fix error words

deploy/utils/config.py

@@ -188,7 +188,7 @@ def parser():
         '-v',
         '--verbose',
         action='store_true',
-        help='wheather print the config info')
+        help='whether print the config info')
     return parser
 
 

docs/en/PULC/PULC_model_list_en.md

@@ -9,7 +9,7 @@ The PULC model zoo is provided here, mainly providing indicators, model storage
 | --- | --- | --- | --- | --- | --- |
 | person_exists |[Human Exists Classification](PULC_person_exists_en.md)| 96.23 |7.0M|2.58ms|[inference model](https://paddleclas.bj.bcebos.com/models/PULC/inference/person_exists_infer.tar) / [pretrained model](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/person_exists_pretrained.pdparams)|
 | person_attribute |[Pedestrian Attribute Classification](PULC_person_attribute_en.md)| 78.59 |7.2M|2.01ms|[inference model](https://paddleclas.bj.bcebos.com/models/PULC/inference/person_attribute_infer.tar) / [pretrained model](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/person_attribute_pretrained.pdparams)|
-| safety_helmet |[Classification of Wheather Wearing Safety Helmet](PULC_safety_helmet_en.md)| 99.38 |7.1M|2.03ms|[inference model](https://paddleclas.bj.bcebos.com/models/PULC/inference/safety_helmet_infer.tar) / [pretrained model](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/safety_helmet_pretrained.pdparams)|
+| safety_helmet |[Classification of Whether Wearing Safety Helmet](PULC_safety_helmet_en.md)| 99.38 |7.1M|2.03ms|[inference model](https://paddleclas.bj.bcebos.com/models/PULC/inference/safety_helmet_infer.tar) / [pretrained model](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/safety_helmet_pretrained.pdparams)|
 | traffic_sign |[Traffic Sign Classification](PULC_traffic_sign_en.md)| 98.35 |8.2M|2.10ms|[inference model](https://paddleclas.bj.bcebos.com/models/PULC/inference/traffic_sign_infer.tar) / [pretrained model](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/traffic_sign_pretrained.pdparams)|
 | vehicle_attribute |[Vehicle Attribute Classification](PULC_vehicle_attribute_en.md)| 90.81 |7.2M|2.36ms|[inference model](https://paddleclas.bj.bcebos.com/models/PULC/inference/vehicle_attribute_infer.tar) / [pretrained model](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/vehicle_attribute_pretrained.pdparams)|
 | car_exists |[Car Exists Classification](PULC_car_exists_en.md) | 95.92 | 7.1M | 2.38ms |[inference model](https://paddleclas.bj.bcebos.com/models/PULC/inference/car_exists_infer.tar) / [pretrained model](https://paddleclas.bj.bcebos.com/models/PULC/pretrained/car_exists_pretrained.pdparams)|

docs/en/PULC/PULC_quickstart_en.md

@@ -108,7 +108,7 @@ The name of PULC series models are as follows:
 | --- | --- |
 | person_exists | Human Exists Classification |
 | person_attribute | Pedestrian Attribute Classification |
-| safety_helmet | Classification of Wheather Wearing Safety Helmet |
+| safety_helmet | Classification of Whether Wearing Safety Helmet |
 | traffic_sign | Traffic Sign Classification |
 | vehicle_attribute | Vehicle Attribute Classification |
 | car_exists | Car Exists Classification |
@@ -120,4 +120,4 @@ The name of PULC series models are as follows:
 
 ## 3. Summary
 
-The PULC series models have been verified to be effective in different scenarios about people, vehicles, OCR, etc. The ultra lightweight model can achieve the accuracy close to SwinTransformer model, and the speed is increased by 40+ times. And PULC also provides the whole process of dataset getting, model training, model compression and deployment. Please refer to [Human Exists Classification](PULC_person_exists_en.md)、[Pedestrian Attribute Classification](PULC_person_attribute_en.md)、[Classification of Wheather Wearing Safety Helmet](PULC_safety_helmet_en.md)、[Traffic Sign Classification](PULC_traffic_sign_en.md)、[Vehicle Attribute Classification](PULC_vehicle_attribute_en.md)、[Car Exists Classification](PULC_car_exists_en.md)、[Text Image Orientation Classification](PULC_text_image_orientation_en.md)、[Text-line Orientation Classification](PULC_textline_orientation_en.md)、[Language Classification](PULC_language_classification_en.md) for more information about different scenarios.
+The PULC series models have been verified to be effective in different scenarios about people, vehicles, OCR, etc. The ultra lightweight model can achieve the accuracy close to SwinTransformer model, and the speed is increased by 40+ times. And PULC also provides the whole process of dataset getting, model training, model compression and deployment. Please refer to [Human Exists Classification](PULC_person_exists_en.md)、[Pedestrian Attribute Classification](PULC_person_attribute_en.md)、[Classification of Whether Wearing Safety Helmet](PULC_safety_helmet_en.md)、[Traffic Sign Classification](PULC_traffic_sign_en.md)、[Vehicle Attribute Classification](PULC_vehicle_attribute_en.md)、[Car Exists Classification](PULC_car_exists_en.md)、[Text Image Orientation Classification](PULC_text_image_orientation_en.md)、[Text-line Orientation Classification](PULC_textline_orientation_en.md)、[Language Classification](PULC_language_classification_en.md) for more information about different scenarios.

docs/en/PULC/PULC_safety_helmet_en.md

-# PULC Classification Model of Wheather Wearing Safety Helmet or Not
+# PULC Classification Model of Whether Wearing Safety Helmet or Not
 
 -----
 
@@ -38,7 +38,7 @@
 
 ## 1. Introduction
 
-This case provides a way for users to quickly build a lightweight, high-precision and practical classification model of wheather wearing safety helmet using PaddleClas PULC (Practical Ultra Lightweight image Classification). The model can be widely used in construction scenes, factory workshop scenes, traffic scenes and so on.
+This case provides a way for users to quickly build a lightweight, high-precision and practical classification model of whether wearing safety helmet using PaddleClas PULC (Practical Ultra Lightweight image Classification). The model can be widely used in construction scenes, factory workshop scenes, traffic scenes and so on.
 
 The following table lists the relevant indicators of the model. The first three lines means that using SwinTransformer_tiny, Res2Net200_vd_26w_4s and MobileNetV3_small_x0_35 as the backbone to training. The fourth to seventh lines means that the backbone is replaced by PPLCNet, additional use of EDA strategy and additional use of EDA strategy and SKL-UGI knowledge distillation strategy.
 

docs/en/PULC/PULC_train_en.md

@@ -194,7 +194,7 @@ We also used the same optimization strategy in the other 8 scenarios and got the
 | scenarios | large model | large model metrics(%) | small model | small model metrics(%) |
 |----------|----------|----------|----------|----------|
 | Pedestrian Attribute Classification | Res2Net200_vd | 81.25 | PPLCNet_x1_0 | 78.59 |
-| Classification of Wheather Wearing Safety Helmet | Res2Net200_vd| 98.92 | PPLCNet_x1_0 |99.38 |
+| Classification of Whether Wearing Safety Helmet | Res2Net200_vd| 98.92 | PPLCNet_x1_0 |99.38 |
 | Traffic Sign Classification | SwinTransformer_tiny | 98.11 | PPLCNet_x1_0 | 98.35 |
 | Vehicle Attribute Classification | Res2Net200_vd_26w_4s | 91.36 | PPLCNet_x1_0 | 90.81 |
 | Car Exists Classification | SwinTransformer_tiny | 97.71 | PPLCNet_x1_0 | 95.92 |

docs/en/inference_deployment/whl_deploy_en.md

@@ -128,7 +128,7 @@ The name of PULC series models are as follows:
 | --- | --- |
 | person_exists | Human Exists Classification |
 | person_attribute | Pedestrian Attribute Classification |
-| safety_helmet | Classification of Wheather Wearing Safety Helmet |
+| safety_helmet | Classification of Whether Wearing Safety Helmet |
 | traffic_sign | Traffic Sign Classification |
 | vehicle_attribute | Vehicle Attribute Classification |
 | car_exists | Car Exists Classification |
@@ -136,7 +136,7 @@ The name of PULC series models are as follows:
 | textline_orientation | Text-line Orientation Classification |
 | language_classification | Language Classification |
 
-Please refer to [Human Exists Classification](../PULC/PULC_person_exists_en.md)、[Pedestrian Attribute Classification](../PULC/PULC_person_attribute_en.md)、[Classification of Wheather Wearing Safety Helmet](../PULC/PULC_safety_helmet_en.md)、[Traffic Sign Classification](../PULC/PULC_traffic_sign_en.md)、[Vehicle Attribute Classification](../PULC/PULC_vehicle_attribute_en.md)、[Car Exists Classification](../PULC/PULC_car_exists_en.md)、[Text Image Orientation Classification](../PULC/PULC_text_image_orientation_en.md)、[Text-line Orientation Classification](../PULC/PULC_textline_orientation_en.md)、[Language Classification](../PULC/PULC_language_classification_en.md) for more information about different scenarios.
+Please refer to [Human Exists Classification](../PULC/PULC_person_exists_en.md)、[Pedestrian Attribute Classification](../PULC/PULC_person_attribute_en.md)、[Classification of Whether Wearing Safety Helmet](../PULC/PULC_safety_helmet_en.md)、[Traffic Sign Classification](../PULC/PULC_traffic_sign_en.md)、[Vehicle Attribute Classification](../PULC/PULC_vehicle_attribute_en.md)、[Car Exists Classification](../PULC/PULC_car_exists_en.md)、[Text Image Orientation Classification](../PULC/PULC_text_image_orientation_en.md)、[Text-line Orientation Classification](../PULC/PULC_textline_orientation_en.md)、[Language Classification](../PULC/PULC_language_classification_en.md) for more information about different scenarios.
 
 <a name="3"></a>