Update image classification module

hub_module/modules/image/classification/efficientnetb0_small_imagenet/module.py

@@ -21,8 +21,8 @@ from efficientnetb0_small_imagenet.efficientnet import EfficientNetB0_small
 @moduleinfo(
     name="efficientnetb0_small_imagenet",
     type="CV/image_classification",
-    author="baidu-vis",
-    author_email="",
+    author="paddlepaddle",
+    author_email="paddle-dev@baidu.com",
     summary=
     "ResNet18vd is a image classfication model, this module is trained with imagenet datasets.",
     version="1.0.0")

hub_module/modules/image/classification/efficientnetb0_small_imagenet/processor.py

@@ -49,7 +49,7 @@ def postprocess(data_out, label_list, top_k):
         output_i = {}
         indexs = np.argsort(result_i)[::-1][0:top_k]
         for index in indexs:
-            label = label_list[index]
+            label = label_list[index].split(',')[0]
             output_i[label] = float(result_i[index])
         output.append(output_i)
     return output

hub_module/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/module.py

@@ -21,15 +21,15 @@ from fix_resnext101_32x48d_wsl_imagenet.resnext101_wsl import Fix_ResNeXt101_32x
 @moduleinfo(
     name="fix_resnext101_32x48d_wsl_imagenet",
     type="CV/image_classification",
-    author="baidu-vis",
-    author_email="",
+    author="paddlepaddle",
+    author_email="paddle-dev@baidu.com",
     summary=
     "fix_resnext101_32x48d_wsl is a image classfication model, this module is trained with imagenet datasets.",
     version="1.0.0")
 class FixResnext10132x48dwslImagenet(hub.Module):
     def _initialize(self):
         self.default_pretrained_model_path = os.path.join(
-            self.directory, "fix_resnext101_32x48d_wsl_imagenet_model")
+            self.directory, "model")
         label_file = os.path.join(self.directory, "label_list.txt")
         with open(label_file, 'r', encoding='utf-8') as file:
             self.label_list = file.read().split("\n")[:-1]

hub_module/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/processor.py

@@ -49,7 +49,7 @@ def postprocess(data_out, label_list, top_k):
         output_i = {}
         indexs = np.argsort(result_i)[::-1][0:top_k]
         for index in indexs:
-            label = label_list[index]
+            label = label_list[index].split(',')[0]
             output_i[label] = float(result_i[index])
         output.append(output_i)
     return output

hub_module/modules/image/classification/res2net101_vd_26w_4s_imagenet/module.py

@@ -21,8 +21,8 @@ from res2net101_vd_26w_4s_imagenet.res2net_vd import Res2Net101_vd_26w_4s
 @moduleinfo(
     name="res2net101_vd_26w_4s_imagenet",
     type="CV/image_classification",
-    author="baidu-vis",
-    author_email="",
+    author="paddlepaddle",
+    author_email="paddle-dev@baidu.com",
     summary=
     "res2net101_vd_26w_4s is a image classfication model, this module is trained with imagenet datasets.",
     version="1.0.0")

hub_module/modules/image/classification/res2net101_vd_26w_4s_imagenet/processor.py

@@ -49,7 +49,7 @@ def postprocess(data_out, label_list, top_k):
         output_i = {}
         indexs = np.argsort(result_i)[::-1][0:top_k]
         for index in indexs:
-            label = label_list[index]
+            label = label_list[index].split(',')[0]
             output_i[label] = float(result_i[index])
         output.append(output_i)
     return output

hub_module/modules/image/classification/resnet18_vd_imagenet/module.py

@@ -21,8 +21,8 @@ from resnet18_vd_imagenet.resnet_vd import ResNet18_vd
 @moduleinfo(
     name="resnet18_vd_imagenet",
     type="CV/image_classification",
-    author="baidu-vis",
-    author_email="",
+    author="paddlepaddle",
+    author_email="paddle-dev@baidu.com",
     summary=
     "ResNet18vd is a image classfication model, this module is trained with imagenet datasets.",
     version="1.0.0")

hub_module/modules/image/classification/resnet18_vd_imagenet/processor.py

@@ -49,7 +49,7 @@ def postprocess(data_out, label_list, top_k):
         output_i = {}
         indexs = np.argsort(result_i)[::-1][0:top_k]
         for index in indexs:
-            label = label_list[index]
+            label = label_list[index].split(',')[0]
             output_i[label] = float(result_i[index])
         output.append(output_i)
     return output

hub_module/modules/image/classification/se_resnet18_vd_imagenet/module.py

@@ -21,8 +21,8 @@ from se_resnet18_vd_imagenet.se_resnet import SE_ResNet18_vd
 @moduleinfo(
     name="se_resnet18_vd_imagenet",
     type="CV/image_classification",
-    author="baidu-vis",
-    author_email="",
+    author="paddlepaddle",
+    author_email="paddle-dev@baidu.com",
     summary=
     "SE_ResNet18_vd is a image classfication model, this module is trained with imagenet datasets.",
     version="1.0.0")

hub_module/modules/image/classification/se_resnet18_vd_imagenet/processor.py

@@ -49,7 +49,7 @@ def postprocess(data_out, label_list, top_k):
         output_i = {}
         indexs = np.argsort(result_i)[::-1][0:top_k]
         for index in indexs:
-            label = label_list[index]
+            label = label_list[index].split(',')[0]
             output_i[label] = float(result_i[index])
         output.append(output_i)
     return output