fix doc_seg (#2390)

docs/module_usage/tutorials/cv_modules/semantic_segmentation.md

@@ -26,7 +26,7 @@
 |OCRNet_HRNet-W18|80.67|48.2335|906.385|43.1 M|
 |OCRNet_HRNet-W48|82.15|78.9976|2226.95|249.8 M|
 |PP-LiteSeg-T|73.10|7.6827|138.683|28.5 M|
-|PP-LiteSeg-B|75.25|-|-|47.0 M|
+|PP-LiteSeg-B|75.25|10.9935|194.727|47.0 M|
 |SegFormer-B0 (slice)|76.73|11.1946|268.929|13.2 M|
 |SegFormer-B1 (slice)|78.35|17.9998|403.393|48.5 M|
 |SegFormer-B2 (slice)|81.60|48.0371|1248.52|96.9 M|

docs/module_usage/tutorials/cv_modules/semantic_segmentation_en.md

@@ -25,7 +25,7 @@ Semantic segmentation is a technique in computer vision that classifies each pix
 |OCRNet_HRNet-W18|80.67|48.2335|906.385|43.1 M|
 |OCRNet_HRNet-W48|82.15|78.9976|2226.95|249.8 M|
 |PP-LiteSeg-T|73.10|7.6827|138.683|28.5 M|
-|PP-LiteSeg-B|75.25|-|-|47.0 M|
+|PP-LiteSeg-B|75.25|10.9935|194.727|47.0 M|
 |SegFormer-B0 (slice)|76.73|11.1946|268.929|13.2 M|
 |SegFormer-B1 (slice)|78.35|17.9998|403.393|48.5 M|
 |SegFormer-B2 (slice)|81.60|48.0371|1248.52|96.9 M|

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.md

@@ -21,7 +21,7 @@
 |OCRNet_HRNet-W18|80.67|48.2335|906.385|43.1 M|
 |OCRNet_HRNet-W48|82.15|78.9976|2226.95|249.8 M|
 |PP-LiteSeg-T|73.10|7.6827|138.683|28.5 M|
-|PP-LiteSeg-B|75.25|-|-|47.0 M|
+|PP-LiteSeg-B|75.25|10.9935|194.727|47.0 M|
 |SegFormer-B0 (slice)|76.73|11.1946|268.929|13.2 M|
 |SegFormer-B1 (slice)|78.35|17.9998|403.393|48.5 M|
 |SegFormer-B2 (slice)|81.60|48.0371|1248.52|96.9 M|

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation_en.md

@@ -19,7 +19,7 @@ Semantic segmentation is a computer vision technique that aims to assign each pi
 |OCRNet_HRNet-W18|80.67|48.2335|906.385|43.1 M|
 |OCRNet_HRNet-W48|82.15|78.9976|2226.95|249.8 M|
 |PP-LiteSeg-T|73.10|7.6827|138.683|28.5 M|
-|PP-LiteSeg-B|75.25|-|-|47.0 M|
+|PP-LiteSeg-B|75.25|10.9935|194.727|47.0 M|
 |SegFormer-B0 (slice)|76.73|11.1946|268.929|13.2 M|
 |SegFormer-B1 (slice)|78.35|17.9998|403.393|48.5 M|
 |SegFormer-B2 (slice)|81.60|48.0371|1248.52|96.9 M|

docs/practical_tutorials/document_scene_information_extraction(seal_recognition)_tutorial.md

@@ -198,9 +198,9 @@ python main.py -c paddlex/configs/text_detection_seal/PP-OCRv4_server_seal_det.y
 python main.py -c paddlex/configs/text_detection_seal/PP-OCRv4_server_seal_det.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/practical_seal \
-    -o Global.epochs_iters=30 \
-    -o Global.batch_size=4 \
-    -o Global.learning_rate=0.0001
+    -o Train.epochs_iters=30 \
+    -o Train.batch_size=4 \
+    -o Train.learning_rate=0.0001
 ```
 
 在 PaddleX 中模型训练支持：修改训练超参数、单机单卡/多卡训练等功能，只需修改配置文件或追加命令行参数。

docs/practical_tutorials/document_scene_information_extraction(seal_recognition)_tutorial_en.md

@@ -197,9 +197,9 @@ Before training, please ensure that you have validated the dataset. To complete
 python main.py -c paddlex/configs/text_detection_seal/PP-OCRv4_server_seal_det.yaml \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/practical_seal \
-    -o Global.epochs_iters=30 \
-    -o Global.batch_size=4 \
-    -o Global.learning_rate=0.0001
+    -o Train.epochs_iters=30 \
+    -o Train.batch_size=4 \
+    -o Train.learning_rate=0.0001
 ```
 
 

docs/support_list/models_list.md

@@ -231,7 +231,7 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 |OCRNet_HRNet-W18|80.67|48.2335|906.385|43.1 M|[OCRNet_HRNet-W18.yaml](../../paddlex/configs/semantic_segmentation/OCRNet_HRNet-W18.yaml)|
 |OCRNet_HRNet-W48|82.15|78.9976|2226.95|249.8 M|[OCRNet_HRNet-W48.yaml](../../paddlex/configs/semantic_segmentation/OCRNet_HRNet-W48.yaml)|
 |PP-LiteSeg-T|73.10|7.6827|138.683|28.5 M|[PP-LiteSeg-T.yaml](../../paddlex/configs/semantic_segmentation/PP-LiteSeg-T.yaml)|
-|PP-LiteSeg-B|75.25|-|-|47.0 M|[PP-LiteSeg-B.yaml](../../paddlex/configs/semantic_segmentation/PP-LiteSeg-B.yaml)|
+|PP-LiteSeg-B|75.25|10.9935|194.727|47.0 M|[PP-LiteSeg-B.yaml](../../paddlex/configs/semantic_segmentation/PP-LiteSeg-B.yaml)|
 |SegFormer-B0 (slice)|76.73|11.1946|268.929|13.2 M|[SegFormer-B0.yaml](../../paddlex/configs/semantic_segmentation/SegFormer-B0.yaml)|
 |SegFormer-B1 (slice)|78.35|17.9998|403.393|48.5 M|[SegFormer-B1.yaml](../../paddlex/configs/semantic_segmentation/SegFormer-B1.yaml)|
 |SegFormer-B2 (slice)|81.60|48.0371|1248.52|96.9 M|[SegFormer-B2.yaml](../../paddlex/configs/semantic_segmentation/SegFormer-B2.yaml)|

docs/support_list/models_list_en.md

@@ -234,7 +234,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 |OCRNet_HRNet-W18|80.67|48.2335|906.385|43.1 M|[OCRNet_HRNet-W18.yaml](../../paddlex/configs/semantic_segmentation/OCRNet_HRNet-W18.yaml)|
 |OCRNet_HRNet-W48|82.15|78.9976|2226.95|249.8 M|[OCRNet_HRNet-W48.yaml](../../paddlex/configs/semantic_segmentation/OCRNet_HRNet-W48.yaml)|
 |PP-LiteSeg-T|73.10|7.6827|138.683|28.5 M|[PP-LiteSeg-T.yaml](../../paddlex/configs/semantic_segmentation/PP-LiteSeg-T.yaml)|
-|PP-LiteSeg-B|75.25|-|-|47.0 M|[PP-LiteSeg-B.yaml](../../paddlex/configs/semantic_segmentation/PP-LiteSeg-B.yaml)|
+|PP-LiteSeg-B|75.25|10.9935|194.727|47.0 M|[PP-LiteSeg-B.yaml](../../paddlex/configs/semantic_segmentation/PP-LiteSeg-B.yaml)|
 |SegFormer-B0 (slice)|76.73|11.1946|268.929|13.2 M|[SegFormer-B0.yaml](../../paddlex/configs/semantic_segmentation/SegFormer-B0.yaml)|
 |SegFormer-B1 (slice)|78.35|17.9998|403.393|48.5 M|[SegFormer-B1.yaml](../../paddlex/configs/semantic_segmentation/SegFormer-B1.yaml)|
 |SegFormer-B2 (slice)|81.60|48.0371|1248.52|96.9 M|[SegFormer-B2.yaml](../../paddlex/configs/semantic_segmentation/SegFormer-B2.yaml)|