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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
Before installing PaddleX, please ensure that you have a basic <b>Python runtime environment</b> (Note: Currently supports <b>Python 3.8 to Python 3.12</b>). The PaddleX 3.0-rc0 version depends on PaddlePaddle version <b>3.0.0rc0</b>.
Installing PaddlePaddle
=== "CPU version"
```bash
python -m pip install paddlepaddle==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
```
=== "GPU version, requiring a GPU driver version ≥450.80.02 (Linux) or ≥452.39 (Windows)."
```bash
python -m pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
```
=== "GPU version, requiring a GPU driver version ≥545.23.06 (Linux) or ≥545.84 (Windows)."
```bash
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](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html).
Installing PaddleX
pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0rc0-py3-none-any.whl
❗ For more installation methods, please refer to the PaddleX Installation Guide
💻 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:0indicates using the first GPU). You can also choose to use NPU (npu:0), XPU (xpu:0), CPU (cpu), etc.
!!! example "OCR-related Pipelines CLI"
=== "OCR"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'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)}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/ocr/03.png"></p>
=== "Table Recognition"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'table_recognition.jpg', 'model_settings': {'use_doc_preprocessor': True, 'use_layout_detection': True, 'use_ocr_model': True}, 'doc_preprocessor_res': {'input_path': '0.jpg', 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': True}, 'angle': 0}, 'layout_det_res': {'input_path': None, 'boxes': [{'cls_id': 0, 'label': 'Table', 'score': 0.9196816086769104, 'coordinate': [0, 8.614925, 550.9877, 132]}]}, 'overall_ocr_res': {'input_path': '0.jpg', 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': [array([[232, 0],
[318, 1],
[318, 24],
[232, 21]], dtype=int16), array([[32, 38],
[67, 38],
[67, 55],
[32, 55]], dtype=int16), array([[119, 34],
[196, 34],
[196, 57],
[119, 57]], dtype=int16), array([[222, 29],
[396, 31],
[396, 60],
[222, 58]], dtype=int16), array([[420, 30],
[542, 32],
[542, 61],
[419, 59]], dtype=int16), array([[29, 71],
[72, 71],
[72, 92],
[29, 92]], dtype=int16), array([[287, 72],
[329, 72],
[329, 93],
[287, 93]], dtype=int16), array([[458, 68],
[501, 71],
[499, 94],
[456, 91]], dtype=int16), array([[ 9, 101],
[ 89, 103],
[ 89, 130],
[ 8, 128]], dtype=int16), array([[139, 105],
[172, 105],
[172, 126],
[139, 126]], dtype=int16), array([[274, 103],
[339, 101],
[340, 128],
[275, 130]], dtype=int16), array([[451, 103],
[508, 103],
[508, 126],
[451, 126]], 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, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 'text_rec_score_thresh': 0, 'rec_texts': ['CRuncover', 'Dres', '连续工作3', '取出来放在网上，没想', '江、江等八大', 'Abstr', 'rSrivi', '$709.', 'cludingGiv', '2.72', 'Ingcubic', '$744.78'], 'rec_scores': [0.9943075180053711, 0.9951075315475464, 0.9907732009887695, 0.9975494146347046, 0.9974043369293213, 0.9983242750167847, 0.991967499256134, 0.9898287653923035, 0.9961177110671997, 0.9975040555000305, 0.9986456632614136, 0.9987970590591431], 'rec_polys': [array([[232, 0],
[318, 1],
[318, 24],
[232, 21]], dtype=int16), array([[32, 38],
[67, 38],
[67, 55],
[32, 55]], dtype=int16), array([[119, 34],
[196, 34],
[196, 57],
[119, 57]], dtype=int16), array([[222, 29],
[396, 31],
[396, 60],
[222, 58]], dtype=int16), array([[420, 30],
[542, 32],
[542, 61],
[419, 59]], dtype=int16), array([[29, 71],
[72, 71],
[72, 92],
[29, 92]], dtype=int16), array([[287, 72],
[329, 72],
[329, 93],
[287, 93]], dtype=int16), array([[458, 68],
[501, 71],
[499, 94],
[456, 91]], dtype=int16), array([[ 9, 101],
[ 89, 103],
[ 89, 130],
[ 8, 128]], dtype=int16), array([[139, 105],
[172, 105],
[172, 126],
[139, 126]], dtype=int16), array([[274, 103],
[339, 101],
[340, 128],
[275, 130]], dtype=int16), array([[451, 103],
[508, 103],
[508, 126],
[451, 126]], dtype=int16)], 'rec_boxes': array([[232, 0, 318, 24],
[ 32, 38, 67, 55],
[119, 34, 196, 57],
[222, 29, 396, 60],
[419, 30, 542, 61],
[ 29, 71, 72, 92],
[287, 72, 329, 93],
[456, 68, 501, 94],
[ 8, 101, 89, 130],
[139, 105, 172, 126],
[274, 101, 340, 130],
[451, 103, 508, 126]], dtype=int16)}, 'table_res_list': [{'cell_box_list': array([[ 8. , 9.61492538, 532. , 26.61492538],
[ 3. , 27.61492538, 104. , 65.61492538],
[109. , 28.61492538, 215. , 66.61492538],
[219. , 28.61492538, 396. , 64.61492538],
[396. , 29.61492538, 546. , 66.61492538],
[ 1. , 65.61492538, 110. , 93.61492538],
[111. , 65.61492538, 215. , 94.61492538],
[220. , 66.61492538, 397. , 94.61492538],
[398. , 67.61492538, 544. , 94.61492538],
[ 2. , 98.61492538, 111. , 131.61492538],
[113. , 98.61492538, 216. , 131.61492538],
[219. , 98.61492538, 400. , 131.61492538],
[403. , 99.61492538, 545. , 130.61492538]]), 'pred_html': '<html><body><table><tr><td colspan="4">CRuncover</td></tr><tr><td>Dres</td><td>连续工作3</td><td>取出来放在网上，没想</td><td>江、江等八大</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([[232, 0],
[318, 1],
[318, 24],
[232, 21]], dtype=int16), array([[32, 38],
[67, 38],
[67, 55],
[32, 55]], dtype=int16), array([[119, 34],
[196, 34],
[196, 57],
[119, 57]], dtype=int16), array([[222, 29],
[396, 31],
[396, 60],
[222, 58]], dtype=int16), array([[420, 30],
[542, 32],
[542, 61],
[419, 59]], dtype=int16), array([[29, 71],
[72, 71],
[72, 92],
[29, 92]], dtype=int16), array([[287, 72],
[329, 72],
[329, 93],
[287, 93]], dtype=int16), array([[458, 68],
[501, 71],
[499, 94],
[456, 91]], dtype=int16), array([[ 9, 101],
[ 89, 103],
[ 89, 130],
[ 8, 128]], dtype=int16), array([[139, 105],
[172, 105],
[172, 126],
[139, 126]], dtype=int16), array([[274, 103],
[339, 101],
[340, 128],
[275, 130]], dtype=int16), array([[451, 103],
[508, 103],
[508, 126],
[451, 126]], dtype=int16)], 'rec_texts': ['CRuncover', 'Dres', '连续工作3', '取出来放在网上，没想', '江、江等八大', 'Abstr', 'rSrivi', '$709.', 'cludingGiv', '2.72', 'Ingcubic', '$744.78'], 'rec_scores': [0.9943075180053711, 0.9951075315475464, 0.9907732009887695, 0.9975494146347046, 0.9974043369293213, 0.9983242750167847, 0.991967499256134, 0.9898287653923035, 0.9961177110671997, 0.9975040555000305, 0.9986456632614136, 0.9987970590591431], 'rec_boxes': [array([232, 0, 318, 24], dtype=int16), array([32, 38, 67, 55], dtype=int16), array([119, 34, 196, 57], dtype=int16), array([222, 29, 396, 60], dtype=int16), array([419, 30, 542, 61], dtype=int16), array([29, 71, 72, 92], dtype=int16), array([287, 72, 329, 93], dtype=int16), array([456, 68, 501, 94], dtype=int16), array([ 8, 101, 89, 130], dtype=int16), array([139, 105, 172, 126], dtype=int16), array([274, 101, 340, 130], dtype=int16), array([451, 103, 508, 126], dtype=int16)]}}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition/03.png"></p>
=== "Table Recognition v2"
```bash
paddlex --pipeline table_recognition_v2 \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg \
--save_path ./output \
--device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'table_recognition.jpg', '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': 0, 'label': 'Table', 'score': 0.9922188520431519, 'coordinate': [3.0127392, 0.14648987, 547.5102, 127.72023]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': [array([[234, 6],
[316, 6],
[316, 25],
[234, 25]], dtype=int16), array([[38, 39],
[73, 39],
[73, 57],
[38, 57]], dtype=int16), array([[122, 32],
[201, 32],
[201, 58],
[122, 58]], dtype=int16), array([[227, 34],
[346, 34],
[346, 57],
[227, 57]], dtype=int16), array([[351, 34],
[391, 34],
[391, 58],
[351, 58]], dtype=int16), array([[417, 35],
[534, 35],
[534, 58],
[417, 58]], dtype=int16), array([[34, 70],
[78, 70],
[78, 90],
[34, 90]], dtype=int16), array([[287, 70],
[328, 70],
[328, 90],
[287, 90]], dtype=int16), array([[454, 69],
[496, 69],
[496, 90],
[454, 90]], dtype=int16), array([[ 17, 101],
[ 95, 101],
[ 95, 124],
[ 17, 124]], dtype=int16), array([[144, 101],
[178, 101],
[178, 122],
[144, 122]], dtype=int16), array([[278, 101],
[338, 101],
[338, 124],
[278, 124]], dtype=int16), array([[448, 101],
[503, 101],
[503, 121],
[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': [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], 'text_rec_score_thresh': 0, 'rec_texts': ['CRuncover', '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_scores': [0.9951260685920715, 0.9943379759788513, 0.9968608021736145, 0.9978817105293274, 0.9985721111297607, 0.9616036415100098, 0.9977153539657593, 0.987593948841095, 0.9906861186027527, 0.9959743618965149, 0.9970152378082275, 0.9977849721908569, 0.9984450936317444], 'rec_polys': [array([[234, 6],
[316, 6],
[316, 25],
[234, 25]], dtype=int16), array([[38, 39],
[73, 39],
[73, 57],
[38, 57]], dtype=int16), array([[122, 32],
[201, 32],
[201, 58],
[122, 58]], dtype=int16), array([[227, 34],
[346, 34],
[346, 57],
[227, 57]], dtype=int16), array([[351, 34],
[391, 34],
[391, 58],
[351, 58]], dtype=int16), array([[417, 35],
[534, 35],
[534, 58],
[417, 58]], dtype=int16), array([[34, 70],
[78, 70],
[78, 90],
[34, 90]], dtype=int16), array([[287, 70],
[328, 70],
[328, 90],
[287, 90]], dtype=int16), array([[454, 69],
[496, 69],
[496, 90],
[454, 90]], dtype=int16), array([[ 17, 101],
[ 95, 101],
[ 95, 124],
[ 17, 124]], dtype=int16), array([[144, 101],
[178, 101],
[178, 122],
[144, 122]], dtype=int16), array([[278, 101],
[338, 101],
[338, 124],
[278, 124]], dtype=int16), array([[448, 101],
[503, 101],
[503, 121],
[448, 121]], dtype=int16)], 'rec_boxes': array([[234, 6, 316, 25],
[ 38, 39, 73, 57],
[122, 32, 201, 58],
[227, 34, 346, 57],
[351, 34, 391, 58],
[417, 35, 534, 58],
[ 34, 70, 78, 90],
[287, 70, 328, 90],
[454, 69, 496, 90],
[ 17, 101, 95, 124],
[144, 101, 178, 122],
[278, 101, 338, 124],
[448, 101, 503, 121]], dtype=int16)}, 'table_res_list': [{'cell_box_list': [array([3.18822289e+00, 1.46489874e-01, 5.46996138e+02, 3.08782365e+01]), array([ 3.21032453, 31.1510637 , 110.20750237, 65.14108063]), array([110.18174553, 31.13076188, 213.00813103, 65.02860047]), array([212.96108818, 31.09959008, 404.19618034, 64.99535157]), array([404.08112907, 31.18304802, 547.00864983, 65.0847223 ]), array([ 3.21772957, 65.0738733 , 110.33685875, 96.07921387]), array([110.23703575, 65.02486207, 213.08839226, 96.01378419]), array([213.06095695, 64.96230103, 404.28425407, 95.97141816]), array([404.23704338, 65.04879548, 547.01273918, 96.03654267]), array([ 3.22793937, 96.08334137, 110.38572502, 127.08698823]), array([110.40586662, 96.10539795, 213.19943047, 127.07002045]), array([213.12627983, 96.0539148 , 404.42686272, 127.02842499]), array([404.33042717, 96.07251526, 547.01273918, 126.45088746])], 'pred_html': '<html><body><table><tr><td colspan="4">CRuncover</td></tr><tr><td>Dres</td><td>连续工作3</td><td>取出来放在网上 没想</td><td>江、整江等八大</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],
[316, 6],
[316, 25],
[234, 25]], dtype=int16), array([[38, 39],
[73, 39],
[73, 57],
[38, 57]], dtype=int16), array([[122, 32],
[201, 32],
[201, 58],
[122, 58]], dtype=int16), array([[227, 34],
[346, 34],
[346, 57],
[227, 57]], dtype=int16), array([[351, 34],
[391, 34],
[391, 58],
[351, 58]], dtype=int16), array([[417, 35],
[534, 35],
[534, 58],
[417, 58]], dtype=int16), array([[34, 70],
[78, 70],
[78, 90],
[34, 90]], dtype=int16), array([[287, 70],
[328, 70],
[328, 90],
[287, 90]], dtype=int16), array([[454, 69],
[496, 69],
[496, 90],
[454, 90]], dtype=int16), array([[ 17, 101],
[ 95, 101],
[ 95, 124],
[ 17, 124]], dtype=int16), array([[144, 101],
[178, 101],
[178, 122],
[144, 122]], dtype=int16), array([[278, 101],
[338, 101],
[338, 124],
[278, 124]], dtype=int16), array([[448, 101],
[503, 101],
[503, 121],
[448, 121]], dtype=int16)], 'rec_texts': ['CRuncover', '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_scores': [0.9951260685920715, 0.9943379759788513, 0.9968608021736145, 0.9978817105293274, 0.9985721111297607, 0.9616036415100098, 0.9977153539657593, 0.987593948841095, 0.9906861186027527, 0.9959743618965149, 0.9970152378082275, 0.9977849721908569, 0.9984450936317444], 'rec_boxes': [array([234, 6, 316, 25], dtype=int16), array([38, 39, 73, 57], dtype=int16), array([122, 32, 201, 58], dtype=int16), array([227, 34, 346, 57], dtype=int16), array([351, 34, 391, 58], dtype=int16), array([417, 35, 534, 58], dtype=int16), array([34, 70, 78, 90], dtype=int16), array([287, 70, 328, 90], dtype=int16), array([454, 69, 496, 90], dtype=int16), array([ 17, 101, 95, 124], dtype=int16), array([144, 101, 178, 122], dtype=int16), array([278, 101, 338, 124], dtype=int16), array([448, 101, 503, 121], dtype=int16)]}}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/table_recognition_v2/03.png"></p>
=== "Layout Parsing"
```bash
paddlex --pipeline layout_parsing \
--input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0 \
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'demo_paper.png', 'model_settings': {'use_doc_preprocessor': True, 'use_general_ocr': True, 'use_seal_recognition': True, 'use_table_recognition': True, 'use_formula_recognition': True}, 'parsing_res_list': [{'layout_bbox': [46.905365, 44.05746, 565.6911, 217.74211], 'image': array([[[255, ..., 255],
...,
[188, ..., 175]],
...,
[[255, ..., 255],
...,
[255, ..., 255]]], dtype=uint8), 'image_text': 'Efficient Hybrid Encoder\nCaevIst s1\nCaaveJs2\nCCFF\n\nFusion\n00□0□000000', 'layout': 'single'}, {'layout_bbox': [42.178703, 227.34215, 570.1248, 284.377], 'text': 'Figure 4, Overview of RT-DETR. We feed the features from the last three stages of the backbone into the encoder. The eficient hybrid\nencoder transforms multi-scale features into a sequence of image features through the Attention-based Intra-scale Feature Interaction (AIFI)\nfeatures to serve as initial object queries for the decoder, Finaly, the decoder with auxiliary prediction heads iteratively optimizes object\nand the CNN-based Cross-scale Feature Fusion (CCFF), Then, the uncertainty-minimal query selection selects a fixed number of encoder\nqueries to generate categories and boxes.', 'layout': 'single'}, {'layout_bbox': [53.227856, 294.16644, 283.854, 396.24164], 'image': array([[[255, ..., 255],
...,
[255, ..., 255]],
...,
[[255, ..., 255],
...,
[255, ..., 255]]], dtype=uint8), 'image_text': '1x1Conv\nFusion\nC\nNX\n1x1Conv\nRepBlock\nCConcatenate\nElement-wise add\nFlatten', 'layout': 'double'}, {'layout_bbox': [99.52045, 401.63477, 240.27536, 411.01334], 'text': 'Figure 5. The fusion block in CCFF', 'layout': 'double'}, {'layout_bbox': [41.776196, 418.61166, 296.82672, 554.4149], 'text': 'D, Ds, not only significantly reduces latency (35% faster),\nbut also improves accuracy (0.4% AP higher). CCFF is opti-\nmized based on the cross-scale fusion module, which inserts\nseveral fusion blocks consisting of convolutional layers into\nthe fusion path. The role of the fusion block is to fuse two\nadjacent scale features into a new feature, and its structure is\nillustrated in Figure 5. The fusion block contains two 1 × 1\nconvolutions to adjust the number of channels, N RepBlocks\ncomposed of RepConv [8] are used for feature fusion, and\nthe two-path outputs are fused by element-wise add. We\nformulate the calculation of the hybrid encoder as:', 'layout': 'double'}, {'layout_bbox': [92.092064, 563.1221, 297.57217, 607.2598], 'formula': '\\begin{array}{r l}{\\mathcal{Q}}&{=\\mathcal{K}=\\mathcal{V}=\\mathtt{F l a t t e r n}(\\mathcal{S}_{5}),}\\\\ {\\mathcal{F}_{5}}&{=\\mathtt{R e s h a p e}(\\mathtt{A I F I}(\\mathcal{Q},\\mathcal{K},\\mathcal{V})),\\qquad\\quad(1)}\\\\ {\\mathcal{O}}&{=\\mathtt{C C F F}(\\{\\mathcal{S}_{3},\\mathcal{S}_{4},\\mathcal{F}_{5}\\}),}\\end{array}', 'layout': 'double'}, {'layout_bbox': [41.275124, 613.64154, 298.0696, 636.9947], 'text': 'where Reshape represents restoring the shape of the flat-\ntened feature to the same shape as S5', 'layout': 'double'}, {'layout_bbox': [41.01993, 645.3085, 253.87302, 656.61505], 'text': '4.3.Uncertainty-minimal Ouery Selection', 'layout': 'double'}, {'layout_bbox': [39.780045, 664.1547, 296.5302, 724.974], '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': 'double'}, {'layout_bbox': [316.3008, 289.54156, 573.4635, 415.4659], 'text': 'The confidence score represents the likelihood that the fea-\nture includes foreground objects. Nevertheless, the detector\nare required to simultaneously model the category and loca-\ntion of objects, both of which determine the quality of the\nfeatures. Hence, the performance score of the feature is a la-\ntent variable that is jointly correlated with both classification\nand localization. Based on the analysis, the current query\nselection lead to a considerable level of uncertainty in the\nselected features, resulting in sub-optimal initialization for\nthe decoder and hindering the performance of the detector.', 'layout': 'double'}, {'layout_bbox': [316.1587, 417.67807, 575.0031, 541.93054], 'text': 'To address this problem, we propose the uncertainty mini-\nmal query selection scheme, which explicitly constructs and\noptimizes the epistemic uncertainty to model the joint latent\nvariable of encoder features, thereby providing high-quality\nqueries for the decoder. Specifically, the feature uncertainty\nL/ is defined as the discrepancy between the predicted dis-\ntributions of localization P and classification C in Eq. (2).\nTo minimize the uncertainty of the queries, we integrate\nthe uncertainty into the loss function for the gradient-based\noptimization in Eq. (3).', 'layout': 'double'}, {'layout_bbox': [343.82712, 551.06995, 573.45465, 589.9438], 'formula': '\\begin{array}{r l r}{\\mathcal{U}(\\hat{\\mathcal{X}})=\\|\\mathcal{P}(\\hat{\\mathcal{X}})-\\mathcal{C}(\\hat{\\mathcal{X}})\\|,\\hat{\\mathcal{X}}\\in\\mathbb{R}^{D}}&{{}(2)}&{}\\\\ {\\mathcal{L}(\\hat{\\mathcal{X}},\\hat{\\mathcal{Y}},\\mathcal{Y})=\\mathcal{L}_{t o x}(\\hat{\\mathbf{b}},\\mathbf{b})+\\mathcal{L}_{c l s}(\\mathcal{U}(\\hat{\\mathcal{X}}),\\hat{\\mathbf{c}},\\mathbf{c})}&{{}(3)}\\end{array}', 'layout': 'double'}, {'layout_bbox': [316.74704, 598.45776, 573.39526, 636.35236], 'text': 'where and y denote the prediction and ground truth.\n= (e, b), C and b represent the category and bounding\nbox respectively, X represent the encoder feature.', 'layout': 'double'}, {'layout_bbox': [315.35437, 638.09393, 572.0008, 724.53687], 'text': 'Effectiveness analysis. To analyze the effectiveness of thc\nuncertainty-minimal query selection, we visualize the clas-\nsification scores and IoU scores of the selected features on\nCOCO va1.2017, Figure 6. We draw the scatterplot with\nclassification scores greater than 0.5. The purple and green\nwith uncertainty-minimal query selection and vanilla query\ndots represent the selected features from the model trained', 'layout': 'double'}], 'doc_preprocessor_res': {'input_path': None, 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': True}, 'angle': 0}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 1, 'label': 'image', 'score': 0.9874590635299683, 'coordinate': [46.905365, 44.05746, 565.6911, 217.74211]}, {'cls_id': 2, 'label': 'text', 'score': 0.9869957566261292, 'coordinate': [41.776196, 418.61166, 296.82672, 554.4149]}, {'cls_id': 2, 'label': 'text', 'score': 0.9792540073394775, 'coordinate': [39.780045, 664.1547, 296.5302, 724.974]}, {'cls_id': 2, 'label': 'text', 'score': 0.9792136549949646, 'coordinate': [316.3008, 289.54156, 573.4635, 415.4659]}, {'cls_id': 2, 'label': 'text', 'score': 0.9789648652076721, 'coordinate': [316.1587, 417.67807, 575.0031, 541.93054]}, {'cls_id': 1, 'label': 'image', 'score': 0.9786934852600098, 'coordinate': [53.227856, 294.16644, 283.854, 396.24164]}, {'cls_id': 2, 'label': 'text', 'score': 0.9765349626541138, 'coordinate': [315.35437, 638.09393, 572.0008, 724.53687]}, {'cls_id': 6, 'label': 'figure_title', 'score': 0.9575827717781067, 'coordinate': [42.178703, 227.34215, 570.1248, 284.377]}, {'cls_id': 2, 'label': 'text', 'score': 0.9554654359817505, 'coordinate': [41.275124, 613.64154, 298.0696, 636.9947]}, {'cls_id': 7, 'label': 'formula', 'score': 0.951255738735199, 'coordinate': [92.092064, 563.1221, 297.57217, 607.2598]}, {'cls_id': 7, 'label': 'formula', 'score': 0.9501133561134338, 'coordinate': [343.82712, 551.06995, 573.45465, 589.9438]}, {'cls_id': 6, 'label': 'figure_title', 'score': 0.9381633400917053, 'coordinate': [99.52045, 401.63477, 240.27536, 411.01334]}, {'cls_id': 2, 'label': 'text', 'score': 0.9283379316329956, 'coordinate': [316.74704, 598.45776, 573.39526, 636.35236]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.9257320761680603, 'coordinate': [41.01993, 645.3085, 253.87302, 656.61505]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[169, 50],
...,
[169, 62]],
...,
[[ 39, 711],
...,
[ 39, 726]]], 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': ['Efficient Hybrid Encoder', 'CaevIst s1', 'CaaveJs2', 'CCFF', '', 'Fusion', '00□0□000000', 'Figure 4, Overview of RT-DETR. We feed the features from the last three stages of the backbone into the encoder. The eficient hybrid', 'encoder transforms multi-scale features into a sequence of image features through the Attention-based Intra-scale Feature Interaction (AIFI)', 'features to serve as initial object queries for the decoder, Finaly, the decoder with auxiliary prediction heads iteratively optimizes object', 'and the CNN-based Cross-scale Feature Fusion (CCFF), Then, the uncertainty-minimal query selection selects a fixed number of encoder', 'queries to generate categories and boxes.', '1x1Conv', 'Fusion', 'The confidence score represents the likelihood that the fea-', 'C', 'ture includes foreground objects. Nevertheless, the detector', 'are required to simultaneously model the category and loca-', 'NX', 'tion of objects, both of which determine the quality of the', '1x1Conv', 'features. Hence, the performance score of the feature is a la-', 'RepBlock', 'tent variable that is jointly correlated with both classification', 'and localization. Based on the analysis, the current query', 'CConcatenate', 'Element-wise add', 'Flatten', 'selection lead to a considerable level of uncertainty in the', 'selected features, resulting in sub-optimal initialization for', 'Figure 5. The fusion block in CCFF', 'the decoder and hindering the performance of the detector.', 'D, Ds, not only significantly reduces latency (35% faster),', 'To address this problem, we propose the uncertainty mini-', 'but also improves accuracy (0.4% AP higher). CCFF is opti-', 'mal query selection scheme, which explicitly constructs and', 'mized based on the cross-scale fusion module, which inserts', 'optimizes the epistemic uncertainty to model the joint latent', 'several fusion blocks consisting of convolutional layers into', 'variable of encoder features, thereby providing high-quality', 'the fusion path. The role of the fusion block is to fuse two', 'queries for the decoder. Specifically, the feature uncertainty', 'adjacent scale features into a new feature, and its structure is', 'L/ is defined as the discrepancy between the predicted dis-', 'illustrated in Figure 5. The fusion block contains two 1 × 1', 'tributions of localization P and classification C in Eq. (2).', 'convolutions to adjust the number of channels, N RepBlocks', 'To minimize the uncertainty of the queries, we integrate', 'composed of RepConv [8] are used for feature fusion, and', 'the uncertainty into the loss function for the gradient-based', 'the two-path outputs are fused by element-wise add. We', 'optimization in Eq. (3).', 'formulate the calculation of the hybrid encoder as:', 'u(x)=P(x）-C(x）,x∈RD', '(2)', '=K =V =F1atten(Ss),', 'F = Reshape(AIFI(Q,K,V)),', '(1)', 'C(x.y)= Lo（b,b）+Cc(u(x）,e,c)（3)', 'O=CCFF（{S,S,F}）', 'where and y denote the prediction and ground truth.', 'where Reshape represents restoring the shape of the flat-', '= (e, b), C and b represent the category and bounding', 'tened feature to the same shape as S5', 'box respectively, X represent the encoder feature.', '4.3.Uncertainty-minimal Ouery Selection', 'Effectiveness analysis. To analyze the effectiveness of thc', 'uncertainty-minimal query selection, we visualize the clas-', 'To reduce the difficulty of optimizing object queries in', 'sification scores and IoU scores of the selected features on', 'DETR, several subsequent works [42, 44, 45] propose query', 'COCO va1.2017, Figure 6. We draw the scatterplot with', 'selection schemes, which have in common that they use the', 'classification scores greater than 0.5. The purple and green', 'confidence score to select the top K features from the en-', 'with uncertainty-minimal query selection and vanilla query', 'dots represent the selected features from the model trained', 'coder to initialize object queries (or just position queries).'], 'rec_scores': array([0.95921248, ..., 0.99757016]), 'rec_polys': array([[[169, 50],
...,
[169, 62]],
...,
[[ 39, 711],
...,
[ 39, 726]]], dtype=int16), 'rec_boxes': array([[169, ..., 62],
...,
[ 39, ..., 726]], dtype=int16)}, 'text_paragraphs_ocr_res': {'rec_polys': array([[[169, 50],
...,
[169, 62]],
...,
[[ 39, 711],
...,
[ 39, 726]]], dtype=int16), 'rec_texts': ['Efficient Hybrid Encoder', 'CaevIst s1', 'CaaveJs2', 'CCFF', '', 'Fusion', '00□0□000000', 'Figure 4, Overview of RT-DETR. We feed the features from the last three stages of the backbone into the encoder. The eficient hybrid', 'encoder transforms multi-scale features into a sequence of image features through the Attention-based Intra-scale Feature Interaction (AIFI)', 'features to serve as initial object queries for the decoder, Finaly, the decoder with auxiliary prediction heads iteratively optimizes object', 'and the CNN-based Cross-scale Feature Fusion (CCFF), Then, the uncertainty-minimal query selection selects a fixed number of encoder', 'queries to generate categories and boxes.', '1x1Conv', 'Fusion', 'The confidence score represents the likelihood that the fea-', 'C', 'ture includes foreground objects. Nevertheless, the detector', 'are required to simultaneously model the category and loca-', 'NX', 'tion of objects, both of which determine the quality of the', '1x1Conv', 'features. Hence, the performance score of the feature is a la-', 'RepBlock', 'tent variable that is jointly correlated with both classification', 'and localization. Based on the analysis, the current query', 'CConcatenate', 'Element-wise add', 'Flatten', 'selection lead to a considerable level of uncertainty in the', 'selected features, resulting in sub-optimal initialization for', 'Figure 5. The fusion block in CCFF', 'the decoder and hindering the performance of the detector.', 'D, Ds, not only significantly reduces latency (35% faster),', 'To address this problem, we propose the uncertainty mini-', 'but also improves accuracy (0.4% AP higher). CCFF is opti-', 'mal query selection scheme, which explicitly constructs and', 'mized based on the cross-scale fusion module, which inserts', 'optimizes the epistemic uncertainty to model the joint latent', 'several fusion blocks consisting of convolutional layers into', 'variable of encoder features, thereby providing high-quality', 'the fusion path. The role of the fusion block is to fuse two', 'queries for the decoder. Specifically, the feature uncertainty', 'adjacent scale features into a new feature, and its structure is', 'L/ is defined as the discrepancy between the predicted dis-', 'illustrated in Figure 5. The fusion block contains two 1 × 1', 'tributions of localization P and classification C in Eq. (2).', 'convolutions to adjust the number of channels, N RepBlocks', 'To minimize the uncertainty of the queries, we integrate', 'composed of RepConv [8] are used for feature fusion, and', 'the uncertainty into the loss function for the gradient-based', 'the two-path outputs are fused by element-wise add. We', 'optimization in Eq. (3).', 'formulate the calculation of the hybrid encoder as:', 'where and y denote the prediction and ground truth.', 'where Reshape represents restoring the shape of the flat-', '= (e, b), C and b represent the category and bounding', 'tened feature to the same shape as S5', 'box respectively, X represent the encoder feature.', '4.3.Uncertainty-minimal Ouery Selection', 'Effectiveness analysis. To analyze the effectiveness of thc', 'uncertainty-minimal query selection, we visualize the clas-', 'To reduce the difficulty of optimizing object queries in', 'sification scores and IoU scores of the selected features on', 'DETR, several subsequent works [42, 44, 45] propose query', 'COCO va1.2017, Figure 6. We draw the scatterplot with', 'selection schemes, which have in common that they use the', 'classification scores greater than 0.5. The purple and green', 'confidence score to select the top K features from the en-', 'with uncertainty-minimal query selection and vanilla query', 'dots represent the selected features from the model trained', 'coder to initialize object queries (or just position queries).'], 'rec_scores': array([0.95921248, ..., 0.99757016]), 'rec_boxes': array([[169, ..., 62],
...,
[ 39, ..., 726]], dtype=int16)}, 'formula_res_list': [{'input_path': None, 'page_index': None, 'rec_formula': '\\begin{array}{r l}{\\mathcal{Q}}&{=\\mathcal{K}=\\mathcal{V}=\\mathtt{F l a t t e r n}(\\mathcal{S}_{5}),}\\\\ {\\mathcal{F}_{5}}&{=\\mathtt{R e s h a p e}(\\mathtt{A I F I}(\\mathcal{Q},\\mathcal{K},\\mathcal{V})),\\qquad\\quad(1)}\\\\ {\\mathcal{O}}&{=\\mathtt{C C F F}(\\{\\mathcal{S}_{3},\\mathcal{S}_{4},\\mathcal{F}_{5}\\}),}\\end{array}', 'formula_region_id': 1, 'dt_polys': [92.092064, 563.1221, 297.57217, 607.2598]}, {'input_path': None, 'page_index': None, 'rec_formula': '\\begin{array}{r l r}{\\mathcal{U}(\\hat{\\mathcal{X}})=\\|\\mathcal{P}(\\hat{\\mathcal{X}})-\\mathcal{C}(\\hat{\\mathcal{X}})\\|,\\hat{\\mathcal{X}}\\in\\mathbb{R}^{D}}&{{}(2)}&{}\\\\ {\\mathcal{L}(\\hat{\\mathcal{X}},\\hat{\\mathcal{Y}},\\mathcal{Y})=\\mathcal{L}_{t o x}(\\hat{\\mathbf{b}},\\mathbf{b})+\\mathcal{L}_{c l s}(\\mathcal{U}(\\hat{\\mathcal{X}}),\\hat{\\mathbf{c}},\\mathbf{c})}&{{}(3)}\\end{array}', 'formula_region_id': 2, 'dt_polys': [343.82712, 551.06995, 573.45465, 589.9438]}]}}
```
=== "Layout Parsing v2"
```bash
paddlex --pipeline layout_parsing_v2 \
--input layout_parsing_v2_demo.png \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--use_textline_orientation False \
--save_path ./output \
--device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'layout_parsing_v2_demo.png', '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]],
...,
[[1154, 1323],
...,
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...,
[1152, ..., 1359]], dtype=int16)}}}
```
=== "Formula Recognition"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'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],)}, ...]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/formula_recognition/04.png"></p>
=== "Seal Text Recognition"
```bash
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
```
??? question "What's the result"
=== "output"
```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': ['天津君和缘商贸有限公司', '发票专用章', '吗繁物', '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)}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/seal_recognition/03.png"></p>
=== "Doc Preprocess"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'doc_test_rotated.jpg', 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': True}, 'angle': 180}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/doc_preprocessor/02.jpg"></p>
!!! example "Computer Vision Pipelines CLI"
=== "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
{'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']}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/image_classification/03.png"></p>
=== "Object Detection"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'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]}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/object_detection/03.png"></p>
=== "Instance Segmentation"
```bash
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 \
```
??? question "What's the result"
=== "output"
```bash
{'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': '...'}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/instance_segmentation/03.png"></p>
=== "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 \
--target_size -1 \
--save_path ./output \
--device gpu:0 \
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'makassaridn-road_demo.png', 'page_index': None, 'pred': '...'}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/semantic_segmentation/03.png"></p>
=== "Image Multi-label Classification"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'test_imgs/general_image_classification_001.jpg', 'page_index': None, 'class_ids': array([21]), 'scores': array([0.99962]), 'label_names': ['bear']}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/image_multi_label_classification/02.png"></p>
=== "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 \
--threshold 0.5 \
--save_path ./output \
--device gpu:0 \
```
??? question "What's the result"
=== "output"
```bash
{'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]}, ...]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/small_object_detection/02.png"></p>
=== "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 --save_path ./output
```
??? question "What's the result"
=== "output"
```bash
{'input_path': 'uad_grid.png', 'pred': '...'}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/image_anomaly_detection/02.png"></p>
=== "3D Multi-modal Fusion Detection"
```bash
paddlex --pipeline 3d_bev_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/data/nuscenes_demo.tar --device gpu:0
```
=== "Human Keypoint Detection"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'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}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/human_keypoint_detection/01.jpg"></p>
=== "Open Vocabulary Segmentation"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'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]}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/open_vocabulary_segmentation/open_vocabulary_segmentation_res.jpg"></p>
=== "Open Vocabulary Detection"
```bash
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
```
??? question "What's the result"
=== "output"
```bash
{'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}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/open_vocabulary_detection/open_vocabulary_detection_res.jpg"></p>
=== "Pedestrian Attribute Recognition"
```bash
paddlex --pipeline pedestrian_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'pedestrian_attribute_002.jpg', 'boxes': [{'labels': ['Trousers(长裤)', 'Age18-60(年龄在18-60岁之间)', 'LongCoat(长外套)', 'Side(侧面)'], '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(长裤)', 'LongCoat(长外套)', 'Front(面朝前)', 'Age18-60(年龄在18-60岁之间)'], '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(长裤)', 'LongCoat(长外套)', 'Age18-60(年龄在18-60岁之间)', 'Side(侧面)'], 'cls_scores': array([0.99996, 0.99514, 0.98726, 0.96224]), 'det_score': 0.9645745754241943, 'coordinate': [399.45944, 281.9107, 869.5312, 1038.9962]}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/pedestrian_attribute_recognition/01.jpg"></p>
=== "Vehicle Attribute Recognition"
```bash
paddlex --pipeline vehicle_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --device gpu:0
```
??? question "What's the result"
=== "output"
```bash
{'res': {'input_path': 'vehicle_attribute_002.jpg', 'boxes': [{'labels': ['red(红色)', 'sedan(轿车)'], 'cls_scores': array([0.96375, 0.94025]), 'det_score': 0.9774094820022583, 'coordinate': [196.32553, 302.3847, 639.3131, 655.57904]}, {'labels': ['suv(SUV)', 'brown(棕色)'], 'cls_scores': array([0.99968, 0.99317]), 'det_score': 0.9705657958984375, 'coordinate': [769.4419, 278.8417, 1401.0217, 641.3569]}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/pipelines/vehicle_attribute_recognition/01.jpg"></p>
=== "Rotated Object Detection"
```bash
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 \
```
??? question "What's the result"
=== "output"
```bash
{'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]}]}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/modules/rotated_object_detection/rotated_object_detection_001_res.png"></p>
!!! example "Time Series-related CLI"
=== "Time Series Forecasting"
```bash
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
```
??? 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_anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0 --save_path ./output
```
??? 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}
```
!!! example "Speech-related Pipelines CLI"
=== "Multilingual Speech Recognition"
```bash
paddlex --pipeline multilingual_speech_recognition \
--input https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav \
--save_path ./output \
--device gpu:0
```
??? question "What's the result"
```bash
{'input_path': 'zh.wav', 'result': {'text': '我认为跑步最重要的就是给我带来了身体健康', 'segments': [{'id': 0, 'seek': 0, 'start': 0.0, 'end': 2.0, 'text': '我认为跑步最重要的就是', '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': '给我带来了身体健康', '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'}}
```
!!! example "Video-related Pipelines CLI"
=== "Video Classification"
```bash
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
```
??? question "What's the result"
```bash
{'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']}}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/video_classification/02.jpg"></p>
=== "Video Detection"
```bash
paddlex --pipeline video_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/HorseRiding.avi --device gpu:0 --save_path output
```
??? question "What's the result"
```bash
{'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']]]}
```
=== "img"
<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/video_detection/HorseRiding_res.jpg"></p>
📝 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
predictmethod of the pipeline 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(
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/")
```
=== "Table Recognition"
```python
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/")
```
=== "Table Recognition v2"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="table_recognition_v2")
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/")
```
=== "General Layout Parsing"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="layout_parsing")
output = pipeline.predict(
input="./demo_paper.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
```
=== "General Layout Parsing v2"
```python
from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="layout_parsing_v2")
output = pipeline.predict(
input="./layout_parsing_v2_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")
```
=== "Formula Recognition"
```python
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/")
```
=== "Seal Text Recognition"
```python
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
```
=== "Doc Preprocessor"
```python
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/")
```
!!! example "Computer Vision Pipeline Command-Line Usage"
=== "General 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() ## 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
```
=== "General Object Detection"
```python
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/")
```
=== "General Instance Segmentation"
```python
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/")
```
=== "General Semantic Segmentation"
```python
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/")
```
=== "Image Multi-label Classification"
```python
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
```
=== "Small Object Detection"
```python
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/")
```
=== "Image Anomaly Detection"
```python
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
```
=== "3D Multi-modal Fusion Detection"
```python
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
```
=== "Human Keypoint Detection"
```python
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/")
```
=== "Open-Vocabulary Segmentation"
```python
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/")
```
=== "Open-Vocabulary Detection"
```python
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/")
```
=== "Pedestrian Attribute Recognition"
```python
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
```
=== "Vehicle Attribute Recognition"
```python
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
```
=== "Rotated Object Detection"
```python
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/")
```
!!! example "Command Line Usage for Time Series pipelines"
=== "Time Series Forecasting"
```python
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
```
=== "Time Series Anomaly Detection"
```python
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
```
=== "Time Series Classification"
```python
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
```
!!! example "Command Line Usage for Speech pipelines"
=== "Multilingual Speech Recognition"
```python
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/")
```
!!! example "Command Line Usage for Video pipelines"
=== "General Video Classification"
```python
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/")
```
=== "General Video Detection"
```python
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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.en.md)
- **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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/ocr_pipelines/OCR.en.md)
- **General Layout Parsing v2**
---
The General Layout Parsing v2 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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/ocr_pipelines/layout_parsing_v2.en.md)
- **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.
[:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/cv_pipelines/table_recognition_v2.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
💬 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.