---
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typora-copy-images-to: images
hide:
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---
## 🔍 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.
## 🛠️ 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
=== "CPU"
```bash
python -m pip install paddlepaddle==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
```
=== "CUDA 11.8"
```bash
python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
```
=== "CUDA 12.3"
```bash
python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
```
> ❗ For more PaddlePaddle Wheel versions, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html).
### Installing PaddleX
```bash
pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b2-py3-none-any.whl
```
> ❗ For more installation methods, please refer to the [PaddleX Installation Guide](https://paddlepaddle.github.io/PaddleX/latest/installation/installation.html)
## 💻 Command Line Usage
A single command can quickly experience the production line effect, with a unified command line format as follows:
```bash
paddlex --pipeline [production line name] --input [input image] --device [running device]
```
You only need to specify three parameters:
* `pipeline`: The name of the production line
* `input`: The local path or URL of the input file to be processed (e.g., an image)
* `device`: The GPU number used (for example, `gpu:0` indicates using the 0th GPU), or you can choose to use CPU (`cpu`)
!!! example "OCR-related CLI"
=== "OCR"
```bash
paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
'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': ['登机牌', 'BOARDING', 'PASS', '舱位', 'CLASS', '序号 SERIALNO.', '座位号', '日期 DATE', 'SEAT NO', '航班 FLIGHW', '035', 'MU2379', '始发地', 'FROM', '登机口', 'GATE', '登机时间BDT', '目的地TO', '福州', 'TAIYUAN', 'G11', 'FUZHOU', '身份识别IDNO', '姓名NAME', 'ZHANGQIWEI', 票号TKTNO', '张祺伟', '票价FARE', 'ETKT7813699238489/1', '登机口于起飞前10分钟关闭GATESCLOSE10MINUTESBEFOREDEPARTURETIME'],
'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]}
```
=== "img"
=== "Table Recognition"
```bash
paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'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', '连续工作3', '取出来放在网上，没想', '江、整江等八大', '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': '| Dres | 连续工作3 | 取出来放在网上，没想 | 江、整江等八大 |
| Abstr | | rSrivi | $709. |
| cludingGiv | 2.72 | Ingcubic | $744.78 |
'}]}
```
=== "img"
=== "Layout Parsing"
```bash
paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0
```
??? question "What's the result"
```bash
{'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 K’ 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'}]}
```
=== "Formula Recognition"
```bash
paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'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)']}
```
=== "img"
=== "Seal Text Recognition"
```bash
paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'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', '吗繁物', '发票专天津君和缘商贸有限公司'], '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}
```
=== "img"
!!! example "Computer VisionCLI"
=== "Image Classification"
```bash
paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'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']}
```
=== "img"
=== "Object Detection"
```bash
paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'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]}]}
```
=== "img"
=== "Instance Segmentation"
```bash
paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'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]}]}
```
=== "img"
=== "Semantic Segmentation"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'input_path': '/root/.paddlex/predict_input/makassaridn-road_demo.png', 'pred': '...'}
```
=== "img"
=== "Image Multi-label Classification"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'input_path': '/root/.paddlex/predict_input/general_image_classification_001.jpg', 'class_ids': [21, 0, 30, 24], 'scores': [0.99257, 0.70596, 0.63001, 0.57852], 'label_names': ['bear', 'person', 'skis', 'backpack']}
```
=== "img"
=== "Small Object Detection"
```bash
paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'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]}]}
```
=== "img"
=== "Image Anomaly Detection"
```bash
paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'input_path': '/root/.paddlex/predict_input/uad_grid.png', 'pred': '...'}
```
=== "img"
!!! example "Time Series-relatedCLI"
=== "Time Series Forecasting"
```bash
paddlex --pipeline ts_fc --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0
```
??? question "What's the result"
```bash
{'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]}
```
=== "Time Series Anomaly Detection"
```bash
paddlex --pipeline ts_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0
```
??? question "What's the result"
```bash
{'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]}
```
=== "Time Series Classification"
```bash
paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0
```
??? question "What's the result"
```bash
{'input_path': 'ts_cls.csv', 'classification': classid score
sample
0 0 0.617688}
```
## 📝 Python Usage
A few lines of code can complete the quick inference of the production line, with a unified Python script format as follows:
```python
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/")
```
The following steps were executed:
* `create_pipeline()` instantiates the production line object
* Pass in the image and call the `predict` method of the production line object for inference prediction
* Process the prediction results
!!! example "OCR-related Python"
=== "OCR"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="OCR")
output = pipeline.predict("general_ocr_002.png")
for res in output:
res.print()
res.save_to_img("./output/")
```
=== "Table Recognition"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="table_recognition")
output = pipeline.predict("table_recognition.jpg")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_xlsx("./output/")
res.save_to_html("./output/")
```
=== "Layout Parsing"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="layout_parsing")
output = pipeline.predict("demo_paper.png")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_xlsx("./output/")
res.save_to_html("./output/")
```
=== "Formula Recognition"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="formula_recognition")
output = pipeline.predict("general_formula_recognition.png")
for res in output:
res.print()
```
=== "Seal Text Recognition"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="seal_recognition")
output = pipeline.predict("seal_text_det.png")
for res in output:
res.print()
res.save_to_img("./output/")
```
!!! example "Computer Vision Python"
=== "Image Classification"
```python
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()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
=== "Object Detection"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="object_detection")
output = pipeline.predict("general_object_detection_002.png")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
=== "Instance Segmentation"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="instance_segmentation")
output = pipeline.predict("general_instance_segmentation_004.png")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
=== "Semantic Segmentation"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="semantic_segmentation")
output = pipeline.predict("makassaridn-road_demo.png")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
=== "Image Multi-label Classification"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="multi_label_image_classification")
output = pipeline.predict("general_image_classification_001.jpg")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
=== "Small Object Detection"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="small_object_detection")
output = pipeline.predict("small_object_detection.jpg")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
=== "Image Anomaly Detection"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="anomaly_detection")
output = pipeline.predict("uad_grid.png")
for res in output:
res.print()
res.save_to_img("./output/")
res.save_to_json("./output/")
```
!!! example "Time Series-related Python"
=== "Time Series Forecasting"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="ts_fc")
output = pipeline.predict("ts_fc.csv")
for res in output:
res.print()
res.save_to_csv("./output/")
```
=== "Time Series Anomaly Detection"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="./my_path/ts_ad.yaml")
output = pipeline.predict("ts_ad.cs")
for res in output:
res.print()
res.save_to_csv("./output/")
```
=== "Time Series Classification"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="ts_cls")
output = pipeline.predict("ts_cls.csv")
for res in output:
res.print()
res.save_to_csv("./output/")
```
## 🚀 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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.en.md)
- **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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/ocr_pipelines/OCR.en.md)
- **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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/cv_pipelines/image_classification.en.md)
- **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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/cv_pipelines/object_detection.en.md)
- **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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/cv_pipelines/small_object_detection.en.md)
- **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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.en.md)
[:octicons-arrow-right-24: More](pipeline_usage/pipeline_develop_guide.en.md)
## 💬 Discussion
We warmly welcome and encourage community members to raise questions, share ideas, and feedback in the [Discussions](https://github.com/PaddlePaddle/PaddleX/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.
