input="small_object_detection.jpg", threshold=0.5)
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="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="human_keypoint_detection")

output = pipeline.predict("keypoint_detection_001.jpg", det_threshold=0.5)
for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_json("./output/")
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
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="rotated_object_detection")
output = pipeline.predict(input="rotated_object_detection_001.png", threshold=0.5)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/")

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

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="multilingual_speech_recognition")
output = pipeline.predict(input="zh.wav")

for res in output:
    res.print()
    res.save_to_json(save_path="./output/")

Command Line Usage for Video pipelines

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="video_classification")

output = pipeline.predict("general_video_classification_001.mp4", topk=5)
for res in output:
    res.print()
    res.save_to_video(save_path="./output/")
    res.save_to_json(save_path="./output/")
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

🚀 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.

    Tutorial

  • 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.

    Tutorial

  • 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.

    Tutorial

  • 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.

    Tutorial

  • 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.

    Tutorial

  • 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.

    Tutorial

More

đŸ’Ŧ 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.

Comments