diff --git a/mindspore/nn/acc/acc.py b/mindspore/nn/acc/acc.py
index 64f67115bcb..85034f68c2c 100644
--- a/mindspore/nn/acc/acc.py
+++ b/mindspore/nn/acc/acc.py
@@ -28,7 +28,7 @@ _acc_config_level = {
         "adasum": False},
     "O1": {
         "less_bn": True,
-        "grad_freeze": False,
+        "grad_freeze": True,
         "adasum": False},
     "O2": {
         "less_bn": True,
@@ -53,8 +53,8 @@ class AutoAcc:
         self._gc_flag = True
         self._param_groups = 10
         self._freeze_type = 1
-        self._freeze_p = 0.5
-        self._total_steps = -1
+        self._freeze_p = 0.7
+        self._total_steps = 65536
         self._gradient_groups = None
         self._get_configuration(kwargs)
         self._param_processer = ParameterProcess()
diff --git a/mindspore/nn/acc/grad_freeze.py b/mindspore/nn/acc/grad_freeze.py
index a664062c08c..288ce254c5c 100644
--- a/mindspore/nn/acc/grad_freeze.py
+++ b/mindspore/nn/acc/grad_freeze.py
@@ -59,10 +59,12 @@ class FreezeOpt(Cell):
             self.opt_class = type(opt.opt)
             self.opt_init_args = opt.opt.init_args
             self.lars_init_args = opt.init_args
+            self.parameters = opt.opt.parameters
         else:
             self.is_lars = False
             self.opt_class = type(opt)
             self.opt_init_args = opt.init_args
+            self.parameters = opt.parameters
         self.opts = []
 
         if train_parameter_groups is None:
diff --git a/mindspore/nn/wrap/grad_reducer.py b/mindspore/nn/wrap/grad_reducer.py
index db463585027..c41ad0397b7 100644
--- a/mindspore/nn/wrap/grad_reducer.py
+++ b/mindspore/nn/wrap/grad_reducer.py
@@ -30,14 +30,14 @@ reduce_opt = C.MultitypeFuncGraph("reduce_opt")
 def _init_allreduce_operators(length, split_indices, group=GlobalComm.WORLD_COMM_GROUP):
     """ initialize allreduce communication operators"""
     fusion_type = 2 ** 10
-    split = 1
+    split = 0
     fusion = ()
     for i in range(length):
         fusion = fusion + (fusion_type,)
-        if split_indices[split - 1] <= i + 1:
-            if split >= len(split_indices):
-                continue
-            fusion_type += split
+        if split >= len(split_indices):
+            continue
+        if split_indices[split] <= i:
+            fusion_type += 1
             split += 1
     index = tuple(range(1, length + 1))
     op_list = ()
diff --git a/model_zoo/official/cv/resnet/config/resnet101_imagenet2012_config.yaml b/model_zoo/official/cv/resnet/config/resnet101_imagenet2012_config.yaml
index 3516107b267..9c941604d4a 100644
--- a/model_zoo/official/cv/resnet/config/resnet101_imagenet2012_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet101_imagenet2012_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 32
 loss_scale: 1024
 momentum: 0.9
@@ -41,6 +42,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet18_cifar10_config.yaml b/model_zoo/official/cv/resnet/config/resnet18_cifar10_config.yaml
index d1113625aaa..b2340a4e038 100644
--- a/model_zoo/official/cv/resnet/config/resnet18_cifar10_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet18_cifar10_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 10
+train_image_size: 224
 batch_size: 32
 loss_scale: 1024
 momentum: 0.9
@@ -41,6 +42,7 @@ dataset: "cifar10"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet18_cifar10_config_gpu.yaml b/model_zoo/official/cv/resnet/config/resnet18_cifar10_config_gpu.yaml
index 073b380a6f7..c548e8c6252 100644
--- a/model_zoo/official/cv/resnet/config/resnet18_cifar10_config_gpu.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet18_cifar10_config_gpu.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 10
+train_image_size: 224
 batch_size: 32
 loss_scale: 1024
 momentum: 0.9
@@ -41,6 +42,7 @@ dataset: "cifar10"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config.yaml b/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config.yaml
index d52d7fdf531..f57e2822d5c 100644
--- a/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 256
 loss_scale: 1024
 momentum: 0.9
@@ -43,6 +44,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config_gpu.yaml b/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config_gpu.yaml
index 33232aacbc5..4aedb8bbeee 100644
--- a/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config_gpu.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet18_imagenet2012_config_gpu.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 256
 loss_scale: 1024
 momentum: 0.9
@@ -43,6 +44,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet34_imagenet2012_config.yaml b/model_zoo/official/cv/resnet/config/resnet34_imagenet2012_config.yaml
index 8e7d79ff503..7d94fdad15a 100644
--- a/model_zoo/official/cv/resnet/config/resnet34_imagenet2012_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet34_imagenet2012_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 256
 loss_scale: 1024
 momentum: 0.9
@@ -43,6 +44,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet50_cifar10_config.yaml b/model_zoo/official/cv/resnet/config/resnet50_cifar10_config.yaml
index fe0995baa9b..ec164440bfa 100644
--- a/model_zoo/official/cv/resnet/config/resnet50_cifar10_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet50_cifar10_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 10
+train_image_size: 224
 batch_size: 32
 loss_scale: 1024
 momentum: 0.9
@@ -41,6 +42,7 @@ dataset: "cifar10"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Acc_config.yaml b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Acc_config.yaml
index 5563840782e..5fe9da7348b 100644
--- a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Acc_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Acc_config.yaml
@@ -16,24 +16,25 @@ checkpoint_file_path: ""
 
 # ==============================================================================
 # Training options
-optimizer: "Momentum"
+optimizer: "LARS"
 infer_label: ""
 class_num: 1001
+train_image_size: 192
 batch_size: 256
 loss_scale: 1024
-momentum: 0.9
-weight_decay: 0.0001
-epoch_size: 90
+momentum: 0.85
+weight_decay: 5.0e-5
+epoch_size: 38
 pretrain_epoch_size: 0
-save_checkpoint: True
+save_checkpoint: False
 save_checkpoint_epochs: 5
 keep_checkpoint_max: 10
-warmup_epochs: 5
-lr_decay_mode: "cosine"
+warmup_epochs: 1
+lr_decay_mode: "poly"
 use_label_smooth: True
 label_smooth_factor: 0.1
 lr_init: 0
-lr_max: 0.8
+lr_max: 13.96
 lr_end: 0.0
 lars_epsilon: 0.0
 lars_coefficient: 0.001
@@ -43,18 +44,19 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
 save_best_ckpt: True
-eval_start_epoch: 40
+eval_start_epoch: 1
 eval_interval: 1
 enable_cache: False
 cache_session_id: ""
 mode_name: "GRAPH"
 acc_mode: "O1"
-conv_init: "XavierUniform"
-dense_init: "TruncatedNormal"
+conv_init: "TruncatedNormal"
+dense_init: "RandomNormal"
 all_reduce_fusion_config:
     - 85
     - 160
diff --git a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Ascend_Thor_config.yaml b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Ascend_Thor_config.yaml
index 4ca211328c6..f7e7db312a1 100644
--- a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Ascend_Thor_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_Ascend_Thor_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Thor"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 32
 loss_scale: 128
 momentum: 0.9
@@ -42,6 +43,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_GPU_Thor_config.yaml b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_GPU_Thor_config.yaml
index ceac3f78ae1..c9706ca3216 100644
--- a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_GPU_Thor_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_GPU_Thor_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Thor"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 32
 loss_scale: 128
 momentum: 0.9
@@ -42,6 +43,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_config.yaml b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_config.yaml
index 89938e3b496..40c7e785630 100644
--- a/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_config.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet50_imagenet2012_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 256
 loss_scale: 1024
 momentum: 0.9
@@ -43,6 +44,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 224
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/config/resnet_benchmark_GPU.yaml b/model_zoo/official/cv/resnet/config/resnet_benchmark_GPU.yaml
index 9491769f4b8..70a65fffec3 100644
--- a/model_zoo/official/cv/resnet/config/resnet_benchmark_GPU.yaml
+++ b/model_zoo/official/cv/resnet/config/resnet_benchmark_GPU.yaml
@@ -18,10 +18,12 @@ checkpoint_file_path: ''
 # Training options
 optimizer: "Momentum"
 infer_label: ""
+train_image_size: 224
 batch_size: 256
 epoch_size: 2
 print_per_steps: 20
 eval: False
+eval_image_size: 224
 save_ckpt: False
 mode_name: "GRAPH"
 dtype: "fp16"
diff --git a/model_zoo/official/cv/resnet/config/se-resnet50_imagenet2012_config.yaml b/model_zoo/official/cv/resnet/config/se-resnet50_imagenet2012_config.yaml
index 9b59225cb65..7938d0cde61 100644
--- a/model_zoo/official/cv/resnet/config/se-resnet50_imagenet2012_config.yaml
+++ b/model_zoo/official/cv/resnet/config/se-resnet50_imagenet2012_config.yaml
@@ -19,6 +19,7 @@ checkpoint_file_path: ""
 optimizer: "Momentum"
 infer_label: ""
 class_num: 1001
+train_image_size: 224
 batch_size: 32
 loss_scale: 1024
 momentum: 0.9
@@ -44,6 +45,7 @@ dataset: "imagenet2012"
 device_num: 1
 pre_trained: ""
 run_eval: False
+eval_image_size: 256
 eval_dataset_path: ""
 parameter_server: False
 filter_weight: False
diff --git a/model_zoo/official/cv/resnet/scripts/run_distribute_train.sh b/model_zoo/official/cv/resnet/scripts/run_distribute_train.sh
index c5f3903be96..979cb51b878 100755
--- a/model_zoo/official/cv/resnet/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/resnet/scripts/run_distribute_train.sh
@@ -121,8 +121,8 @@ do
     if [ $# == 5 ]
     then
       taskset -c $cmdopt python train.py --run_distribute=True --device_num=$RANK_SIZE --data_path=$PATH2 \
-      --run_eval=$RUN_EVAL --eval_data_path=$EVAL_DATASET_PATH --enable_cache=True --cache_session_id=$CACHE_SESSION_ID \
-      --config_path=$CONFIG_FILE --output_path './output' &> log &
+      --run_eval=$RUN_EVAL --eval_dataset_path=$EVAL_DATASET_PATH --enable_cache=True \
+      --cache_session_id=$CACHE_SESSION_ID --config_path=$CONFIG_FILE --output_path './output' &> log &
       if [ "x${RUN_EVAL}" == "xTrue" ]
       then
         echo -e "\nWhen training run is done, remember to shut down the cache server via \"cache_admin --stop\""
diff --git a/model_zoo/official/cv/resnet/src/dataset.py b/model_zoo/official/cv/resnet/src/dataset.py
index a9a85bd3b98..06f6ed95028 100755
--- a/model_zoo/official/cv/resnet/src/dataset.py
+++ b/model_zoo/official/cv/resnet/src/dataset.py
@@ -25,8 +25,8 @@ from src.model_utils.config import config
 from src.model_utils.device_adapter import get_device_num, get_rank_id
 
 
-def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False,
-                    enable_cache=False, cache_session_id=None):
+def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, train_image_size=224, eval_image_size=224,
+                    target="Ascend", distribute=False, enable_cache=False, cache_session_id=None):
     """
     create a train or evaluate cifar10 dataset for resnet50
     Args:
@@ -67,7 +67,7 @@ def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target=
         ]
 
     trans += [
-        C.Resize((224, 224)),
+        C.Resize((train_image_size, train_image_size)),
         C.Rescale(1.0 / 255.0, 0.0),
         C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
         C.HWC2CHW()
@@ -95,8 +95,8 @@ def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target=
     return data_set
 
 
-def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False,
-                    enable_cache=False, cache_session_id=None):
+def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, train_image_size=224, eval_image_size=224,
+                    target="Ascend", distribute=False, enable_cache=False, cache_session_id=None):
     """
     create a train or eval imagenet2012 dataset for resnet50
 
@@ -130,14 +130,13 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
         data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True,
                                          num_shards=device_num, shard_id=rank_id)
 
-    image_size = 224
     mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
     std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
 
     # define map operations
     if do_train:
         trans = [
-            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
             C.RandomHorizontalFlip(prob=0.5),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
@@ -146,7 +145,7 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
         trans = [
             C.Decode(),
             C.Resize(256),
-            C.CenterCrop(image_size),
+            C.CenterCrop(eval_image_size),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
         ]
@@ -174,8 +173,9 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
 
     return data_set
 
-def create_dataset_pynative(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False,
-                            enable_cache=False, cache_session_id=None):
+def create_dataset_pynative(dataset_path, do_train, repeat_num=1, batch_size=32, train_image_size=224,
+                            eval_image_size=224, target="Ascend", distribute=False, enable_cache=False,
+                            cache_session_id=None):
     """
     create a train or eval imagenet2012 dataset for resnet50 benchmark
 
@@ -209,14 +209,14 @@ def create_dataset_pynative(dataset_path, do_train, repeat_num=1, batch_size=32,
         data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=2, shuffle=True,
                                          num_shards=device_num, shard_id=rank_id)
 
-    image_size = 224
+
     mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
     std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
 
     # define map operations
     if do_train:
         trans = [
-            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
             C.RandomHorizontalFlip(prob=0.5),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
@@ -225,7 +225,7 @@ def create_dataset_pynative(dataset_path, do_train, repeat_num=1, batch_size=32,
         trans = [
             C.Decode(),
             C.Resize(256),
-            C.CenterCrop(image_size),
+            C.CenterCrop(eval_image_size),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
         ]
@@ -253,8 +253,8 @@ def create_dataset_pynative(dataset_path, do_train, repeat_num=1, batch_size=32,
 
     return data_set
 
-def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False,
-                    enable_cache=False, cache_session_id=None):
+def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, train_image_size=224, eval_image_size=224,
+                    target="Ascend", distribute=False, enable_cache=False, cache_session_id=None):
     """
     create a train or eval imagenet2012 dataset for resnet101
     Args:
@@ -285,14 +285,14 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target=
     else:
         data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
                                          num_shards=device_num, shard_id=rank_id)
-    image_size = 224
+
     mean = [0.475 * 255, 0.451 * 255, 0.392 * 255]
     std = [0.275 * 255, 0.267 * 255, 0.278 * 255]
 
     # define map operations
     if do_train:
         trans = [
-            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
             C.RandomHorizontalFlip(rank_id / (rank_id + 1)),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
@@ -301,7 +301,7 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target=
         trans = [
             C.Decode(),
             C.Resize(256),
-            C.CenterCrop(image_size),
+            C.CenterCrop(eval_image_size),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
         ]
@@ -329,8 +329,8 @@ def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target=
     return data_set
 
 
-def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False,
-                    enable_cache=False, cache_session_id=None):
+def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, train_image_size=224, eval_image_size=224,
+                    target="Ascend", distribute=False, enable_cache=False, cache_session_id=None):
     """
     create a train or eval imagenet2012 dataset for se-resnet50
 
@@ -362,14 +362,14 @@ def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target=
     else:
         data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True,
                                          num_shards=device_num, shard_id=rank_id)
-    image_size = 224
+
     mean = [123.68, 116.78, 103.94]
     std = [1.0, 1.0, 1.0]
 
     # define map operations
     if do_train:
         trans = [
-            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
             C.RandomHorizontalFlip(prob=0.5),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
@@ -378,7 +378,7 @@ def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target=
         trans = [
             C.Decode(),
             C.Resize(292),
-            C.CenterCrop(256),
+            C.CenterCrop(eval_image_size),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
         ]
diff --git a/model_zoo/official/cv/resnet/train.py b/model_zoo/official/cv/resnet/train.py
index 7048543b7ac..a2c0ad89a7c 100755
--- a/model_zoo/official/cv/resnet/train.py
+++ b/model_zoo/official/cv/resnet/train.py
@@ -199,7 +199,9 @@ def run_eval(target, model, ckpt_save_dir, cb):
         if config.eval_dataset_path is None or (not os.path.isdir(config.eval_dataset_path)):
             raise ValueError("{} is not a existing path.".format(config.eval_dataset_path))
         eval_dataset = create_dataset(dataset_path=config.eval_dataset_path, do_train=False,
-                                      batch_size=config.batch_size, target=target, enable_cache=config.enable_cache,
+                                      batch_size=config.batch_size, train_image_size=config.train_image_size,
+                                      eval_image_size=config.eval_image_size,
+                                      target=target, enable_cache=config.enable_cache,
                                       cache_session_id=config.cache_session_id)
         eval_param_dict = {"model": model, "dataset": eval_dataset, "metrics_name": "acc"}
         eval_cb = EvalCallBack(apply_eval, eval_param_dict, interval=config.eval_interval,
@@ -226,7 +228,8 @@ def train_net():
     target = config.device_target
     set_parameter()
     dataset = create_dataset(dataset_path=config.data_path, do_train=True, repeat_num=1,
-                             batch_size=config.batch_size, target=target,
+                             batch_size=config.batch_size, train_image_size=config.train_image_size,
+                             eval_image_size=config.eval_image_size, target=target,
                              distribute=config.run_distribute)
     step_size = dataset.get_dataset_size()
     net = resnet(class_num=config.class_num)