diff --git a/docs/en/inference_deployment/whl_deploy_en.md b/docs/en/inference_deployment/whl_deploy_en.md
index 7c94f6ded4a02548012f536e222ffebb84254c21..d726005a22f117a1958b7a9499b8c4c6e8fcbada 100644
--- a/docs/en/inference_deployment/whl_deploy_en.md
+++ b/docs/en/inference_deployment/whl_deploy_en.md
@@ -62,7 +62,7 @@ print(next(result))
 
 ```
 >>> result
-[{'class_ids': [8, 7, 136, 80, 84], 'scores': [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], 'label_names': ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']}]
+[{'class_ids': [8, 7, 86, 82, 80], 'scores': [0.97968, 0.02028, 3e-05, 1e-05, 0.0], 'label_names': ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], 'filename': 'docs/images/inference_deployment/whl_demo.jpg'}]
 ```
 
 * CLI
@@ -72,7 +72,7 @@ paddleclas --model_name=ResNet50  --infer_imgs="docs/images/inference_deployment
 
 ```
 >>> result
-filename: docs/images/inference_deployment/whl_demo.jpg, top-5, class_ids: [8, 7, 136, 80, 84], scores: [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], label_names: ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']
+class_ids: [8, 7, 86, 82, 80], scores: [0.97968, 0.02028, 3e-05, 1e-05, 0.0], label_names: ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], filename: docs/images/inference_deployment/whl_demo.jpg
 Predict complete!
 ```
 
diff --git a/docs/zh_CN/deployment/image_classification/whl.md b/docs/zh_CN/deployment/image_classification/whl.md
index 38422bc0c0db1969f8359d85d51090708d745d2c..89d47d008299767ca05da1f067e20c23b1c2d637 100644
--- a/docs/zh_CN/deployment/image_classification/whl.md
+++ b/docs/zh_CN/deployment/image_classification/whl.md
@@ -55,7 +55,7 @@ print(next(result))
 
 ```
 >>> result
-[{'class_ids': [8, 7, 136, 80, 84], 'scores': [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], 'label_names': ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']}]
+[{'class_ids': [8, 7, 86, 82, 80], 'scores': [0.97968, 0.02028, 3e-05, 1e-05, 0.0], 'label_names': ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], 'filename': 'docs/images/inference_deployment/whl_demo.jpg'}]
 ```
 
 * 在命令行中使用
@@ -65,7 +65,7 @@ paddleclas --model_name=ResNet50  --infer_imgs="docs/images/inference_deployment
 
 ```
 >>> result
-filename: docs/images/inference_deployment/whl_demo.jpg, top-5, class_ids: [8, 7, 136, 80, 84], scores: [0.79368, 0.16329, 0.01853, 0.00959, 0.00239], label_names: ['hen', 'cock', 'European gallinule, Porphyrio porphyrio', 'black grouse', 'peacock']
+class_ids: [8, 7, 86, 82, 80], scores: [0.97968, 0.02028, 3e-05, 1e-05, 0.0], label_names: ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], filename: docs/images/inference_deployment/whl_demo.jpg
 Predict complete!
 ```