docs: fix outdated APIs, config paths and broken links across docs

docs/CHANGELOG.en.md

@@ -120,7 +120,7 @@ PaddleX 3.0 rc0 is fully compatible with PaddlePaddle 3.0rc0 version, adding 10+
   - Optimized user experience, with changes to some model and pipeline interfaces. For details, refer to the [API Upgrade Document](API_change_log/v3.0.0rc.md).
 
 - <b>Multi-hardware support:</b>
-  - Added model training and inference capabilities for the Suiyuan GCU hardware, supporting 90+ models, [GCU Model List](support_list/model_list_gcu.md)
+  - Added model training and inference capabilities for the Suiyuan GCU hardware, supporting 90+ models, [GCU Model List](support_list/model_list_gcu.en.md)
   - Added 50+ models for Ascend NPU, [NPU Model List](support_list/model_list_npu.en.md)
   - Added 10+ models for Kunlunxin XPU, [XPU Model List](support_list/model_list_xpu.en.md)
   - Added 10+ models for Cambricon MLU, [MLU Model List](support_list/model_list_mlu.en.md)
@@ -147,7 +147,7 @@ PaddleX 3.0 Beta2 is fully compatible with the PaddlePaddle 3.0b2 version. <b>Th
 - <b>New Pipelines:</b>
   - <b>[General Image Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.en.md):</b> Introducing a powerful pipeline with enhanced feature extraction models. This allows for user-defined image database recognition of unknown categories, providing more customizable recognition options compared to existing open-domain object detection. Supports high-performance inference and service deployment.
   - <b>[Face Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.en.md):</b> This new pipeline enables the addition and removal of entries in the face database, with robust support for high-performance inference and service deployment.
-  - <b>[Vehicle Attribute Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.md):</b> Detect and recognize vehicle attributes such as color and model in images. Supports high-performance inference and service deployment.
+  - <b>[Vehicle Attribute Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.en.md):</b> Detect and recognize vehicle attributes such as color and model in images. Supports high-performance inference and service deployment.
   - <b>[Pedestrian Attribute Recognition](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta2/docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.en.md):</b> Detect and recognize pedestrian attributes such as age, gender, and clothing in images. Supports high-performance inference and service deployment.
 
 - <b>New Capabilities:</b>

docs/VisualDL.en.md

@@ -18,7 +18,6 @@ VisualDL natively supports the use of Python. Developers can retrieve plentiful
 * [Function Preview](#Function-Preview)
 * [Contribution](#Contribution)
 * [More Details](#More-Details)
-* [Technical Communication](#Technical-Communication)
 
 
 

docs/data_annotations/cv_modules/image_feature.en.md

@@ -79,7 +79,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/image_classification_labelme_examples.tar -P ./dataset
 tar -xf ./dataset/image_classification_labelme_examples.tar -C ./dataset/
 # Convert the LabelMe example dataset
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/modules/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/image_classification_labelme_examples \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/cv_modules/image_feature.md

@@ -76,7 +76,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/image_classification_labelme_examples.tar -P ./dataset
 tar -xf ./dataset/image_classification_labelme_examples.tar -C ./dataset/
 #将 LabelMe 示例数据集进行转换
-python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml \
+python main.py -c paddlex/configs/modules/image_feature/PP-ShiTuV2_rec.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/image_classification_labelme_examples \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/cv_modules/ml_classification.en.md

@@ -98,7 +98,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_labelme_examples.tar -P ./dataset
 tar -xf ./dataset/det_labelme_examples.tar -C ./dataset/
 
-python main.py -c paddlex/configs/obeject_detection/PicoDet-L.yaml \
+python main.py -c paddlex/configs/modules/object_detection/PicoDet-L.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_labelme_examples \
     -o CheckDataset.convert.enable=True \
@@ -195,7 +195,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_coco_examples.tar -P ./dataset
 tar -xf ./dataset/det_coco_examples.tar -C ./dataset/
 # Convert the COCO example dataset to MLClsDataset
-python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+python main.py -c paddlex/configs/modules/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_coco_examples \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/cv_modules/ml_classification.md

@@ -93,7 +93,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_labelme_examples.tar -P ./dataset
 tar -xf ./dataset/det_labelme_examples.tar -C ./dataset/
 
-python main.py -c paddlex/configs/object_detection/PicoDet-L.yaml \
+python main.py -c paddlex/configs/modules/object_detection/PicoDet-L.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_labelme_examples \
     -o CheckDataset.convert.enable=True \
@@ -190,7 +190,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_coco_examples.tar -P ./dataset
 tar -xf ./dataset/det_coco_examples.tar -C ./dataset/
 #将COCO示例数据集转化为MLClsDataset数据集
-python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yaml \
+python main.py -c paddlex/configs/modules/image_multilabel_classification/PP-LCNet_x1_0_ML.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_coco_examples \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/cv_modules/object_detection.en.md

@@ -90,7 +90,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_labelme_examples.tar -P ./dataset
 tar -xf ./dataset/det_labelme_examples.tar -C ./dataset/
 
-python main.py -c paddlex/configs/obeject_detection/PicoDet-L.yaml \
+python main.py -c paddlex/configs/modules/object_detection/PicoDet-L.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_labelme_examples \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/cv_modules/object_detection.md

@@ -85,7 +85,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_labelme_examples.tar -P ./dataset
 tar -xf ./dataset/det_labelme_examples.tar -C ./dataset/
 
-python main.py -c paddlex/configs/object_detection/PicoDet-L.yaml \
+python main.py -c paddlex/configs/modules/object_detection/PicoDet-L.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/det_labelme_examples \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/cv_modules/semantic_segmentation.en.md

@@ -111,7 +111,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/seg_dataset_to_convert.tar -P ./dataset
 tar -xf ./dataset/seg_dataset_to_convert.tar -C ./dataset/
 
-python main.py -c paddlex/configs/semantic_segmentation/PP-LiteSeg-T.yaml \
+python main.py -c paddlex/configs/modules/semantic_segmentation/PP-LiteSeg-T.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/seg_dataset_to_convert \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/cv_modules/semantic_segmentation.md

@@ -112,7 +112,7 @@ cd /path/to/paddlex
 wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/seg_dataset_to_convert.tar -P ./dataset
 tar -xf ./dataset/seg_dataset_to_convert.tar -C ./dataset/
 
-python main.py -c paddlex/configs/semantic_segmentation/PP-LiteSeg-T.yaml \
+python main.py -c paddlex/configs/modules/semantic_segmentation/PP-LiteSeg-T.yaml \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/seg_dataset_to_convert \
     -o CheckDataset.convert.enable=True \

docs/data_annotations/video_modules/video_detection.md

@@ -22,7 +22,7 @@ dataset_dir  # 数据集根目录，目录名称可以改变
 
 ### 1.2 视频数据转换
 
-原始视频数据需要转换成图像帧，可以使用[convert_video_to_images.py]（https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/applications/video_det_dataset_prepare/convert_video_to_images.py）脚本，执行以下命令：
+原始视频数据需要转换成图像帧，可以使用[convert_video_to_images.py](https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/applications/video_det_dataset_prepare/convert_video_to_images.py)脚本，执行以下命令：
 
 ```bash
 python convert_video_to_images.py dataset_dir dataset_dir/rgb-images
@@ -36,7 +36,7 @@ python convert_video_to_images.py dataset_dir dataset_dir/rgb-images
 
 * 标注可以参考[目标检测标注文档](../cv_modules/object_detection.md)，使用`Labelme`或者 `PaddleLabel` 把每张图的待检测框标注出来, 并存为coco.json的数据格式。
 
-* 标注完成后，需要将标注文件整理为下面的2.数据格式中要求的格式，使用 [convert_coco_to_txt.py]（https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/applications/video_det_dataset_prepare/convert_coco_to_txt.py）脚本，将coco.json的标注文件转为txt格式, 放在`labels`目录下，每个标注文件对应一个视频的图像帧。
+* 标注完成后，需要将标注文件整理为下面的2.数据格式中要求的格式，使用 [convert_coco_to_txt.py](https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/applications/video_det_dataset_prepare/convert_coco_to_txt.py)脚本，将coco.json的标注文件转为txt格式, 放在`labels`目录下，每个标注文件对应一个视频的图像帧。
 
 
 ```bash

docs/index.en.md

@@ -1060,7 +1060,7 @@ Each pipeline in PaddleX corresponds to specific parameters. You can find detail
     === "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
+        paddlex --pipeline ts_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0
         ```
 
         ??? question "What's the result"
@@ -1170,7 +1170,7 @@ The following steps were executed:
         ```python
         from paddlex import create_pipeline
 
-        pipeline = create_pipeline(pipeline_name="table_recognition")
+        pipeline = create_pipeline(pipeline="table_recognition")
 
         output = pipeline.predict(
             input="table_recognition.jpg",
@@ -1191,7 +1191,7 @@ The following steps were executed:
         ```python
         from paddlex import create_pipeline
 
-        pipeline = create_pipeline(pipeline_name="table_recognition_v2")
+        pipeline = create_pipeline(pipeline="table_recognition_v2")
 
         output = pipeline.predict(
             input="table_recognition_v2.jpg",
@@ -1340,7 +1340,7 @@ The following steps were executed:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="instance_segmentation")
+        pipeline = create_pipeline(pipeline="instance_segmentation")
         output = pipeline.predict(input="general_instance_segmentation_004.png", threshold=0.5)
         for res in output:
             res.print()
@@ -1352,7 +1352,7 @@ The following steps were executed:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="semantic_segmentation")
+        pipeline = create_pipeline(pipeline="semantic_segmentation")
         output = pipeline.predict(input="general_semantic_segmentation_002.png", target_size = -1)
         for res in output:
             res.print()
@@ -1378,7 +1378,7 @@ The following steps were executed:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="small_object_detection")
+        pipeline = create_pipeline(pipeline="small_object_detection")
         output = pipeline.predict(input="small_object_detection.jpg", threshold=0.5)
         for res in output:
             res.print()
@@ -1430,7 +1430,7 @@ The following steps were executed:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="open_vocabulary_segmentation")
+        pipeline = create_pipeline(pipeline="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()
@@ -1442,7 +1442,7 @@ The following steps were executed:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="open_vocabulary_detection")
+        pipeline = create_pipeline(pipeline="open_vocabulary_detection")
         output = pipeline.predict(input="open_vocabulary_detection.jpg", prompt="bus . walking man . rearview mirror .")
         for res in output:
             res.print()
@@ -1482,7 +1482,7 @@ The following steps were executed:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="rotated_object_detection")
+        pipeline = create_pipeline(pipeline="rotated_object_detection")
         output = pipeline.predict(input="rotated_object_detection_001.png", threshold=0.5)
         for res in output:
             res.print()
@@ -1629,7 +1629,7 @@ The following steps were executed:
 
     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)
+    [:octicons-arrow-right-24: Tutorial](pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.en.md)
 
 - **Small Object Detection**
 

docs/index.md

@@ -850,7 +850,7 @@ PaddleX的每一条产线对应特定的参数，您可以在各自的产线文
     === "时序分类"
 
         ```bash
-        paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0
+        paddlex --pipeline ts_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0
         ```
 
         ??? question "查看运行结果"
@@ -1089,7 +1089,7 @@ for res in output:
         from paddlex import create_pipeline
         pipeline = create_pipeline(pipeline="doc_preprocessor")
         output = pipeline.predict(
-            input="doc_test_rotated.jpg"
+            input="doc_test_rotated.jpg",
             use_doc_orientation_classify=True,
             use_doc_unwarping=True,
         )
@@ -1133,7 +1133,7 @@ for res in output:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="instance_segmentation")
+        pipeline = create_pipeline(pipeline="instance_segmentation")
         output = pipeline.predict(input="general_instance_segmentation_004.png", threshold=0.5)
         for res in output:
             res.print()
@@ -1145,7 +1145,7 @@ for res in output:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="semantic_segmentation")
+        pipeline = create_pipeline(pipeline="semantic_segmentation")
         output = pipeline.predict(input="general_semantic_segmentation_002.png", target_size=-1)
         for res in output:
             res.print()
@@ -1171,7 +1171,7 @@ for res in output:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="small_object_detection")
+        pipeline = create_pipeline(pipeline="small_object_detection")
         output = pipeline.predict(input="small_object_detection.jpg", threshold=0.5)
         for res in output:
             res.print()
@@ -1212,7 +1212,7 @@ for res in output:
 
         pipeline = create_pipeline(pipeline="human_keypoint_detection")
 
-        output = pipeline.predict("keypoint_detection_001.jpg"， det_threshold=0.5)
+        output = pipeline.predict("keypoint_detection_001.jpg", det_threshold=0.5)
         for res in output:
             res.print()
             res.save_to_img("./output/")
@@ -1223,7 +1223,7 @@ for res in output:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="open_vocabulary_segmentation")
+        pipeline = create_pipeline(pipeline="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()
@@ -1235,7 +1235,7 @@ for res in output:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="open_vocabulary_detection")
+        pipeline = create_pipeline(pipeline="open_vocabulary_detection")
         output = pipeline.predict(input="open_vocabulary_detection.jpg", prompt="bus . walking man . rearview mirror .")
         for res in output:
             res.print()
@@ -1275,7 +1275,7 @@ for res in output:
 
         ```python
         from paddlex import create_pipeline
-        pipeline = create_pipeline(pipeline_name="rotated_object_detection")
+        pipeline = create_pipeline(pipeline="rotated_object_detection")
         output = pipeline.predict(input="rotated_object_detection_001.png", threshold=0.5)
         for res in output:
             res.print()
@@ -1421,7 +1421,7 @@ for res in output:
 
     通用表格识别产线v2用于解决表格识别任务，对图片中的表格进行识别，并以HTML格式输出。基于本产线，可实现对表格的精准预测，使用场景覆盖通用、制造、金融、交通等各个领域。
 
-    [:octicons-arrow-right-24: 教程](pipeline_usage/tutorials/cv_pipelines/table_recognition_v2.md)
+    [:octicons-arrow-right-24: 教程](pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.md)
 
 - **小目标检测**
 

docs/module_usage/instructions/config_parameters_common.en.md

@@ -25,7 +25,7 @@ comments: true
 <tr>
 <td>mode</td>
 <td>str</td>
-<td>Specifies the mode (check_dataset/train/evaluate/export/predict)</td>
+<td>Specifies the mode (check_dataset/train/evaluate/export/predict/pdparams2safetensors)</td>
 <td>check_dataset</td>
 </tr>
 <tr>
@@ -263,8 +263,8 @@ comments: true
 <tr>
 <td>kernel_option</td>
 <td>dict</td>
-<td>Path to the prediction input</td>
 <td>Inference engine setting, such as: "run_mode: paddle"</td>
+<td></td>
 </tr>
 
 <tr>

docs/module_usage/instructions/distributed_training.en.md

@@ -16,7 +16,7 @@ Distributed training refers to splitting a training task across multiple computi
 ```
 python main.py -c paddlex/configs/modules/image_classification/PP-LCNet_x1_0.yaml  \
     -o Global.mode=train \
-    -o Global.dataset_dir=./dataset/cls_flowers_examples
+    -o Global.dataset_dir=./dataset/cls_flowers_examples \
     -o Train.dist_ips="xx.xx.xx.xx,xx.xx.xx.xx"
 ```
 **Note**：

docs/module_usage/instructions/distributed_training.md

@@ -16,7 +16,7 @@ comments: true
 ```
 python main.py -c paddlex/configs/modules/image_classification/PP-LCNet_x1_0.yaml  \
     -o Global.mode=train \
-    -o Global.dataset_dir=./dataset/cls_flowers_examples
+    -o Global.dataset_dir=./dataset/cls_flowers_examples \
     -o Train.dist_ips="xx.xx.xx.xx,xx.xx.xx.xx"
 ```
 **注**：