update docs (#2542)

* update docs

* update docs

* update docs

* update docs

.github/workflows/deploy_docs.yml

@@ -1,32 +0,0 @@
-name: Develop Docs
-on:
-  push:
-    branches: #设置更新哪个分支会更新站点
-      - develop
-      - release/3.0-beta2
-permissions:
-  contents: write
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - name: Configure Git Credentials
-        run: |
-          git config user.name github-actions[bot]
-          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
-      - uses: actions/setup-python@v5
-        with:
-          python-version: 3.x
-      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
-      - uses: actions/cache@v4
-        with:
-          key: mkdocs-material-${{ env.cache_id }}
-          path: .cache
-          restore-keys: |
-            mkdocs-material-
-      - run: pip install mike mkdocs-material jieba mkdocs-git-revision-date-localized-plugin mkdocs-git-committers-plugin-2 mkdocs-git-authors-plugin mkdocs-static-i18n mkdocs-minify-plugin 
-      - run: |
-          git fetch origin gh-pages --depth=1
-          mike deploy --push --update-aliases main latest
-          mike set-default --push latest

docs/pipeline_usage/pipeline_develop_guide.en.md

@@ -186,67 +186,83 @@ Choose the appropriate deployment method for your model pipeline based on your n
 <tbody>
 <tr>
 <td>PP-ChatOCR-doc v3</td>
-<td><a href="./tutorials/information_extraction_pipelines/document_scene_information_extraction.en.md">PP-ChatOCR-doc v3 Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">PP-ChatOCR-doc v3 Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Image Classification</td>
-<td><a href="./tutorials/cv_pipelines/image_classification.en.md">Image Classification Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_classification.html">Image Classification Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Object Detection</td>
-<td><a href="./tutorials/cv_pipelines/object_detection.en.md">Object Detection Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/object_detection.html">Object Detection Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Instance Segmentation</td>
-<td><a href="./tutorials/cv_pipelines/instance_segmentation.en.md">Instance Segmentation Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">Instance Segmentation Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Semantic Segmentation</td>
-<td><a href="./tutorials/cv_pipelines/semantic_segmentation.en.md">Semantic Segmentation Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">Semantic Segmentation Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Image Multi-label Classification</td>
-<td><a href="./tutorials/cv_pipelines/image_multi_label_classification.en.md">Image Multi-label Classification Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">Image Multi-label Classification Pipeline Usage Tutorial</a></td>
+</tr>
+<tr>
+<td>Image Recognition</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html">Image Recognition Pipeline Usage Tutorial</a></td>
+</tr>
+<tr>
+<td>Pedestrian Attribute Recognition</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute.html">Pedestrian Attribute Recognition Pipeline Usage Tutorial</a></td>
+</tr>
+<tr>
+<td>Vehicle Attribute Recognition</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html">Vehicle Attribute Recognition Pipeline Usage Tutorial</a></td>
+</tr>
+<tr>
+<td>Face Recognition</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">Face Recognition Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Small Object Detection</td>
-<td><a href="./tutorials/cv_pipelines/small_object_detection.en.md">Small Object Detection Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">Small Object Detection Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Image Anomaly Detection</td>
-<td><a href="./tutorials/cv_pipelines/image_anomaly_detection.en.md">Image Anomaly Detection Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html">Image Anomaly Detection Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>OCR</td>
-<td><a href="./tutorials/ocr_pipelines/OCR.en.md">OCR Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html">OCR Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Table Recognition</td>
-<td><a href="./tutorials/ocr_pipelines/table_recognition.en.md">Table Recognition Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html">Table Recognition Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Layout Parsing</td>
-<td><a href="./tutorials/ocr_pipelines/layout_parsing.en.md">Layout Parsing Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">Layout Parsing Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Formula Recognition</td>
-<td><a href="./tutorials/ocr_pipelines/formula_recognition.en.md">Formula Recognition Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">Formula Recognition Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Seal Recognition</td>
-<td><a href="./tutorials/ocr_pipelines/seal_recognition.en.md">Seal Recognition Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">Seal Recognition Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Time Series Forecasting</td>
-<td><a href="./tutorials/time_series_pipelines/time_series_forecasting.en.md">Time Series Forecasting Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">Time Series Forecasting Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Time Series Anomaly Detection</td>
-<td><a href="./tutorials/time_series_pipelines/time_series_anomaly_detection.en.md">Time Series Anomaly Detection Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">Time Series Anomaly Detection Pipeline Usage Tutorial</a></td>
 </tr>
 <tr>
 <td>Time Series Classification</td>
-<td><a href="./tutorials/time_series_pipelines/time_series_classification.en.md">Time Series Classification Pipeline Usage Tutorial</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">Time Series Classification Pipeline Usage Tutorial</a></td>
 </tr>
 </tbody>
 </table>

docs/pipeline_usage/pipeline_develop_guide.md

@@ -188,67 +188,83 @@ Pipeline:
 <tbody>
 <tr>
 <td>文档场景信息抽取v3</td>
-<td><a href="./tutorials/information_extraction_pipelines/document_scene_information_extraction.md">文档场景信息抽取v3产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v3产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用图像分类</td>
-<td><a href="./tutorials/cv_pipelines/image_classification.md">通用图像分类产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html">通用图像分类产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用目标检测</td>
-<td><a href="./tutorials/cv_pipelines/object_detection.md">通用目标检测产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html">通用目标检测产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用实例分割</td>
-<td><a href="./tutorials/cv_pipelines/instance_segmentation.md">通用实例分割产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">通用实例分割产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用语义分割</td>
-<td><a href="./tutorials/cv_pipelines/semantic_segmentation.md">通用语义分割产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">通用语义分割产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用图像多标签分类</td>
-<td><a href="./tutorials/cv_pipelines/image_multi_label_classification.md">通用图像多标签分类产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">通用图像多标签分类产线使用教程</a></td>
+</tr>
+<tr>
+<td>通用图像识别</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html">通用图像识别产线使用教程</a></td>
+</tr>
+<tr>
+<td>行人属性识别</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.html">行人属性识别产线使用教程</a></td>
+</tr>
+<tr>
+<td>车辆属性识别</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.html">车辆属性识别产线使用教程</a></td>
+</tr>
+<tr>
+<td>人脸识别</td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/face_recognition.html">人脸识别产线使用教程</a></td>
 </tr>
 <tr>
 <td>小目标检测</td>
-<td><a href="./tutorials/cv_pipelines/small_object_detection.md">小目标检测产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">小目标检测产线使用教程</a></td>
 </tr>
 <tr>
 <td>图像异常检测</td>
-<td><a href="./tutorials/cv_pipelines/image_anomaly_detection.md">图像异常检测产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html">图像异常检测产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用OCR</td>
-<td><a href="./tutorials/ocr_pipelines/OCR.md">通用OCR产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html">通用OCR产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用表格识别</td>
-<td><a href="./tutorials/ocr_pipelines/table_recognition.md">通用表格识别产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html">通用表格识别产线使用教程</a></td>
 </tr>
 <tr>
 <td>通用版面解析</td>
-<td><a href="./tutorials/ocr_pipelines/layout_parsing.md">通用版面解析产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html">通用版面解析产线使用教程</a></td>
 </tr>
 <tr>
 <td>公式识别</td>
-<td><a href="./tutorials/ocr_pipelines/formula_recognition.md">公式识别产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html">公式识别产线使用教程</a></td>
 </tr>
 <tr>
 <td>印章文本识别</td>
-<td><a href="./tutorials/ocr_pipelines/seal_recognition.md">印章文本识别产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html">印章文本识别产线使用教程</a></td>
 </tr>
 <tr>
 <td>时序预测</td>
-<td><a href="./tutorials/time_series_pipelines/time_series_forecasting.md">通用时序预测产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">通用时序预测产线使用教程</a></td>
 </tr>
 <tr>
 <td>时序异常检测</td>
-<td><a href="./tutorials/time_series_pipelines/time_series_anomaly_detection.md">通用时序异常检测产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">通用时序异常检测产线使用教程</a></td>
 </tr>
 <tr>
 <td>时序分类</td>
-<td><a href="./tutorials/time_series_pipelines/time_series_classification.md">通用时序分类产线使用教程</a></td>
+<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">通用时序分类产线使用教程</a></td>
 </tr>
 </tbody>
 </table>

docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.en.md

@@ -64,8 +64,8 @@ When executing the above command, the default image anomaly detection pipeline c
 
 After running, the result is:
 
-```
-{'input_path': 'uad_grid.png'}
+```bash
+{'input_path': 'uad_grid.png', 'pred': '...'}
 ```
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/image_anomaly_detection/02.png">
 

docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.md

@@ -65,8 +65,8 @@ paddlex --pipeline anomaly_detection --input uad_grid.png --device gpu:0
 
 运行后，得到的结果为：
 
-```
-{'input_path': 'uad_grid.png'}
+```bash
+{'input_path': 'uad_grid.png', 'pred': '...'}
 ```
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/image_anomaly_detection/02.png">
 

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.en.md

@@ -239,9 +239,7 @@ When executing the above command, the default semantic segmentation pipeline con
 
 After running, the result is:
 
-```bash
-{'input_path': 'general_object_detection_002.png'}
-```
+{'input_path': 'makassaridn-road_demo.png', 'pred': '...'}
 
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/semantic_segmentation/03.png">
 

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.md

@@ -241,9 +241,7 @@ paddlex --pipeline semantic_segmentation --input makassaridn-road_demo.png --dev
 
 运行后，得到的结果为：
 
-```
-{'input_path': 'general_object_detection_002.png'}
-```
+{'input_path': 'makassaridn-road_demo.png', 'pred': '...'}
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/semantic_segmentation/03.png">
 可视化图片默认不进行保存，您可以通过 `--save_path` 自定义保存路径，随后所有结果将被保存在指定路径下。
 

docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.en.md

@@ -195,113 +195,135 @@ After running, the result obtained is:
 
 <details><summary>  👉 Click to expand</summary>
 
-<pre><code>{'input_path': 'seal_text_det.png', 'layout_result': {'input_path': 'seal_text_det.png', 'boxes': [{'cls_id': 2, 'label': 'seal', 'score': 0.9813116192817688, 'coordinate': [0, 5.2238655, 639.59766, 637.6985]}]}, 'ocr_result': [{'input_path': PosixPath('/root/.paddlex/temp/tmp19fn93y5.png'), 'dt_polys': [array([[468, 469],
-       [472, 469],
-       [477, 471],
-       [507, 501],
-       [509, 505],
-       [509, 509],
-       [508, 513],
-       [506, 514],
-       [456, 553],
-       [454, 555],
-       [391, 581],
-       [388, 581],
-       [309, 590],
-       [306, 590],
-       [234, 577],
-       [232, 577],
-       [172, 548],
-       [170, 546],
-       [121, 504],
-       [118, 501],
-       [118, 496],
-       [119, 492],
-       [121, 490],
-       [152, 463],
-       [156, 461],
-       [160, 461],
-       [164, 463],
-       [202, 495],
-       [252, 518],
-       [311, 530],
-       [371, 522],
-       [425, 501],
-       [464, 471]]), array([[442, 439],
-       [445, 442],
-       [447, 447],
-       [449, 490],
-       [448, 494],
-       [446, 497],
-       [440, 499],
-       [197, 500],
-       [193, 499],
-       [190, 496],
-       [188, 491],
-       [188, 448],
-       [189, 444],
-       [192, 441],
-       [197, 439],
-       [438, 438]]), array([[465, 341],
-       [470, 344],
-       [472, 346],
-       [476, 356],
-       [476, 419],
-       [475, 424],
-       [472, 428],
-       [467, 431],
-       [462, 433],
-       [175, 434],
-       [170, 433],
-       [166, 430],
-       [163, 426],
-       [161, 420],
-       [161, 354],
-       [162, 349],
-       [165, 345],
-       [170, 342],
-       [175, 340],
-       [460, 340]]), array([[326,  34],
-       [481,  85],
-       [485,  88],
-       [488,  90],
-       [584, 220],
-       [586, 225],
-       [587, 229],
-       [589, 378],
-       [588, 383],
-       [585, 388],
-       [581, 391],
-       [576, 393],
-       [570, 392],
-       [507, 373],
-       [502, 371],
-       [498, 367],
-       [496, 359],
-       [494, 255],
-       [423, 162],
-       [322, 129],
-       [246, 151],
-       [205, 169],
-       [144, 252],
-       [139, 360],
-       [137, 365],
-       [134, 369],
-       [128, 373],
-       [ 66, 391],
-       [ 61, 392],
-       [ 56, 390],
-       [ 51, 387],
-       [ 48, 382],
-       [ 47, 377],
-       [ 49, 230],
-       [ 50, 225],
-       [ 52, 221],
-       [149,  89],
-       [153,  86],
-       [157,  84],
-       [318,  34],
-       [322,  33]])], 'dt_scores': [0.9943362380813267, 0.9994290391836306, 0.9945320407374245, 0.9908104427126033], 'rec_text': ['5263647368706', '吗繁物', '发票专用章', '天津君和缘商贸有限公司'], 'rec_score': [0.9921098351478577, 0.997374951839447, 0.9999369382858276, 0.9901710152626038]}]}
+<pre><code>
+{'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}
 </code></pre></details>
 
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/seal_recognition/03.png">

docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.md

@@ -199,113 +199,135 @@ paddlex --pipeline seal_recognition --input seal_text_det.png --device gpu:0 --s
 
 <details><summary> 👉点击展开</summary>
 
-<pre><code>{'input_path': 'seal_text_det.png', 'layout_result': {'input_path': 'seal_text_det.png', 'boxes': [{'cls_id': 2, 'label': 'seal', 'score': 0.9813116192817688, 'coordinate': [0, 5.2238655, 639.59766, 637.6985]}]}, 'ocr_result': [{'input_path': PosixPath('/root/.paddlex/temp/tmp19fn93y5.png'), 'dt_polys': [array([[468, 469],
-       [472, 469],
-       [477, 471],
-       [507, 501],
-       [509, 505],
-       [509, 509],
-       [508, 513],
-       [506, 514],
-       [456, 553],
-       [454, 555],
-       [391, 581],
-       [388, 581],
-       [309, 590],
-       [306, 590],
-       [234, 577],
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-       [164, 463],
-       [202, 495],
-       [252, 518],
-       [311, 530],
-       [371, 522],
-       [425, 501],
-       [464, 471]]), array([[442, 439],
-       [445, 442],
-       [447, 447],
-       [449, 490],
-       [448, 494],
-       [446, 497],
-       [440, 499],
-       [197, 500],
-       [193, 499],
-       [190, 496],
-       [188, 491],
-       [188, 448],
-       [189, 444],
-       [192, 441],
-       [197, 439],
-       [438, 438]]), array([[465, 341],
-       [470, 344],
-       [472, 346],
-       [476, 356],
-       [476, 419],
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-       [472, 428],
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-       [175, 434],
-       [170, 433],
-       [166, 430],
-       [163, 426],
-       [161, 420],
-       [161, 354],
-       [162, 349],
-       [165, 345],
-       [170, 342],
-       [175, 340],
-       [460, 340]]), array([[326,  34],
-       [481,  85],
-       [485,  88],
-       [488,  90],
-       [584, 220],
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-       [587, 229],
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-       [507, 373],
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-       [496, 359],
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-       [423, 162],
-       [322, 129],
-       [246, 151],
-       [205, 169],
-       [144, 252],
-       [139, 360],
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-       [134, 369],
-       [128, 373],
-       [ 66, 391],
-       [ 61, 392],
-       [ 56, 390],
-       [ 51, 387],
-       [ 48, 382],
-       [ 47, 377],
-       [ 49, 230],
-       [ 50, 225],
-       [ 52, 221],
-       [149,  89],
-       [153,  86],
-       [157,  84],
-       [318,  34],
-       [322,  33]])], 'dt_scores': [0.9943362380813267, 0.9994290391836306, 0.9945320407374245, 0.9908104427126033], 'rec_text': ['5263647368706', '吗繁物', '发票专用章', '天津君和缘商贸有限公司'], 'rec_score': [0.9921098351478577, 0.997374951839447, 0.9999369382858276, 0.9901710152626038]}]}
+<pre><code>
+{'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}
 </code></pre></details>
 
 <img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/seal_recognition/03.png">

docs/quick_start.en.md

@@ -1,262 +0,0 @@
----
-comments: true
-typora-copy-images-to: images
-hide:
-  - navigation
-  - toc
----
-
-### 🛠️ Installation
-
-> ❗Before installing PaddleX, please ensure you have a basic <b>Python environment</b> (Note: Currently supports Python 3.8 to Python 3.10, with more Python versions being adapted).
-
-* <b>Installing PaddlePaddle</b>
-
-```bash
-# cpu
-python -m pip install paddlepaddle==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
-
-# gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
-
-# gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
-```
-> ❗For more PaddlePaddle versions, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html).
-
-* <b>Installing PaddleX</b>
-
-```bash
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b2-py3-none-any.whl
-```
-
-> ❗For more installation methods, refer to the [PaddleX Installation Guide](https://paddlepaddle.github.io/PaddleX/latest/en/installation/installation.html).
-
-
-### 💻 CLI Usage
-
-One command can quickly experience the pipeline effect, the unified CLI format is:
-
-```bash
-paddlex --pipeline [Pipeline Name] --input [Input Image] --device [Running Device]
-```
-
-You only need to specify three parameters:
-* `pipeline`: The name of the pipeline
-* `input`: The local path or URL of the input image to be processed
-* `device`: The GPU number used (for example, `gpu:0` means using the 0th GPU), you can also choose to use the CPU (`cpu`)
-
-For example, using the  OCR pipeline:
-```bash
-paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png  --device gpu:0
-```
-
-<pre><code class="language-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]}
-</code></pre>
-<p>The visualization result is as follows:</p>
-<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png"></p>
-
-To use the command line for other pipelines, simply adjust the `pipeline` parameter to the name of the corresponding pipeline. Below are the commands for each pipeline:
-
-
-<table>
-<thead>
-<tr>
-<th>Pipeline Name</th>
-<th>Command</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Image Classification</td>
-<td><code>paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Object Detection</td>
-<td><code>paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Instance Segmentation</td>
-<td><code>paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Semantic Segmentation</td>
-<td><code>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</code></td>
-</tr>
-<tr>
-<td>Image Multi-label Classification</td>
-<td><code>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</code></td>
-</tr>
-<tr>
-<td>Small Object Detection</td>
-<td><code>paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Image Anomaly Detection</td>
-<td><code>paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --device gpu:0 </code></td>
-</tr>
-<tr>
-<td>OCR</td>
-<td><code>paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Table Recognition</td>
-<td><code>paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Layout Parsing</td>
-<td><code>paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Formula Recognition</td>
-<td><code>paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Seal Recognition</td>
-<td><code>paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Time Series Forecasting</td>
-<td><code>paddlex --pipeline ts_fc --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Time Series Anomaly Detection</td>
-<td><code>paddlex --pipeline ts_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0</code></td>
-</tr>
-<tr>
-<td>Time Series Classification</td>
-<td><code>paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0</code></td>
-</tr>
-</tbody>
-</table>
-
-### 📝 Python Script Usage
-
-With just a few lines of code, you can quickly perform inference on a production line. The unified Python script format is as follows:
-```python
-from paddlex import create_pipeline
-
-pipeline = create_pipeline(pipeline=[PipelineName])
-output = pipeline.predict([InputImageName])
-for res in output:
-    res.print()
-    res.save_to_img("./output/")
-    res.save_to_json("./output/")
-```
-The following steps are executed:
-
-* `create_pipeline()` instantiates the production line object.
-* An image is passed in and the `predict` method of the production line object is called for inference and prediction.
-* The prediction results are processed.
-
-For other production lines using the Python script, you only need to adjust the `pipeline` parameter of the `create_pipeline()` method to the corresponding production line name. Below is a list of each production line's corresponding parameter name and detailed usage explanation:
-
-<table>
-<thead>
-<tr>
-<th>Production Line Name</th>
-<th>Corresponding Parameter</th>
-<th>Detailed Explanation</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Document Scene Information Extraction v3</td>
-<td><code>PP-ChatOCRv3-doc</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">Document Scene Information Extraction v3 Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>General Image Classification</td>
-<td><code>image_classification</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_classification.html">General Image Classification Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>General Object Detection</td>
-<td><code>object_detection</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/object_detection.html">General Object Detection Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>General Instance Segmentation</td>
-<td><code>instance_segmentation</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/instance_segmentation.html">General Instance Segmentation Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>General Semantic Segmentation</td>
-<td><code>semantic_segmentation</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/semantic_segmentation.html">General Semantic Segmentation Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Image Multi-label Classification</td>
-<td><code>multi_label_image_classification</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_multi_label_classification.html">Image Multi-label Classification Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Small Object Detection</td>
-<td><code>small_object_detection</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/small_object_detection.html">Small Object Detection Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Image Anomaly Detection</td>
-<td><code>anomaly_detection</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/cv_pipelines/image_anomaly_detection.html">Image Anomaly Detection Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>General OCR</td>
-<td><code>OCR</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/OCR.html">General OCR Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>General Table Recognition</td>
-<td><code>table_recognition</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/table_recognition.html">General Table Recognition Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>General Layout Parsing</td>
-<td><code>layout_parsing</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/layout_parsing.html">General Layout Parsing Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Formula Recognition</td>
-<td><code>formula_recognition</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/formula_recognition.html">Formula Recognition Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Seal Text Recognition</td>
-<td><code>seal_recognition</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/ocr_pipelines/seal_recognition.html">Seal Text Recognition Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Time Series Forecasting</td>
-<td><code>ts_fc</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_forecasting.html">Time Series Forecasting Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Time Series Anomaly Detection</td>
-<td><code>ts_ad</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_anomaly_detection.html">Time Series Anomaly Detection Python Script Instructions</a></td>
-</tr>
-<tr>
-<td>Time Series Classification</td>
-<td><code>ts_cls</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/tutorials/time_series_pipelines/time_series_classification.html">Time Series Classification Python Script Instructions</a></td>
-</tr>
-</tbody>
-</table>

docs/quick_start.md

@@ -1,264 +0,0 @@
----
-comments: true
-typora-copy-images-to: images
-hide:
-  - navigation
-  - toc
----
-
-### 🛠️ 安装
-
-> ❗安装 PaddleX 前请先确保您有基础的 <b>Python 运行环境</b>（注：当前支持Python 3.8 ～ Python 3.10下运行，更多Python版本适配中）。
-
-* <b>安装 PaddlePaddle</b>
-```bash
-# cpu
-python -m pip install paddlepaddle==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
-
-# gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
-
-# gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
-```
-> ❗ 更多飞桨 Wheel 版本请参考[飞桨官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html)。
-
-
-* <b>安装PaddleX</b>
-
-```bash
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b2-py3-none-any.whl
-```
-
-> ❗ 更多安装方式参考 [PaddleX 安装教程](https://paddlepaddle.github.io/PaddleX/latest/installation/installation.html)
-
-### 💻 命令行使用
-
-一行命令即可快速体验产线效果，统一的命令行格式为：
-
-```bash
-paddlex --pipeline [产线名称] --input [输入图片] --device [运行设备]
-```
-
-只需指定三个参数：
-
-* `pipeline`：产线名称
-* `input`：待处理的输入文件（如图片）的本地路径或 URL
-* `device`: 使用的 GPU 序号（例如`gpu:0`表示使用第 0 块 GPU），也可选择使用 CPU（`cpu`）
-
-
-以通用 OCR 产线为例：
-```bash
-paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0
-```
-运行结果如下：
-
-<pre><code class="language-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]}
-</code></pre>
-
-<p>可视化结果如下：</p>
-<p><img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png"></p></
-
-其他产线的命令行使用，只需将 `pipeline` 参数调整为相应产线的名称。下面列出了每个产线对应的命令：
-
-<table>
-<thead>
-<tr>
-<th>产线名称</th>
-<th>使用命令</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>通用图像分类</td>
-<td><code>paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0</code></td>
-</tr>
-<tr>
-<td>通用目标检测</td>
-<td><code>paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>通用实例分割</td>
-<td><code>paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>通用语义分割</td>
-<td><code>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</code></td>
-</tr>
-<tr>
-<td>图像多标签分类</td>
-<td><code>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</code></td>
-</tr>
-<tr>
-<td>小目标检测</td>
-<td><code>paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --device gpu:0</code></td>
-</tr>
-<tr>
-<td>图像异常检测</td>
-<td><code>paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --device gpu:0 </code></td>
-</tr>
-<tr>
-<td>通用OCR</td>
-<td><code>paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>通用表格识别</td>
-<td><code>paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0</code></td>
-</tr>
-<tr>
-<td>通用版面解析</td>
-<td><code>paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>公式识别</td>
-<td><code>paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>印章文本识别</td>
-<td><code>paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0</code></td>
-</tr>
-<tr>
-<td>时序预测</td>
-<td><code>paddlex --pipeline ts_fc --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0</code></td>
-</tr>
-<tr>
-<td>时序异常检测</td>
-<td><code>paddlex --pipeline ts_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0</code></td>
-</tr>
-<tr>
-<td>时序分类</td>
-<td><code>paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0</code></td>
-</tr>
-</tbody>
-</table>
-
-### 📝 Python 脚本使用
-
-几行代码即可完成产线的快速推理，统一的 Python 脚本格式如下：
-```python
-from paddlex import create_pipeline
-
-pipeline = create_pipeline(pipeline=[产线名称])
-output = pipeline.predict([输入图片名称])
-for res in output:
-    res.print()
-    res.save_to_img("./output/")
-    res.save_to_json("./output/")
-```
-执行了如下几个步骤：
-
-* `create_pipeline()` 实例化产线对象
-* 传入图片并调用产线对象的 `predict` 方法进行推理预测
-* 对预测结果进行处理
-
-其他产线的 Python 脚本使用，只需将 `create_pipeline()` 方法的 `pipeline` 参数调整为相应产线的名称。下面列出了每个产线对应的参数名称及详细的使用解释：
-
-<table>
-<thead>
-<tr>
-<th>产线名称</th>
-<th>对应参数</th>
-<th>详细说明</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>文档场景信息抽取v3</td>
-<td><code>PP-ChatOCRv3-doc</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html">文档场景信息抽取v3产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>通用图像分类</td>
-<td><code>image_classification</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html">通用图像分类产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>通用目标检测</td>
-<td><code>object_detection</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html">通用目标检测产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>通用实例分割</td>
-<td><code>instance_segmentation</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html">通用实例分割产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>通用语义分割</td>
-<td><code>semantic_segmentation</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html">通用语义分割产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>图像多标签分类</td>
-<td><code>multi_label_image_classification</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html">图像多标签分类产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>小目标检测</td>
-<td><code>small_object_detection</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html">小目标检测产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>图像异常检测</td>
-<td><code>anomaly_detection</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html#22-python脚本方式集成">图像异常检测产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>通用OCR</td>
-<td><code>OCR</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html">通用OCR产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>通用表格识别</td>
-<td><code>table_recognition</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html#22-python脚本方式集成">通用表格识别产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>通用版面解析</td>
-<td><code>layout_parsing</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html#22-python脚本方式集成">通用版面解析产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>公式识别</td>
-<td><code>formula_recognition</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python脚本方式集成">公式识别产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>印章文本识别</td>
-<td><code>seal_recognition</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python脚本方式集成">印章文本识别产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>时序预测</td>
-<td><code>ts_fc</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html">时序预测产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>时序异常检测</td>
-<td><code>ts_ad</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html">时序异常检测产线Python脚本使用说明</a></td>
-</tr>
-<tr>
-<td>时序分类</td>
-<td><code>ts_cls</code></td>
-<td><a href="https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html">时序分类产线Python脚本使用说明</a></td>
-</tr>
-</tbody>
-</table>

mkdocs.yml

@@ -212,7 +212,7 @@ plugins:
             FAQ: FAQ
             近期更新: Recently Update
       repository: PaddlePaddle/PaddleX #仓库名称
-      branch: release/3.0-beta1 #仓库分支
+      branch: release/3.0-beta2 #仓库分支
   - git-revision-date-localized: #显示更新时间
       enable_creation_date: true
 
@@ -291,7 +291,6 @@ markdown_extensions:
 # 页面结构
 nav:
   - Home: index.md
-  - 快速开始: quick_start.md
   - 安装:
        - 安装PaddlePaddle: installation/paddlepaddle_install.md
        - 安装PaddleX: installation/installation.md