diff --git a/configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml b/configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..827029b7dd24ff1a28c98db24b31b7e01ec15925
--- /dev/null
+++ b/configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
@@ -0,0 +1,70 @@
+mode: 'train'
+ARCHITECTURE:
+    name: 'MobileNetV3_large_x1_0'
+pretrained_model: "./pretrained/MobileNetV3_large_x1_0_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.00375
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000001
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 4
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
diff --git a/configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml b/configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3f025437922a9a67bb71ed94fc25d40317d0f6a7
--- /dev/null
+++ b/configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
@@ -0,0 +1,75 @@
+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0'
+
+pretrained_model:
+    - "./pretrain/flowers102_R50_vd_final/ppcls"
+    - "./pretrained/MobileNetV3_large_x1_0_pretrained/"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 7169
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+use_distillation: True
+
+LEARNING_RATE:
+    function: 'Cosine'
+    params:                   
+        lr: 0.0125
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.00007
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_test_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 4
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
diff --git a/configs/quick_start/ResNet50_vd.yaml b/configs/quick_start/ResNet50_vd.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..913090921da7111d1e0625016158fec8af8c8bcf
--- /dev/null
+++ b/configs/quick_start/ResNet50_vd.yaml
@@ -0,0 +1,70 @@
+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.0125
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.00001
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 4
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
diff --git a/configs/quick_start/ResNet50_vd_finetune.yaml b/configs/quick_start/ResNet50_vd_finetune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..415e0e80d1d38da991c6431f406e5d39dc03bb6f
--- /dev/null
+++ b/configs/quick_start/ResNet50_vd_finetune.yaml
@@ -0,0 +1,70 @@
+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+pretrained_model: "./pretrained/ResNet50_vd_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.00375
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000001
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 4
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
diff --git a/configs/quick_start/ResNet50_vd_ssld_finetune.yaml b/configs/quick_start/ResNet50_vd_ssld_finetune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d6bf863414a23dd9de3e3c490e5c09a7e20e01c7
--- /dev/null
+++ b/configs/quick_start/ResNet50_vd_ssld_finetune.yaml
@@ -0,0 +1,72 @@
+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+    params:
+        lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
+pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.00375
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000001
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 4
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
diff --git a/configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml b/configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..629f050eda26edae9efe512980dc7faea28fc39f
--- /dev/null
+++ b/configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
@@ -0,0 +1,74 @@
+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+    params:
+        lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
+pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 20
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.00375
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000001
+
+TRAIN:
+    batch_size: 32
+    num_workers: 4
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - RandomErasing:
+            EPSILON: 0.5
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 4
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
diff --git a/ppcls/modeling/architectures/__init__.py b/ppcls/modeling/architectures/__init__.py
index ae2ac733536b72b3087bf9001e1408caab020723..34be3ce2addc0a8320e4b3606e1d87f0873d2a47 100644
--- a/ppcls/modeling/architectures/__init__.py
+++ b/ppcls/modeling/architectures/__init__.py
@@ -44,4 +44,4 @@ from .darts_gs import DARTS_GS_6M, DARTS_GS_4M
 from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet
 
 # distillation model
-from .distillation_models import ResNet50_vd_distill_MobileNetV3_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd
+from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd
diff --git a/ppcls/modeling/architectures/distillation_models.py b/ppcls/modeling/architectures/distillation_models.py
index 258627f8059eb9804f5b7cf15f6b44f621029b58..f5f24b36a260f7d816a164dd0a8e86266550b0dc 100644
--- a/ppcls/modeling/architectures/distillation_models.py
+++ b/ppcls/modeling/architectures/distillation_models.py
@@ -27,12 +27,12 @@ from .mobilenet_v3 import MobileNetV3_large_x1_0
 from .resnext101_wsl import ResNeXt101_32x16d_wsl
 
 __all__ = [
-    'ResNet50_vd_distill_MobileNetV3_x1_0',
+    'ResNet50_vd_distill_MobileNetV3_large_x1_0',
     'ResNeXt101_32x16d_wsl_distill_ResNet50_vd'
 ]
 
 
-class ResNet50_vd_distill_MobileNetV3_x1_0():
+class ResNet50_vd_distill_MobileNetV3_large_x1_0():
     def net(self, input, class_dim=1000):
         # student
         student = MobileNetV3_large_x1_0()
diff --git a/ppcls/utils/save_load.py b/ppcls/utils/save_load.py
index 986d5ac787c75dc34340c82ce86b0c8e31530664..673e54304b84bd962486e5bdc61b4ddcc1fa511d 100644
--- a/ppcls/utils/save_load.py
+++ b/ppcls/utils/save_load.py
@@ -118,7 +118,10 @@ def init_model(config, program, exe):
 
     pretrained_model = config.get('pretrained_model')
     if pretrained_model:
-        load_params(exe, program, pretrained_model)
+        if not isinstance(pretrained_model, list):
+            pretrained_model = [pretrained_model]
+        for pretrain in pretrained_model:
+            load_params(exe, program, pretrain)
         logger.info("Finish initing model from {}".format(pretrained_model))