Home
๐ 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.
๐ ๏ธ Installation¶
Warning
Please ensure you have a basic Python runtime environment before installing PaddleX (Note: Currently supports Python 3.8 to Python 3.10, with more Python versions being adapted).
Installing PaddlePaddle¶
โ For more PaddlePaddle Wheel versions, please refer to the PaddlePaddle official website.
Installing PaddleX¶
โ For more installation methods, please refer to the PaddleX Installation Guide
๐ป Command Line Usage¶
A single command can quickly experience the production line effect, with a unified command line format as follows:
You only need to specify three parameters:
pipeline: The name of the production lineinput: The local path or URL of the input file to be processed (e.g., an image)device: The GPU number used (for example,gpu:0indicates using the 0th GPU), or you can choose to use CPU (cpu)
OCR-related CLI
paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': '/root/.paddlex/predict_input/general_ocr_002.png',
'dt_polys': [array([[161, 27],
[353, 22],
[354, 69],
[162, 74]], dtype=int16), array([[426, 26],
[657, 21],
[657, 58],
[426, 62]], dtype=int16), array([[702, 18],
[822, 13],
[824, 57],
[704, 62]], dtype=int16), array([[341, 106],
[405, 106],
[405, 128],
[341, 128]], dtype=int16)
...],
'dt_scores': [0.758478200014338, 0.7021546472698513, 0.8536622648391111, 0.8619181462164781, 0.8321051217096188, 0.8868756173427551, 0.7982964727675609, 0.8289939036796322, 0.8289428877522524, 0.8587063317632897, 0.7786755892491615, 0.8502032769081344, 0.8703346500042997, 0.834490931790065, 0.908291103353393, 0.7614978661708064, 0.8325774055997542, 0.7843421347676149, 0.8680889482955594, 0.8788859304537682, 0.8963341277518075, 0.9364654810069546, 0.8092413027028257, 0.8503743089091863, 0.7920740420391101, 0.7592224394793805, 0.7920547400069311, 0.6641757962457888, 0.8650289477605955, 0.8079483304467047, 0.8532207681055275, 0.8913377034754717],
'rec_text': ['็ปๆบ็', 'BOARDING', 'PASS', '่ฑไฝ', 'CLASS', 'ๅบๅท SERIALNO.', 'ๅบงไฝๅท', 'ๆฅๆ DATE', 'SEAT NO', '่ช็ญ FLIGHW', '035', 'MU2379', 'ๅงๅๅฐ', 'FROM', '็ปๆบๅฃ', 'GATE', '็ปๆบๆถ้ดBDT', '็ฎ็ๅฐTO', '็ฆๅท', 'TAIYUAN', 'G11', 'FUZHOU', '่บซไปฝ่ฏๅซIDNO', 'ๅงๅNAME', 'ZHANGQIWEI', ็ฅจๅทTKTNO', 'ๅผ ็ฅบไผ', '็ฅจไปทFARE', 'ETKT7813699238489/1', '็ปๆบๅฃไบ่ตท้ฃๅ10ๅ้ๅ
ณ้ญGATESCLOSE10MINUTESBEFOREDEPARTURETIME'],
'rec_score': [0.9985831379890442, 0.999696917533874512, 0.9985735416412354, 0.9842517971992493, 0.9383274912834167, 0.9943678975105286, 0.9419361352920532, 0.9221674799919128, 0.9555020928382874, 0.9870321154594421, 0.9664073586463928, 0.9988052248954773, 0.9979352355003357, 0.9985110759735107, 0.9943482875823975, 0.9991195797920227, 0.9936401844024658, 0.9974591135978699, 0.9743705987930298, 0.9980487823486328, 0.9874696135520935, 0.9900962710380554, 0.9952947497367859, 0.9950481653213501, 0.989926815032959, 0.9915552139282227, 0.9938777685165405, 0.997239887714386, 0.9963340759277344, 0.9936134815216064, 0.97223961353302]}
paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': '/root/.paddlex/predict_input/table_recognition.jpg', 'layout_result': {'input_path': '/root/.paddlex/predict_input/table_recognition.jpg', 'boxes': [{'cls_id': 3, 'label': 'Table', 'score': 0.6014542579650879, 'coordinate': [0, 21, 551, 118]}]}, 'ocr_result': {'dt_polys': [array([[37., 40.],
[75., 40.],
[75., 60.],
[37., 60.]], dtype=float32), array([[123., 37.],
[200., 37.],
[200., 59.],
[123., 59.]], dtype=float32), array([[227., 37.],
[391., 37.],
[391., 58.],
[227., 58.]], dtype=float32), array([[416., 36.],
[535., 38.],
[535., 61.],
[415., 58.]], dtype=float32), array([[35., 73.],
[78., 73.],
[78., 92.],
[35., 92.]], dtype=float32), array([[287., 73.],
[328., 73.],
[328., 92.],
[287., 92.]], dtype=float32), array([[453., 72.],
[495., 72.],
[495., 94.],
[453., 94.]], dtype=float32), array([[ 17., 103.],
[ 94., 103.],
[ 94., 118.],
[ 17., 118.]], dtype=float32), array([[145., 104.],
[178., 104.],
[178., 118.],
[145., 118.]], dtype=float32), array([[277., 104.],
[337., 102.],
[338., 118.],
[278., 118.]], dtype=float32), array([[446., 102.],
[504., 104.],
[503., 118.],
[445., 118.]], dtype=float32)], 'rec_text': ['Dres', '่ฟ็ปญๅทฅไฝ3', 'ๅๅบๆฅๆพๅจ็ฝไธ๏ผๆฒกๆณ', 'ๆฑใๆดๆฑ็ญๅ
ซๅคง', 'Abstr', 'rSrivi', '$709.', 'cludingGiv', '2.72', 'Ingcubic', '$744.78'], 'rec_score': [0.9934158325195312, 0.9990204572677612, 0.9967061877250671, 0.9375461935997009, 0.9947397112846375, 0.9972746968269348, 0.9904290437698364, 0.973427414894104, 0.9983080625534058, 0.993423342704773, 0.9964120984077454], 'input_path': 'table_recognition.jpg'}, 'table_result': [{'input_path': 'table_recognition.jpg', 'layout_bbox': [0, 21, 551, 118], 'bbox': array([[ 4.395736 , 25.238262 , 113.31014 , 25.316246 , 115.454315 ,
71.8867 , 3.7177477, 71.7937 ],
[110.727455 , 25.94007 , 210.07187 , 26.028755 , 209.66394 ,
65.96484 , 109.59861 , 66.09809 ],
[214.45381 , 26.027939 , 407.95276 , 26.112846 , 409.6684 ,
66.91336 , 215.27292 , 67.002014 ],
[402.81863 , 26.123789 , 549.03656 , 26.231564 , 549.19995 ,
66.88339 , 404.48068 , 66.74034 ],
[ 2.4458022, 64.68588 , 102.7665 , 65.10228 , 105.79447 ,
96.051254 , 2.5367072, 95.35514 ],
[108.85877 , 65.80549 , 211.70216 , 66.02091 , 210.79245 ,
94.75581 , 107.59308 , 94.42664 ],
[217.05621 , 64.98496 , 407.76328 , 65.133484 , 406.8436 ,
96.00133 , 214.67896 , 95.87226 ],
[401.73572 , 64.60494 , 547.9967 , 64.73921 , 548.19135 ,
96.09901 , 402.26733 , 95.95529 ],
[ 2.4882016, 93.589554 , 107.01325 , 93.67592 , 107.8446 ,
120.13259 , 2.508764 , 119.85027 ],
[110.773125 , 93.98633 , 213.354 , 94.08046 , 212.46033 ,
120.80207 , 109.29008 , 120.613045 ],
[216.08781 , 94.19984 , 405.843 , 94.28341 , 405.9974 ,
121.33152 , 215.10301 , 121.299034 ],
[403.92212 , 94.44883 , 548.30963 , 94.54982 , 548.4949 ,
122.610176 , 404.53433 , 122.49881 ]], dtype=float32), 'img_idx': 0, 'html': '<html><body><table><tr><td>Dres</td><td>่ฟ็ปญๅทฅไฝ3</td><td>ๅๅบๆฅๆพๅจ็ฝไธ๏ผๆฒกๆณ</td><td>ๆฑใๆดๆฑ็ญๅ
ซๅคง</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>'}]}
paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': PosixPath('/root/.paddlex/temp/tmp5jmloefs.png'), 'parsing_result': [{'input_path': PosixPath('/root/.paddlex/temp/tmpshsq8_w0.png'), 'layout_bbox': [51.46833, 74.22329, 542.4082, 232.77504], 'image': {'img': array([[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[213, 221, 238],
[217, 223, 240],
[233, 234, 241]],
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]]], dtype=uint8), 'image_text': ''}, 'layout': 'single'}, {'input_path': PosixPath('/root/.paddlex/temp/tmpcd2q9uyu.png'), 'layout_bbox': [47.68295, 243.08054, 546.28253, 295.71045], 'figure_title': 'Overview of RT-DETR, We feed th', 'layout': 'single'}, {'input_path': PosixPath('/root/.paddlex/temp/tmpr_iqa8b3.png'), 'layout_bbox': [58.416977, 304.1531, 275.9134, 400.07513], 'image': {'img': array([[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]]], dtype=uint8), 'image_text': ''}, 'layout': 'left'}, {'input_path': PosixPath('/root/.paddlex/temp/tmphpblxl3p.png'), 'layout_bbox': [100.62961, 405.97458, 234.79774, 414.77414], 'figure_title': 'Figure 5. The fusion block in CCFF.', 'layout': 'left'}, {'input_path': PosixPath('/root/.paddlex/temp/tmplgnczrsf.png'), 'layout_bbox': [47.81724, 421.9041, 288.01566, 550.538], 'text': 'D, Ds, not only significantly reduces latency (35% faster),\nRut\nnproves accuracy (0.4% AP higher), CCFF is opti\nased on the cross-scale fusion module, which\nnsisting of convolutional lavers intc\npath.\nThe role of the fusion block is t\n into a new feature, and its\nFigure 5. The f\nblock contains tw\n1 x1\nchannels, /V RepBlock\n. anc\n: two-path outputs are fused by element-wise add. We\ntormulate the calculation ot the hvbrid encoder as:', 'layout': 'left'}, {'input_path': PosixPath('/root/.paddlex/temp/tmpsq0ey9md.png'), 'layout_bbox': [94.60716, 558.703, 288.04193, 600.19434], 'formula': '\\begin{array}{l}{{\\Theta=K=\\mathrm{p.s.sp{\\pm}}\\mathrm{i.s.s.}(\\mathrm{l.s.}(\\mathrm{l.s.}(\\mathrm{l.s.}}),{\\qquad\\mathrm{{a.s.}}\\mathrm{s.}}}\\\\ {{\\tau_{\\mathrm{{s.s.s.s.s.}}(\\mathrm{l.s.},\\mathrm{l.s.},\\mathrm{s.s.}}\\mathrm{s.}\\mathrm{s.}}\\end{array}),}}\\\\ {{\\bar{\\mathrm{e-c.c.s.s.}(\\mathrm{s.},\\mathrm{s.s.},\\ s_{s}}\\mathrm{s.s.},\\tau),}}\\end{array}', 'layout': 'left'}, {'input_path': PosixPath('/root/.paddlex/temp/tmpv30qy0v4.png'), 'layout_bbox': [47.975555, 607.12024, 288.5776, 629.1252], 'text': 'tened feature to the same shape as Ss.\nwhere Re shape represents restoring the shape of the flat-', 'layout': 'left'}, {'input_path': PosixPath('/root/.paddlex/temp/tmp0jejzwwv.png'), 'layout_bbox': [48.383354, 637.581, 245.96404, 648.20496], 'paragraph_title': '4.3. Uncertainty-minimal Query Selection', 'layout': 'left'}, {'input_path': PosixPath('/root/.paddlex/temp/tmpushex416.png'), 'layout_bbox': [47.80134, 656.002, 288.50192, 713.24994], 'text': 'To reduce the difficulty of optimizing object queries in\nDETR, several subsequent works [42, 44, 45] propose query\nselection schemes, which have in common that they use the\nconfidence score to select the top Kโ features from the en-\ncoder to initialize object queries (or just position queries).', 'layout': 'left'}, {'input_path': PosixPath('/root/.paddlex/temp/tmpki7e_6wc.png'), 'layout_bbox': [306.6371, 302.1026, 546.3772, 419.76724], 'text': 'The confidence score represents the likelihood that the fea\nture includes foreground objects. Nevertheless, the \nare required to simultaneously model the category\nojects, both of which determine the quality of the\npertor\ncore of the fes\nBased on the analysis, the current query\n considerable level of uncertainty in the\nresulting in sub-optimal initialization for\nand hindering the performance of the detector.', 'layout': 'right'}, {'input_path': PosixPath('/root/.paddlex/temp/tmppbxrfehp.png'), 'layout_bbox': [306.0642, 422.7347, 546.9216, 539.45734], 'text': 'To address this problem, we propose the uncertainty mini\nmal query selection scheme, which explicitly const\noptim\n the epistemic uncertainty to model the\nfeatures, thereby providing \nhigh-quality\nr the decoder. Specifically,\n the discrepancy between i\nalization P\nand classificat\n.(2\ntunction for the gradie', 'layout': 'right'}, {'input_path': PosixPath('/root/.paddlex/temp/tmp1mgiyd21.png'), 'layout_bbox': [331.52808, 549.32635, 546.5229, 586.15546], 'formula': '\\begin{array}{c c c}{{}}&{{}}&{{\\begin{array}{c}{{i\\langle X\\rangle=({\\bar{Y}}({\\bar{X}})+{\\bar{Z}}({\\bar{X}})\\mid X\\in{\\bar{\\pi}}^{\\prime}}}&{{}}\\\\ {{}}&{{}}&{{}}\\end{array}}}&{{\\emptyset}}\\\\ {{}}&{{}}&{{C(\\bar{X},{\\bar{X}})=C..\\scriptstyle(\\bar{0},{\\bar{Y}})+{\\mathcal{L}}_{{\\mathrm{s}}}({\\bar{X}}),\\ 6)}}&{{}}\\end{array}', 'layout': 'right'}, {'input_path': PosixPath('/root/.paddlex/temp/tmp8t73dpym.png'), 'layout_bbox': [306.44016, 592.8762, 546.84314, 630.60126], 'text': 'where and y denote the prediction and ground truth,\n= (c, b), c and b represent the category and bounding\nbox respectively, X represent the encoder feature.', 'layout': 'right'}, {'input_path': PosixPath('/root/.paddlex/temp/tmpftnxeyjm.png'), 'layout_bbox': [306.15652, 632.3142, 546.2463, 713.19073], 'text': 'Effectiveness analysis. To analyze the effectiveness of the\nuncertainty-minimal query selection, we visualize the clas-\nsificatior\nscores and IoU scores of the selected fe\nCOCO\na 12017, Figure 6. We draw the scatterplo\nt with\ndots\nrepresent the selected features from the model trained\nwith uncertainty-minimal query selection and vanilla query', 'layout': 'right'}]}
paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': '/root/.paddlex/predict_input/general_formula_recognition.png', 'layout_result': {'input_path': '/root/.paddlex/predict_input/general_formula_recognition.png', 'boxes': [{'cls_id': 3, 'label': 'number', 'score': 0.7580855488777161, 'coordinate': [1028.3635, 205.46213, 1038.953, 222.99033]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.8882032632827759, 'coordinate': [272.75305, 204.50894, 433.7473, 226.17996]}, {'cls_id': 2, 'label': 'text', 'score': 0.9685840606689453, 'coordinate': [272.75928, 282.17773, 1041.9316, 374.44687]}, {'cls_id': 2, 'label': 'text', 'score': 0.9559416770935059, 'coordinate': [272.39056, 385.54114, 1044.1521, 443.8598]}, {'cls_id': 2, 'label': 'text', 'score': 0.9610629081726074, 'coordinate': [272.40817, 467.2738, 1045.1033, 563.4855]}, {'cls_id': 7, 'label': 'formula', 'score': 0.8916195034980774, 'coordinate': [503.45743, 594.6236, 1040.6804, 619.73895]}, {'cls_id': 2, 'label': 'text', 'score': 0.973675549030304, 'coordinate': [272.32007, 648.8599, 1040.8702, 775.15686]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9038916230201721, 'coordinate': [554.2307, 803.5825, 1040.4657, 855.3159]}, {'cls_id': 2, 'label': 'text', 'score': 0.9025381803512573, 'coordinate': [272.535, 875.1402, 573.1086, 898.3587]}, {'cls_id': 2, 'label': 'text', 'score': 0.8336610794067383, 'coordinate': [317.48013, 909.60864, 966.8498, 933.7868]}, {'cls_id': 2, 'label': 'text', 'score': 0.8779091238975525, 'coordinate': [19.704018, 653.322, 72.433235, 1215.1992]}, {'cls_id': 2, 'label': 'text', 'score': 0.8832409977912903, 'coordinate': [272.13028, 958.50806, 1039.7928, 1019.476]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9088466167449951, 'coordinate': [517.1226, 1042.3978, 1040.2208, 1095.7457]}, {'cls_id': 2, 'label': 'text', 'score': 0.9587949514389038, 'coordinate': [272.03336, 1112.9269, 1041.0201, 1206.8417]}, {'cls_id': 2, 'label': 'text', 'score': 0.8885666131973267, 'coordinate': [271.7495, 1231.8752, 710.44495, 1255.7981]}, {'cls_id': 7, 'label': 'formula', 'score': 0.8907185196876526, 'coordinate': [581.2295, 1287.4525, 1039.8014, 1312.772]}, {'cls_id': 2, 'label': 'text', 'score': 0.9559596180915833, 'coordinate': [273.1827, 1341.421, 1041.0299, 1401.7255]}, {'cls_id': 2, 'label': 'text', 'score': 0.875311553478241, 'coordinate': [272.8338, 1427.3711, 789.7108, 1451.1359]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9152213931083679, 'coordinate': [524.9582, 1474.8136, 1041.6333, 1530.7142]}, {'cls_id': 2, 'label': 'text', 'score': 0.9584835767745972, 'coordinate': [272.81665, 1549.524, 1042.9962, 1608.7157]}]}, 'ocr_result': {}, 'table_result': [], 'dt_polys': [array([[ 503.45743, 594.6236 ],
[1040.6804 , 594.6236 ],
[1040.6804 , 619.73895],
[ 503.45743, 619.73895]], dtype=float32), array([[ 554.2307, 803.5825],
[1040.4657, 803.5825],
[1040.4657, 855.3159],
[ 554.2307, 855.3159]], dtype=float32), array([[ 517.1226, 1042.3978],
[1040.2208, 1042.3978],
[1040.2208, 1095.7457],
[ 517.1226, 1095.7457]], dtype=float32), array([[ 581.2295, 1287.4525],
[1039.8014, 1287.4525],
[1039.8014, 1312.772 ],
[ 581.2295, 1312.772 ]], dtype=float32), array([[ 524.9582, 1474.8136],
[1041.6333, 1474.8136],
[1041.6333, 1530.7142],
[ 524.9582, 1530.7142]], dtype=float32)], 'rec_formula': ['F({\bf x})=C(F_{1}(x_{1}),\cdot\cdot\cdot,F_{N}(x_{N})).\qquad\qquad\qquad(1)', 'p(\mathbf{x})=c(\mathbf{u})\prod_{i}p(x_{i}).\qquad\qquad\qquad\qquad\qquad\quad\quad~~\quad~~~~~~~~~~~~~~~(2)', 'H_{c}({\bf x})=-\int_{{\bf{u}}}c({\bf{u}})\log c({\bf{u}})d{\bf{u}}.~~~~~~~~~~~~~~~~~~~~~(3)', 'I({\bf x})=-H_{c}({\bf x}).\qquad\qquad\qquad\qquad(4)', 'H({\bf x})=\sum_{i}H(x_{i})+H_{c}({\bf x}).\eqno\qquad\qquad\qquad(5)']}
paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': PosixPath('/root/.paddlex/temp/tmpa8eqnpus.png'), 'layout_result': {'input_path': PosixPath('/root/.paddlex/temp/tmpa8eqnpus.png'), 'boxes': [{'cls_id': 2, 'label': 'seal', 'score': 0.9813321828842163, 'coordinate': [0, 5.1820183, 639.59314, 637.7533]}]}, 'ocr_result': {'dt_polys': [array([[166, 468],
[206, 503],
[249, 523],
[312, 535],
[364, 529],
[390, 521],
[428, 505],
[465, 476],
[468, 474],
[473, 474],
[476, 475],
[478, 477],
[508, 507],
[510, 510],
[511, 514],
[509, 518],
[507, 521],
[458, 559],
[455, 560],
[399, 584],
[399, 584],
[369, 591],
[367, 592],
[308, 597],
[305, 596],
[240, 584],
[239, 584],
[220, 577],
[169, 552],
[166, 551],
[120, 510],
[117, 507],
[116, 503],
[117, 499],
[121, 495],
[153, 468],
[156, 467],
[161, 467]]), array([[439, 444],
[443, 444],
[446, 446],
[448, 448],
[450, 451],
[450, 454],
[448, 498],
[448, 502],
[445, 505],
[442, 507],
[439, 507],
[399, 505],
[196, 506],
[192, 505],
[189, 503],
[187, 500],
[187, 497],
[186, 458],
[186, 456],
[187, 451],
[188, 448],
[192, 444],
[194, 444],
[198, 443]]), array([[463, 347],
[468, 347],
[472, 350],
[474, 353],
[476, 360],
[477, 425],
[476, 429],
[474, 433],
[470, 436],
[466, 438],
[463, 438],
[175, 439],
[170, 438],
[166, 435],
[163, 432],
[161, 426],
[161, 361],
[161, 356],
[163, 352],
[167, 349],
[172, 347],
[184, 346],
[186, 346]]), array([[325, 38],
[485, 91],
[489, 94],
[493, 96],
[587, 225],
[588, 230],
[589, 234],
[592, 384],
[591, 389],
[588, 393],
[585, 397],
[581, 399],
[576, 399],
[572, 398],
[508, 380],
[503, 379],
[499, 375],
[498, 370],
[497, 367],
[493, 258],
[428, 171],
[421, 165],
[323, 136],
[225, 165],
[207, 175],
[144, 260],
[141, 365],
[141, 370],
[138, 374],
[134, 378],
[131, 379],
[ 66, 398],
[ 61, 398],
[ 56, 398],
[ 52, 395],
[ 48, 391],
[ 47, 386],
[ 47, 384],
[ 47, 235],
[ 48, 230],
[ 50, 226],
[146, 96],
[151, 92],
[154, 91],
[315, 38],
[320, 37]])], 'dt_scores': [0.99375725701319, 0.9871711582010613, 0.9937523531067023, 0.9911629231838204], 'rec_text': ['5263647368706', 'ๅ็น็ฉ', 'ๅ็ฅจไธๅคฉๆดฅๅๅ็ผๅ่ดธๆ้ๅ
ฌๅธ'], 'rec_score': [0.9933745265007019, 0.998288631439209, 0.9999362230300903, 0.9923253655433655], 'input_path': PosixPath('/Users/chenghong0temp/tmpa8eqnpus.png')}, 'src_file_name': 'https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png', 'page_id': 0}
Computer VisionCLI
paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': '/root/.paddlex/predict_input/general_image_classification_001.jpg', 'class_ids': [296, 170, 356, 258, 248], 'scores': [0.62736, 0.03752, 0.03256, 0.0323, 0.03194], '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 --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': '/root/.paddlex/predict_input/general_object_detection_002.png', 'boxes': [{'cls_id': 49, 'label': 'orange', 'score': 0.8188097476959229, 'coordinate': [661, 93, 870, 305]}, {'cls_id': 47, 'label': 'apple', 'score': 0.7743489146232605, 'coordinate': [76, 274, 330, 520]}, {'cls_id': 47, 'label': 'apple', 'score': 0.7270504236221313, 'coordinate': [285, 94, 469, 297]}, {'cls_id': 46, 'label': 'banana', 'score': 0.5570532083511353, 'coordinate': [310, 361, 685, 712]}, {'cls_id': 47, 'label': 'apple', 'score': 0.5484835505485535, 'coordinate': [764, 285, 924, 440]}, {'cls_id': 47, 'label': 'apple', 'score': 0.5160726308822632, 'coordinate': [853, 169, 987, 303]}, {'cls_id': 60, 'label': 'dining table', 'score': 0.5142655968666077, 'coordinate': [0, 0, 1072, 720]}, {'cls_id': 47, 'label': 'apple', 'score': 0.5101479291915894, 'coordinate': [57, 23, 213, 176]}]}
paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': '/root/.paddlex/predict_input/general_instance_segmentation_004.png', 'boxes': [{'cls_id': 0, 'label': 'person', 'score': 0.8698326945304871, 'coordinate': [339, 0, 639, 575]}, {'cls_id': 0, 'label': 'person', 'score': 0.8571141362190247, 'coordinate': [0, 0, 195, 575]}, {'cls_id': 0, 'label': 'person', 'score': 0.8202633857727051, 'coordinate': [88, 113, 401, 574]}, {'cls_id': 0, 'label': 'person', 'score': 0.7108577489852905, 'coordinate': [522, 21, 767, 574]}, {'cls_id': 27, 'label': 'tie', 'score': 0.554280698299408, 'coordinate': [247, 311, 355, 574]}]}
paddlex --pipeline semantic_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
paddlex --pipeline multi_label_image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --device gpu:0 --save_path output
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
What's the result
{'input_path': '/root/.paddlex/predict_input/small_object_detection.jpg', 'boxes': [{'cls_id': 3, 'label': 'car', 'score': 0.9243856072425842, 'coordinate': [624, 638, 682, 741]}, {'cls_id': 3, 'label': 'car', 'score': 0.9206348061561584, 'coordinate': [242, 561, 356, 613]}, {'cls_id': 3, 'label': 'car', 'score': 0.9194547533988953, 'coordinate': [670, 367, 705, 400]}, {'cls_id': 3, 'label': 'car', 'score': 0.9162291288375854, 'coordinate': [459, 259, 523, 283]}, {'cls_id': 4, 'label': 'van', 'score': 0.9075379371643066, 'coordinate': [467, 213, 498, 242]}, {'cls_id': 4, 'label': 'van', 'score': 0.9066920876502991, 'coordinate': [547, 351, 577, 397]}, {'cls_id': 3, 'label': 'car', 'score': 0.9041045308113098, 'coordinate': [502, 632, 562, 736]}, {'cls_id': 3, 'label': 'car', 'score': 0.8934890627861023, 'coordinate': [613, 383, 647, 427]}, {'cls_id': 3, 'label': 'car', 'score': 0.8803309202194214, 'coordinate': [640, 280, 671, 309]}, {'cls_id': 3, 'label': 'car', 'score': 0.8727016448974609, 'coordinate': [1199, 256, 1259, 281]}, {'cls_id': 3, 'label': 'car', 'score': 0.8705748915672302, 'coordinate': [534, 410, 570, 461]}, {'cls_id': 3, 'label': 'car', 'score': 0.8654043078422546, 'coordinate': [669, 248, 702, 271]}, {'cls_id': 3, 'label': 'car', 'score': 0.8555219769477844, 'coordinate': [525, 243, 550, 270]}, {'cls_id': 3, 'label': 'car', 'score': 0.8522038459777832, 'coordinate': [526, 220, 553, 243]}, {'cls_id': 3, 'label': 'car', 'score': 0.8392605185508728, 'coordinate': [557, 141, 575, 158]}, {'cls_id': 3, 'label': 'car', 'score': 0.8353804349899292, 'coordinate': [537, 120, 553, 133]}, {'cls_id': 3, 'label': 'car', 'score': 0.8322211503982544, 'coordinate': [585, 132, 603, 147]}, {'cls_id': 3, 'label': 'car', 'score': 0.8298957943916321, 'coordinate': [701, 283, 736, 313]}, {'cls_id': 3, 'label': 'car', 'score': 0.8217393159866333, 'coordinate': [885, 347, 943, 377]}, {'cls_id': 3, 'label': 'car', 'score': 0.820313572883606, 'coordinate': [493, 150, 511, 168]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.8183429837226868, 'coordinate': [203, 701, 224, 743]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.815082848072052, 'coordinate': [185, 710, 201, 744]}, {'cls_id': 6, 'label': 'tricycle', 'score': 0.7892289757728577, 'coordinate': [311, 371, 344, 407]}, {'cls_id': 6, 'label': 'tricycle', 'score': 0.7812919020652771, 'coordinate': [345, 380, 388, 405]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.7748346328735352, 'coordinate': [295, 500, 309, 532]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.7688500285148621, 'coordinate': [851, 436, 863, 466]}, {'cls_id': 3, 'label': 'car', 'score': 0.7466475367546082, 'coordinate': [565, 114, 580, 128]}, {'cls_id': 3, 'label': 'car', 'score': 0.7156463265419006, 'coordinate': [483, 66, 495, 78]}, {'cls_id': 3, 'label': 'car', 'score': 0.704211950302124, 'coordinate': [607, 138, 642, 152]}, {'cls_id': 3, 'label': 'car', 'score': 0.7021926045417786, 'coordinate': [505, 72, 518, 83]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.6897469162940979, 'coordinate': [802, 460, 815, 488]}, {'cls_id': 3, 'label': 'car', 'score': 0.671891450881958, 'coordinate': [574, 123, 593, 136]}, {'cls_id': 9, 'label': 'motorcycle', 'score': 0.6712754368782043, 'coordinate': [445, 317, 472, 334]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.6695684790611267, 'coordinate': [479, 309, 489, 332]}, {'cls_id': 3, 'label': 'car', 'score': 0.6273623704910278, 'coordinate': [654, 210, 677, 234]}, {'cls_id': 3, 'label': 'car', 'score': 0.6070230603218079, 'coordinate': [640, 166, 667, 185]}, {'cls_id': 3, 'label': 'car', 'score': 0.6064521670341492, 'coordinate': [461, 59, 476, 71]}, {'cls_id': 3, 'label': 'car', 'score': 0.5860581398010254, 'coordinate': [464, 87, 484, 100]}, {'cls_id': 9, 'label': 'motorcycle', 'score': 0.5792551636695862, 'coordinate': [390, 390, 419, 408]}, {'cls_id': 3, 'label': 'car', 'score': 0.5559225678443909, 'coordinate': [481, 125, 496, 140]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.5531904697418213, 'coordinate': [869, 306, 880, 331]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.5468509793281555, 'coordinate': [895, 294, 904, 319]}, {'cls_id': 3, 'label': 'car', 'score': 0.5451828241348267, 'coordinate': [505, 94, 518, 108]}, {'cls_id': 3, 'label': 'car', 'score': 0.5398445725440979, 'coordinate': [657, 188, 681, 208]}, {'cls_id': 4, 'label': 'van', 'score': 0.5318890810012817, 'coordinate': [518, 88, 534, 102]}, {'cls_id': 3, 'label': 'car', 'score': 0.5296525359153748, 'coordinate': [527, 71, 540, 81]}, {'cls_id': 6, 'label': 'tricycle', 'score': 0.5168400406837463, 'coordinate': [528, 320, 563, 346]}, {'cls_id': 3, 'label': 'car', 'score': 0.5088561177253723, 'coordinate': [511, 84, 530, 95]}, {'cls_id': 0, 'label': 'pedestrian', 'score': 0.502006471157074, 'coordinate': [841, 266, 850, 283]}]}
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
- After running, the output results and visualized images will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
Time Series-relatedCLI
paddlex --pipeline ts_fc --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0 --save_path output
- After running, the output results will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
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_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0 --save_path output
- After running, the output results will be saved in the
outputfolder. You can also customize the save path by modifying the--save_pathparameter.
๐ Python Usage¶
A few lines of code can complete the quick inference of the production line, with a unified Python script format as follows:
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline=[production line 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 production line object- Pass in the image and call the
predictmethod of the production line object for inference prediction - Process the prediction results
OCR-related Python
Computer Vision Python
Time Series-related Python
๐ Detailed Tutorials¶
-
Document Information Extraction
Document scene information extraction v3 (PP-ChatOCRv3) is a document and image intelligent analysis solution with PaddlePaddle features, combining LLM and OCR technologies to solve complex document information extraction challenges such as layout analysis, rare character recognition, multi-page PDF, table, and seal recognition in one stop.
-
OCR
The general OCR production line 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.
-
Image Classification
Image classification can automatically extract image features and classify them accurately, recognizing various objects such as animals, plants, traffic signs, etc., and is widely used in object recognition, scene understanding, and automatic tagging fields.
-
Object Detection
Object detection aims to identify the categories and locations of multiple objects in images or videos by generating bounding boxes to mark these objects. This technology is widely used in fields such as autonomous driving, surveillance systems, and smart photo albums.
-
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.
๐ฌ 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.