support for ssld

configs/AlexNet/AlexNet.yaml

 mode: 'train'
-architecture: "AlexNet"
+ARCHITECTURE:
+    name: "AlexNet"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DPN/DPN107.yaml

 mode: 'train'
-architecture: 'DPN107'
+ARCHITECTURE:
+    name: 'DPN107'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DPN/DPN131.yaml

 mode: 'train'
-architecture: 'DPN131'
+ARCHITECTURE:
+    name: 'DPN131'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DPN/DPN68.yaml

 mode: 'train'
-architecture: 'DPN68'
+ARCHITECTURE:
+    name: 'DPN68'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DPN/DPN92.yaml

 mode: 'train'
-architecture: 'DPN92'
+ARCHITECTURE:
+    name: 'DPN92'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DPN/DPN98.yaml

 mode: 'train'
-architecture: 'DPN98'
+ARCHITECTURE:
+    name: 'DPN98'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DarkNet/DarkNet53.yaml

 mode: 'train'
-architecture: "DarkNet53"
+ARCHITECTURE:
+    name: "DarkNet53"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DenseNet/DenseNet121.yaml

 mode: 'train'
-architecture: 'DenseNet121'
+ARCHITECTURE:
+    name: 'DenseNet121'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DenseNet/DenseNet161.yaml

 mode: 'train'
-architecture: 'DenseNet161'
+ARCHITECTURE:
+    name: 'DenseNet161'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DenseNet/DenseNet169.yaml

 mode: 'train'
-architecture: 'DenseNet169'
+ARCHITECTURE:
+    name: 'DenseNet169'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DenseNet/DenseNet201.yaml

 mode: 'train'
-architecture: 'DenseNet201'
+ARCHITECTURE:
+    name: 'DenseNet201'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/DenseNet/DenseNet264.yaml

 mode: 'train'
-architecture: 'DenseNet264'
+ARCHITECTURE:
+    name: 'DenseNet264'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/HRNet/HRNet_W18_C.yaml

 mode: 'train'
-architecture: 'HRNet_W18_C'
+ARCHITECTURE:
+    name: 'HRNet_W18_C'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/HRNet/HRNet_W30_C.yaml

 mode: 'train'
-architecture: 'HRNet_W30_C'
+ARCHITECTURE:
+    name: 'HRNet_W30_C'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/HRNet/HRNet_W32_C.yaml

 mode: 'train'
-architecture: 'HRNet_W32_C'
+ARCHITECTURE:
+    name: 'HRNet_W32_C'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/HRNet/HRNet_W40_C.yaml

 mode: 'train'
-architecture: 'HRNet_W40_C'
+ARCHITECTURE:
+    name: 'HRNet_W40_C'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/HRNet/HRNet_W44_C.yaml

 mode: 'train'
-architecture: 'HRNet_W44_C'
+ARCHITECTURE:
+    name: 'HRNet_W44_C'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/HRNet/HRNet_W48_C.yaml

 mode: 'train'
-architecture: 'HRNet_W48_C'
+ARCHITECTURE:
+    name: 'HRNet_W48_C'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/HRNet/HRNet_W64_C.yaml

 mode: 'train'
-architecture: 'HRNet_W64_C'
+ARCHITECTURE:
+    name: 'HRNet_W64_C'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/Inception/GoogLeNet.yaml

 mode: 'train'
-architecture: "GoogLeNet"
+ARCHITECTURE:
+    name: "GoogLeNet"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/Inception/InceptionV4.yaml

 mode: 'train'
-architecture: 'InceptionV4'
+ARCHITECTURE:
+    name: 'InceptionV4'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV1/MobileNetV1.yaml

 mode: 'train'
-architecture: "MobileNetV1"
+ARCHITECTURE:
+    name: "MobileNetV1"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV1/MobileNetV1_x0_25.yaml

 mode: 'train'
-architecture: "MobileNetV1_x0_25"
+ARCHITECTURE:
+    name: "MobileNetV1_x0_25"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV1/MobileNetV1_x0_5.yaml

 mode: 'train'
-architecture: "MobileNetV1_x0_5"
+ARCHITECTURE:
+    name: "MobileNetV1_x0_5"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV1/MobileNetV1_x0_75.yaml

 mode: 'train'
-architecture: "MobileNetV1_x0_75"
+ARCHITECTURE:
+    name: "MobileNetV1_x0_75"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV2/MobileNetV2.yaml

 mode: 'train'
-architecture: "MobileNetV2"
+ARCHITECTURE:
+    name: "MobileNetV2"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV2/MobileNetV2_x0_25.yaml

 mode: 'train'
-architecture: "MobileNetV2_x0_25"
+ARCHITECTURE:
+    name: "MobileNetV2_x0_25"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV2/MobileNetV2_x0_5.yaml

 mode: 'train'
-architecture: "MobileNetV2_x0_5"
+ARCHITECTURE:
+    name: "MobileNetV2_x0_5"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV2/MobileNetV2_x0_75.yaml

 mode: 'train'
-architecture: "MobileNetV2_x0_75"
+ARCHITECTURE:
+    name: "MobileNetV2_x0_75"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV2/MobileNetV2_x1_5.yaml

 mode: 'train'
-architecture: "MobileNetV2_x1_5"
+ARCHITECTURE:
+    name: "MobileNetV2_x1_5"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV2/MobileNetV2_x2_0.yaml

 mode: 'train'
-architecture: "MobileNetV2_x2_0"
+ARCHITECTURE:
+    name: "MobileNetV2_x2_0"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_large_x0_35.yaml

 mode: 'train'
-architecture: "MobileNetV3_large_x0_35"
+ARCHITECTURE:
+    name: "MobileNetV3_large_x0_35"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_large_x0_5.yaml

 mode: 'train'
-architecture: "MobileNetV3_large_x0_5"
+ARCHITECTURE:
+    name: "MobileNetV3_large_x0_5"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_large_x0_75.yaml

 mode: 'train'
-architecture: "MobileNetV3_large_x0_75"
+ARCHITECTURE:
+    name: "MobileNetV3_large_x0_75"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_large_x1_0.yaml

 mode: 'train'
-architecture: "MobileNetV3_large_x1_0"
+ARCHITECTURE:
+    name: "MobileNetV3_large_x1_0"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_large_x1_25.yaml

 mode: 'train'
-architecture: "MobileNetV3_large_x1_25"
+ARCHITECTURE:
+    name: "MobileNetV3_large_x1_25"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_small_x0_35.yaml

 mode: 'train'
-architecture: "MobileNetV3_small_x0_35"
+ARCHITECTURE:
+    name: "MobileNetV3_small_x0_35"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_small_x0_5.yaml

 mode: 'train'
-architecture: "MobileNetV3_small_x0_5"
+ARCHITECTURE:
+    name: "MobileNetV3_small_x0_5"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_small_x0_75.yaml

 mode: 'train'
-architecture: "MobileNetV3_small_x0_75"
+ARCHITECTURE:
+    name: "MobileNetV3_small_x0_75"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_small_x1_0.yaml

 mode: 'train'
-architecture: "MobileNetV3_small_x1_0"
+ARCHITECTURE:
+    name: "MobileNetV3_small_x1_0"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/MobileNetV3/MobileNetV3_small_x1_25.yaml

 mode: 'train'
-architecture: "MobileNetV3_small_x1_25"
+ARCHITECTURE:
+    name: "MobileNetV3_small_x1_25"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/Res2Net/Res2Net101_vd_26w_4s.yaml

 mode: 'train'
-architecture: 'Res2Net101_vd_26w_4s'
+ARCHITECTURE:
+    name: 'Res2Net101_vd_26w_4s'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/Res2Net/Res2Net200_vd_26w_4s.yaml

 mode: 'train'
-architecture: 'Res2Net200_vd_26w_4s'
+ARCHITECTURE:
+    name: 'Res2Net200_vd_26w_4s'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/Res2Net/Res2Net50_14w_8s.yaml

 mode: 'train'
-architecture: 'Res2Net50_14w_8s'
+ARCHITECTURE:
+    name: 'Res2Net50_14w_8s'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/Res2Net/Res2Net50_26w_4s.yaml

 mode: 'train'
-architecture: 'Res2Net50_26w_4s'
+ARCHITECTURE:
+    name: 'Res2Net50_26w_4s'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/Res2Net/Res2Net50_vd_26w_4s.yaml

 mode: 'train'
-architecture: 'Res2Net50_vd_26w_4s'
+ARCHITECTURE:
+    name: 'Res2Net50_vd_26w_4s'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt101_32x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt101_32x4d'
+ARCHITECTURE:
+    name: 'ResNeXt101_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt101_64x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt101_64x4d'
+ARCHITECTURE:
+    name: 'ResNeXt101_64x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt101_vd_32x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt101_vd_32x4d'
+ARCHITECTURE:
+    name: 'ResNeXt101_vd_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt101_vd_64x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt101_vd_64x4d'
+ARCHITECTURE:
+    name: 'ResNeXt101_vd_64x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt152_32x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt152_32x4d'
+ARCHITECTURE:
+    name: 'ResNeXt152_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt152_64x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt152_64x4d'
+ARCHITECTURE:
+    name: 'ResNeXt152_64x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt152_vd_32x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt152_vd_32x4d'
+ARCHITECTURE:
+    name: 'ResNeXt152_vd_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt152_vd_64x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt152_vd_64x4d'
+ARCHITECTURE:
+    name: 'ResNeXt152_vd_64x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt50_32x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt50_32x4d'
+ARCHITECTURE:
+    name: 'ResNeXt50_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt50_64x4d.yaml

 mode: 'train'
-architecture: "ResNeXt50_64x4d"
+ARCHITECTURE:
+    name: "ResNeXt50_64x4d"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt50_vd_32x4d.yaml

 mode: 'train'
-architecture: "ResNeXt50_vd_32x4d"
+ARCHITECTURE:
+    name: "ResNeXt50_vd_32x4d"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNeXt/ResNeXt50_vd_64x4d.yaml

 mode: 'train'
-architecture: 'ResNeXt50_vd_64x4d'
+ARCHITECTURE:
+    name: 'ResNeXt50_vd_64x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet101.yaml

 mode: 'train'
-architecture: 'ResNet101'
+ARCHITECTURE:
+    name: 'ResNet101'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet101_vd.yaml

 mode: 'train'
-architecture: 'ResNet101_vd'
+ARCHITECTURE:
+    name: 'ResNet101_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet152.yaml

 mode: 'train'
-architecture: 'ResNet152'
+ARCHITECTURE:
+    name: 'ResNet152'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet152_vd.yaml

 mode: 'train'
-architecture: 'ResNet152_vd'
+ARCHITECTURE:
+    name: 'ResNet152_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet18.yaml

 mode: 'train'
-architecture: 'ResNet18'
+ARCHITECTURE:
+    name: 'ResNet18'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet18_vd.yaml

 mode: 'train'
-architecture: 'ResNet18_vd'
+ARCHITECTURE:
+    name: 'ResNet18_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet200_vd.yaml

 mode: 'train'
-architecture: 'ResNet200_vd'
+ARCHITECTURE:
+    name: 'ResNet200_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet34.yaml

 mode: 'train'
-architecture: 'ResNet34'
+ARCHITECTURE:
+    name: 'ResNet34'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet34_vd.yaml

 mode: 'train'
-architecture: 'ResNet34_vd'
+ARCHITECTURE:
+    name: 'ResNet34_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet50.yaml

 mode: 'train'
-architecture: 'ResNet50'
+ARCHITECTURE:
+    name: 'ResNet50'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet50_vc.yaml

 mode: 'train'
-architecture: 'ResNet50_vc'
+ARCHITECTURE:
+    name: 'ResNet50_vc'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet/ResNet50_vd.yaml

 mode: 'train'
-architecture: 'ResNet50_vd'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ResNet_ACNet/ResNet_ACNet.yaml

 mode: 'train'
-architecture: "ResNet_ACNet"
+ARCHITECTURE:
+    name: "ResNet_ACNet"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SENet/SENet154_vd.yaml

 mode: 'train'
-architecture: 'SENet154_vd'
+ARCHITECTURE:
+    name: 'SENet154_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SENet/SE_ResNeXt101_32x4d.yaml

 mode: 'train'
-architecture: 'SE_ResNeXt101_32x4d'
+ARCHITECTURE:
+    name: 'SE_ResNeXt101_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SENet/SE_ResNeXt50_32x4d.yaml

 mode: 'train'
-architecture: 'SE_ResNeXt50_32x4d'
+ARCHITECTURE:
+    name: 'SE_ResNeXt50_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SENet/SE_ResNeXt50_vd_32x4d.yaml

 mode: 'train'
-architecture: 'SE_ResNeXt50_vd_32x4d'
+ARCHITECTURE:
+    name: 'SE_ResNeXt50_vd_32x4d'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SENet/SE_ResNet18_vd.yaml

 mode: 'train'
-architecture: 'SE_ResNet18_vd'
+ARCHITECTURE:
+    name: 'SE_ResNet18_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SENet/SE_ResNet34_vd.yaml

 mode: 'train'
-architecture: 'SE_ResNet34_vd'
+ARCHITECTURE:
+    name: 'SE_ResNet34_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SENet/SE_ResNet50_vd.yaml

 mode: 'train'
-architecture: 'SE_ResNet50_vd'
+ARCHITECTURE:
+    name: 'SE_ResNet50_vd'
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ShuffleNet/ShuffleNetV2.yaml

 mode: 'train'
-architecture: "ShuffleNetV2"
+ARCHITECTURE:
+    name: "ShuffleNetV2"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ShuffleNet/ShuffleNetV2_swish.yaml

 mode: 'train'
-architecture: "ShuffleNetV2_swish"
+ARCHITECTURE:
+    name: "ShuffleNetV2_swish"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ShuffleNet/ShuffleNetV2_x0_25.yaml

 mode: 'train'
-architecture: "ShuffleNetV2_x0_25"
+ARCHITECTURE:
+    name: "ShuffleNetV2_x0_25"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ShuffleNet/ShuffleNetV2_x0_33.yaml

 mode: 'train'
-architecture: "ShuffleNetV2_x0_33"
+ARCHITECTURE:
+    name: "ShuffleNetV2_x0_33"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ShuffleNet/ShuffleNetV2_x0_5.yaml

 mode: 'train'
-architecture: "ShuffleNetV2_x0_5"
+ARCHITECTURE:
+    name: "ShuffleNetV2_x0_5"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ShuffleNet/ShuffleNetV2_x1_5.yaml

 mode: 'train'
-architecture: "ShuffleNetV2_x1_5"
+ARCHITECTURE:
+    name: "ShuffleNetV2_x1_5"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/ShuffleNet/ShuffleNetV2_x2_0.yaml

 mode: 'train'
-architecture: "ShuffleNetV2_x2_0"
+ARCHITECTURE:
+    name: "ShuffleNetV2_x2_0"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SqueezeNet/SqueezeNet1_0.yaml

 mode: 'train'
-architecture: "SqueezeNet1_0"
+ARCHITECTURE:
+    name: "SqueezeNet1_0"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/SqueezeNet/SqueezeNet1_1.yaml

 mode: 'train'
-architecture: "SqueezeNet1_1"
+ARCHITECTURE:
+    name: "SqueezeNet1_1"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/VGG/VGG11.yaml

 mode: 'train'
-architecture: "VGG11"
+ARCHITECTURE:
+    name: "VGG11"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/VGG/VGG13.yaml

 mode: 'train'
-architecture: "VGG13"
+ARCHITECTURE:
+    name: "VGG13"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/VGG/VGG16.yaml

 mode: 'train'
-architecture: "VGG16"
+ARCHITECTURE:
+    name: "VGG16"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/VGG/VGG19.yaml

 mode: 'train'
-architecture: "VGG19"
+ARCHITECTURE:
+    name: "VGG19"
+
 pretrained_model: ""
 model_save_dir: "./output/"
 classes_num: 1000

configs/eval.yaml

 mode: 'valid'
-architecture: ""
+ARCHITECTURE:
+    name: ""
+
 pretrained_model: ""
 classes_num: 1000
 total_images: 1281167