yongsheng yuan 1 year ago
parent
commit
dad42dddf9

+ 1 - 1
docs/module_usage/tutorials/cv_modules/anomaly_detection.md

@@ -116,7 +116,7 @@ python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
 * 指定模型的`.yaml` 配置文件路径(此处为`STFPM.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`STFPM.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>

+ 1 - 2
docs/module_usage/tutorials/cv_modules/anomaly_detection_en.md

@@ -128,8 +128,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
-After completing model training, all outputs are saved in the specified output directory (default is `./output/`), typically```markdown
-Similar to model training, the following steps are required:
+After completing model training, all outputs are saved in the specified output directory (default is `./output/`), the following steps are required:
 
 
 * Specify the `.yaml` configuration file path of the model (here it is `STFPM.yaml`)
 * Specify the `.yaml` configuration file path of the model (here it is `STFPM.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Set the mode to model evaluation: `-o Global.mode=evaluate`

+ 1 - 1
docs/module_usage/tutorials/cv_modules/face_detection.md

@@ -179,7 +179,7 @@ python main.py -c paddlex/configs/face_detection/PicoDet_LCNet_x2_5_face.yaml \
 * 指定模型的`.yaml` 配置文件路径(此处为`PicoDet_LCNet_x2_5_face.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`PicoDet_LCNet_x2_5_face.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>

+ 1 - 2
docs/module_usage/tutorials/cv_modules/face_detection_en.md

@@ -185,8 +185,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
-After completing model training, all outputs are saved in the specified output directory (default is `./output/`), typically```markdown
-Similar to model training, the following steps are required:
+After completing model training, all outputs are saved in the specified output directory (default is `./output/`), the following steps are required:
 
 
 * Specify the `.yaml` configuration file path of the model (here it is `PicoDet_LCNet_x2_5_face.yaml`)
 * Specify the `.yaml` configuration file path of the model (here it is `PicoDet_LCNet_x2_5_face.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Set the mode to model evaluation: `-o Global.mode=evaluate`

+ 1 - 1
docs/module_usage/tutorials/cv_modules/human_detection.md

@@ -207,7 +207,7 @@ python main.py -c paddlex/configs/human_detection/PP-YOLOE-S_human.yaml \
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-YOLOE-S_human.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-YOLOE-S_human.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>

+ 1 - 3
docs/module_usage/tutorials/cv_modules/human_detection_en.md

@@ -212,9 +212,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
-After completing model training, all outputs are saved in the specified output directory (default is `./output/`), typically.
-
-Similar to model training, the following steps are required:
+After completing model training, all outputs are saved in the specified output directory (default is `./output/`), the following steps are required:
 
 
 * Specify the `.yaml` configuration file path of the model (here it is `PP-YOLOE-S_human.yaml`)
 * Specify the `.yaml` configuration file path of the model (here it is `PP-YOLOE-S_human.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Set the mode to model evaluation: `-o Global.mode=evaluate`

+ 1 - 1
docs/module_usage/tutorials/cv_modules/mainbody_detection.md

@@ -193,7 +193,7 @@ python main.py -c paddlex/configs/mainbody_detection/PP-ShiTuV2_det.yaml \
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-ShiTuV2_det.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-ShiTuV2_det.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>

+ 1 - 2
docs/module_usage/tutorials/cv_modules/mainbody_detection_en.md

@@ -200,8 +200,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
-After completing model training, all outputs are saved in the specified output directory (default is `./output/`), typically```markdown
-Similar to model training, the following steps are required:
+After completing model training, all outputs are saved in the specified output directory (default is `./output/`), the following steps are required:
 
 
 * Specify the `.yaml` configuration file path of the model (here it is `PP-ShiTuV2_det.yaml`)
 * Specify the `.yaml` configuration file path of the model (here it is `PP-ShiTuV2_det.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Set the mode to model evaluation: `-o Global.mode=evaluate`

+ 1 - 1
docs/module_usage/tutorials/cv_modules/object_detection.md

@@ -523,7 +523,7 @@ python main.py -c paddlex/configs/object_detection/PicoDet-S.yaml \
 * 指定模型的`.yaml` 配置文件路径(此处为`PicoDet-S.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`PicoDet-S.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
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+ 1 - 1
docs/module_usage/tutorials/cv_modules/semantic_segmentation.md

@@ -233,7 +233,7 @@ python main.py -c paddlex/configs/semantic_segmentation/PP-LiteSeg-T.yaml \
 * 指定模型的.yaml 配置文件路径(此处为 `PP-LiteSeg-T.yam`)
 * 指定模型的.yaml 配置文件路径(此处为 `PP-LiteSeg-T.yam`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>

+ 9 - 9
docs/module_usage/tutorials/cv_modules/small_object_detection.md

@@ -90,7 +90,7 @@ tar -xf ./dataset/small_det_examples.tar -C ./dataset/
 一行命令即可完成数据校验:
 一行命令即可完成数据校验:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/small_det_examples
     -o Global.dataset_dir=./dataset/small_det_examples
 ```
 ```
@@ -174,14 +174,14 @@ CheckDataset:
 随后执行命令:
 随后执行命令:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset
 ```
 ```
 当然,以上参数同样支持通过追加命令行参数的方式进行设置,以 `LabelMe` 格式的数据集为例:
 当然,以上参数同样支持通过追加命令行参数的方式进行设置,以 `LabelMe` 格式的数据集为例:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset \
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset \
     -o CheckDataset.convert.enable=True \
     -o CheckDataset.convert.enable=True \
@@ -213,7 +213,7 @@ CheckDataset:
 随后执行命令:
 随后执行命令:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/small_det_examples
     -o Global.dataset_dir=./dataset/small_det_examples
 ```
 ```
@@ -222,7 +222,7 @@ python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml
 以上参数同样支持通过追加命令行参数的方式进行设置:
 以上参数同样支持通过追加命令行参数的方式进行设置:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml  \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml  \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o CheckDataset.split.enable=True \
     -o CheckDataset.split.enable=True \
@@ -235,7 +235,7 @@ python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml
 一条命令即可完成模型的训练,以此处PP-YOLOE_plus_SOD-S的训练为例:
 一条命令即可完成模型的训练,以此处PP-YOLOE_plus_SOD-S的训练为例:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=train \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o Train.num_classes=10
     -o Train.num_classes=10
@@ -245,7 +245,7 @@ python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-YOLOE_plus_SOD-S.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-YOLOE_plus_SOD-S.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
@@ -266,7 +266,7 @@ python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml
 在完成模型训练后,可以对指定的模型权重文件在验证集上进行评估,验证模型精度。使用 PaddleX 进行模型评估,一条命令即可完成模型的评估:
 在完成模型训练后,可以对指定的模型权重文件在验证集上进行评估,验证模型精度。使用 PaddleX 进行模型评估,一条命令即可完成模型的评估:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=evaluate \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/small_det_examples
     -o Global.dataset_dir=./dataset/small_det_examples
 ```
 ```
@@ -294,7 +294,7 @@ python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml
 #### 4.4.1 模型推理
 #### 4.4.1 模型推理
 * 通过命令行的方式进行推理预测,只需如下一条命令。运行以下代码前,请您下载[示例图片](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg)到本地。
 * 通过命令行的方式进行推理预测,只需如下一条命令。运行以下代码前,请您下载[示例图片](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg)到本地。
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=predict \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="small_object_detection.jpg"
     -o Predict.input="small_object_detection.jpg"

+ 10 - 11
docs/module_usage/tutorials/cv_modules/small_object_detection_en.md

@@ -62,7 +62,7 @@ from paddlex import create_model
 model_name = "PP-YOLOE_plus_SOD-S"
 model_name = "PP-YOLOE_plus_SOD-S"
 
 
 model = create_model(model_name)
 model = create_model(model_name)
-output = model.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg", batch_size=1)
+output = model.predict("small_object_detection.jpg", batch_size=1)
 
 
 for res in output:
 for res in output:
     res.print(json_format=False)
     res.print(json_format=False)
@@ -90,7 +90,7 @@ tar -xf ./dataset/small_det_examples.tar -C ./dataset/
 You can complete data validation with a single command:
 You can complete data validation with a single command:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/small_det_examples
     -o Global.dataset_dir=./dataset/small_det_examples
 ```
 ```
@@ -173,14 +173,14 @@ CheckDataset:
 Then execute the command:
 Then execute the command:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset
 ```
 ```
 Of course, the above parameters also support being set by appending command line arguments. Taking a `LabelMe` format dataset as an example:
 Of course, the above parameters also support being set by appending command line arguments. Taking a `LabelMe` format dataset as an example:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset \
     -o Global.dataset_dir=./path/to/your_smallobject_labelme_dataset \
     -o CheckDataset.convert.enable=True \
     -o CheckDataset.convert.enable=True \
@@ -211,7 +211,7 @@ CheckDataset:
 Then execute the command:
 Then execute the command:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/small_det_examples
     -o Global.dataset_dir=./dataset/small_det_examples
 ```
 ```
@@ -220,7 +220,7 @@ After dataset splitting, the original annotation files will be renamed to `xxx.b
 The above parameters can also be set by appending command-line arguments:
 The above parameters can also be set by appending command-line arguments:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml  \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml  \
     -o Global.mode=check_dataset \
     -o Global.mode=check_dataset \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o CheckDataset.split.enable=True \
     -o CheckDataset.split.enable=True \
@@ -233,7 +233,7 @@ python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml
 Model training can be completed with a single command, taking the training of `PP-YOLOE_plus_SOD-S` as an example:
 Model training can be completed with a single command, taking the training of `PP-YOLOE_plus_SOD-S` as an example:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=train \
     -o Global.mode=train \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o Global.dataset_dir=./dataset/small_det_examples \
     -o Train.num_classes=10
     -o Train.num_classes=10
@@ -251,8 +251,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
-After completing model training, all outputs are saved in the specified output directory (default is `./output/`), typically```markdown
-Similar to model training, the following steps are required:
+After completing model training, all outputs are saved in the specified output directory (default is `./output/`), the following steps are required:
 
 
 * Specify the `.yaml` configuration file path of the model (here it is `PP-YOLOE_plus_SOD-S.yaml`)
 * Specify the `.yaml` configuration file path of the model (here it is `PP-YOLOE_plus_SOD-S.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
@@ -264,7 +263,7 @@ Other related parameters can be set by modifying the fields under `Global` and `
 After completing model training, you can evaluate the specified model weight file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation, you can complete the evaluation with a single command:
 After completing model training, you can evaluate the specified model weight file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation, you can complete the evaluation with a single command:
 
 
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=evaluate \
     -o Global.mode=evaluate \
     -o Global.dataset_dir=./dataset/small_det_examples
     -o Global.dataset_dir=./dataset/small_det_examples
 ```
 ```
@@ -291,7 +290,7 @@ After completing model training and evaluation, you can use the trained model we
 #### 4.4.1 Model Inference
 #### 4.4.1 Model Inference
 * To perform inference predictions through the command line, simply use the following command. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg) to your local machine.
 * To perform inference predictions through the command line, simply use the following command. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg) to your local machine.
 ```bash
 ```bash
-python main.py -c paddlex/configs/smallobject_detection/PP-YOLOE_plus_SOD-S.yaml \
+python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yaml \
     -o Global.mode=predict \
     -o Global.mode=predict \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.model_dir="./output/best_model/inference" \
     -o Predict.input="small_object_detection.jpg"
     -o Predict.input="small_object_detection.jpg"

+ 1 - 1
docs/module_usage/tutorials/cv_modules/vehicle_detection.md

@@ -198,7 +198,7 @@ python main.py -c paddlex/configs/vehicle_detection/PP-YOLOE-S_vehicle.yaml \
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-YOLOE-S_vehicle.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`PP-YOLOE-S_vehicle.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>

+ 1 - 2
docs/module_usage/tutorials/cv_modules/vehicle_detection_en.md

@@ -205,8 +205,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
-After completing model training, all outputs are saved in the specified output directory (default is `./output/`), typically
-Similar to model training, the following steps are required:
+After completing model training, all outputs are saved in the specified output directory (default is `./output/`), the following steps are required:
 
 
 * Specify the `.yaml` configuration file path of the model (here it is `PP-YOLOE-S_vehicle.yaml`)
 * Specify the `.yaml` configuration file path of the model (here it is `PP-YOLOE-S_vehicle.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Set the mode to model evaluation: `-o Global.mode=evaluate`

+ 1 - 1
docs/module_usage/tutorials/ocr_modules/layout_detection.md

@@ -181,7 +181,7 @@ python main.py -c paddlex/configs/structure_analysis/PicoDet-L_layout_3cls.yaml
 * 指定模型的`.yaml` 配置文件路径(此处为`PicoDet-L_layout_3cls.yaml`)
 * 指定模型的`.yaml` 配置文件路径(此处为`PicoDet-L_layout_3cls.yaml`)
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定模式为模型训练:`-o Global.mode=train`
 * 指定训练数据集路径:`-o Global.dataset_dir`
 * 指定训练数据集路径:`-o Global.dataset_dir`
-其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
+其他相关参数均可通过修改`.yaml`配置文件中的`Global`和`Train`下的字段来进行设置,也可以通过在命令行中追加参数来进行调整。如指定前 2 卡 gpu 训练:`-o Global.device=gpu:0,1`;设置训练轮次数为 10:`-o Train.epochs_iters=10`。更多可修改的参数及其详细解释,可以查阅模型对应任务模块的配置文件说明[PaddleX通用模型配置文件参数说明](../../instructions/config_parameters_common.md)。
 
 
 <details>
 <details>
   <summary>👉 <b>更多说明(点击展开)</b></summary>
   <summary>👉 <b>更多说明(点击展开)</b></summary>

+ 1 - 2
docs/module_usage/tutorials/ocr_modules/layout_detection_en.md

@@ -191,8 +191,7 @@ Other related parameters can be set by modifying the `Global` and `Train` fields
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * During model training, PaddleX automatically saves model weight files, defaulting to `output`. To specify a save path, use the `-o Global.output` field in the configuration file.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
 * When training other models, specify the corresponding configuration file. The correspondence between models and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list_en.md).
-After completing model training, all outputs are saved in the specified output directory (default is `./output/`), typically```markdown
-Similar to model training, the following steps are required:
+After completing model training, all outputs are saved in the specified output directory (default is `./output/`), the following steps are required:
 
 
 * Specify the `.yaml` configuration file path of the model (here it is `PicoDet-L_layout_3cls.yaml`)
 * Specify the `.yaml` configuration file path of the model (here it is `PicoDet-L_layout_3cls.yaml`)
 * Set the mode to model evaluation: `-o Global.mode=evaluate`
 * Set the mode to model evaluation: `-o Global.mode=evaluate`