diff --git a/research/cv/glore_res/README_CN.md b/research/cv/glore_res/README_CN.md
index 153e6551b5ee96bd5a7115abd4af95f1b693c29a..ead07cb539af3114f1a5976e5b296df0115b0db8 100644
--- a/research/cv/glore_res/README_CN.md
+++ b/research/cv/glore_res/README_CN.md
@@ -30,9 +30,11 @@
     - [性能](#性能)
         - [训练性能](#训练性能)
             - [ImageNet2012上的Glore_resnet50](#imagenet2012上的glore_resnet50)
+            - [ImageNet2012上的Glore_resnet101](#imagenet2012上的glore_resnet101)
             - [ImageNet2012上的Glore_resnet200](#imagenet2012上的glore_resnet200)
         - [推理性能](#推理性能)
             - [ImageNet2012上的Glore_resnet50](#imagenet2012上的glore_resnet50)
+            - [ImageNet2012上的Glore_resnet101](#imagenet2012上的glore_resnet101)
             - [ImageNet2012上的Glore_resnet200](#imagenet2012上的glore_resnet200)
 - [随机情况说明](#随机情况说明)
 - [ModelZoo主页](#modelzoo主页)
@@ -100,26 +102,26 @@ glore_res200网络模型的backbone是ResNet200, 在Stage2, Stage3中分别均
 
 ```bash
 # 分布式训练
-用法:bash run_distribute_train.sh [DATASET_PATH] [RANK_TABLE] [CONFIG_PATH]
+用法:bash run_distribute_train.sh [TRAIN_DATA_PATH] [RANK_TABLE] [CONFIG_PATH] [EVAL_DATA_PATH]
 
 # 单机训练
-用法:bash run_standalone_train.sh [DATASET_PATH] [DEVICE_ID] [CONFIG_PATH]
+用法:bash run_standalone_train.sh [TRAIN_DATA_PATH] [DEVICE_ID] [CONFIG_PATH] [EVAL_DATA_PATH]
 
 # 运行评估示例
-用法:bash run_eval.sh [DATASET_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
+用法:bash run_eval.sh [EVAL_DATA_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
 ```
 
 - GPU处理器环境运行
 
 ```bash
 # 分布式训练
-用法:bash run_distribute_train_gpu.sh [DATASET_PATH] [RANK_SIZE] [CONFIG_PATH]
+用法:bash run_distribute_train_gpu.sh [TRAIN_DATA_PATH] [EVAL_DATA_PATH] [RANK_SIZE] [CONFIG_PATH]
 
 # 单机训练
-用法:bash run_standalone_train_gpu.sh [DATASET_PATH] [CONFIG_PATH]
+用法:bash run_standalone_train.sh [TRAIN_DATA_PATH] [DEVICE_ID] [CONFIG_PATH] [EVAL_DATA_PATH]
 
 # 运行评估示例
-用法:bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
+用法:bash run_eval.sh [EVAL_DATA_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
 ```
 
   对于分布式训练，需要提前创建JSON格式的hccl配置文件。
@@ -136,6 +138,12 @@ glore_res200网络模型的backbone是ResNet200, 在Stage2, Stage3中分别均
 .
 └──Glore_resnet
   ├── README.md
+  ├── config
+    ├── config_resnet50_ascend.yaml            # Ascend glore_resnet50配置
+    ├── config_resnet50_gpu.yaml            # GPU glore_resnet50配置
+    ├── config_resnet101_gpu.yaml            # GPU glore_resnet101配置
+    ├── config_resnet200_ascend.yaml            # Ascend glore_resnet200配置
+    └── config_resnet200_gpu.yaml            # GPU glore_resnet200配置
   ├── script
     ├── run_distribute_train.sh            # 启动Ascend分布式训练（8卡）
     ├── run_distribute_train_gpu.sh        # 启动GPU分布式训练（8卡）
@@ -212,6 +220,27 @@ glore_res200网络模型的backbone是ResNet200, 在Stage2, Stage3中分别均
 "lr_end":0.0,                    # 最小学习率
 ```
 
+- 配置Glore_resnet101在ImageNet2012数据集参数(GPU)。
+
+```text
+"class_num":1000,                # 数据集类数
+"batch_size":64,                 # 输入张量的批次大小
+"loss_scale":1024,               # 损失等级
+"momentum":0.08,                 # 动量优化器
+"weight_decay":0.0002,           # 权重衰减
+"epoch_size":150,                # 此值仅适用于训练；应用于推理时固定为1
+"pretrain_epoch_size":0,         # 加载预训练检查点之前已经训练好的模型的周期大小；实际训练周期大小等于epoch_size减去pretrain_epoch_size
+"save_checkpoint":True,          # 是否保存检查点
+"save_checkpoint_epochs":5,      # 两个检查点之间的周期间隔；默认情况下，最后一个检查点将在最后一个周期完成后保存
+"keep_checkpoint_max":10,        # 只保存最后一个keep_checkpoint_max检查点
+"save_checkpoint_path":"./",     # 检查点相对于执行路径的保存路径
+"warmup_epochs":0,               # 热身周期数
+"lr_decay_mode":"poly",          # 用于生成学习率的衰减模式
+"lr_init":0.1,                   # 初始学习率
+"lr_max":0.4,                    # 最大学习率
+"lr_end":0.0,                    # 最小学习率
+```
+
 - 配置Glore_resnet200在ImageNet2012数据集参数(Ascend)。
 
 ```text
@@ -264,13 +293,13 @@ glore_res200网络模型的backbone是ResNet200, 在Stage2, Stage3中分别均
 
 ```text
 # 分布式训练
-用法:bash run_distribute_train.sh [DATASET_PATH] [RANK_TABLE] [CONFIG_PATH]
+用法:bash run_distribute_train.sh [TRAIN_DATA_PATH] [RANK_TABLE] [CONFIG_PATH] [EVAL_DATA_PATH]
 
 # 单机训练
-用法:bash run_standalone_train.sh [DATASET_PATH] [DEVICE_ID] [CONFIG_PATH]
+用法:bash run_standalone_train.sh [TRAIN_DATA_PATH] [RANK_TABLE] [CONFIG_PATH] [EVAL_DATA_PATH]
 
 # 运行推理示例
-用法:bash run_eval.sh [DATASET_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
+用法:bash run_eval.sh [EVAL_DATA_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
 ```
 
 分布式训练需要提前创建JSON格式的HCCL配置文件。
@@ -283,13 +312,14 @@ glore_res200网络模型的backbone是ResNet200, 在Stage2, Stage3中分别均
 
 ```text
 # 分布式训练
-用法:bash run_distribute_train_gpu.sh [DATASET_PATH] [RANK_SIZE] [CONFIG_PATH]
+用法:bash run_distribute_train_gpu.sh [TRAIN_DATA_PATH] [EVAL_DATA_PATH] [RANK_SIZE] [CONFIG_PATH]
+示例:bash run_distribute_train_gpu.sh ~/Imagenet_Original/train/ ~/Imagenet_Original/val/ 8 ../config/config_resnet50_gpu.yaml
 
 # 单机训练
-用法:bash run_standalone_train_gpu.sh [DATASET_PATH] [CONFIG_PATH]
+用法:bash run_standalone_train.sh [TRAIN_DATA_PATH] [CONFIG_PATH] [EVAL_DATA_PATH]
 
 # 运行推理示例
-用法:bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
+用法:bash run_eval.sh [EVAL_DATA_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
 ```
 
 ## 训练结果
@@ -306,6 +336,18 @@ epoch:5 step:1251, loss is 3.3024906
 ...
 ```
 
+- 使用ImageNet2012数据集训练Glore_resnet101（8 pcs）
+
+```text
+# 分布式训练结果（8P）
+epoch:1 step:5004, loss is 4.7398486
+epoch:2 step:5004, loss is 4.129058
+epoch:3 step:5004, loss is 3.5034246
+epoch:4 step:5004, loss is 3.4452052
+epoch:5 step:5004, loss is 3.148675
+...
+```
+
 - 使用ImageNet2012数据集训练Glore_resnet200（8 pcs）
 
 ```text
@@ -326,24 +368,24 @@ epoch:5 step:1251, loss is 4.080069
 
 ```bash
 # 推理
-Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
+Usage: bash run_eval.sh [EVAL_DATA_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
 ```
 
 ```bash
 # 推理示例
-bash run_eval.sh ~/Imagenet_Original/ 0 ~/glore_resnet200-150_1251.ckpt ../config/config_resnet50_gpu.yaml
+bash run_eval.sh ~/Imagenet_Original/val/ 0 ~/glore_resnet200-150_1251.ckpt ../config/config_resnet50_gpu.yaml
 ```
 
 #### GPU处理器环境运行
 
 ```bash
 # 推理
-Usage: bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH] [CONFIG_PATH]
+Usage: bash run_eval_gpu.sh [EVAL_DATA_PATH] [DEVICE_ID] [CHECKPOINT_PATH] [CONFIG_PATH]
 ```
 
 ```bash
 # 推理示例
-bash run_eval.sh ~/Imagenet  ~/glore_resnet200-150_2502.ckpt ../config/config_resnet50_gpu.yaml
+bash run_eval.sh ~/Imagenet/val/  ~/glore_resnet200-150_2502.ckpt ../config/config_resnet50_gpu.yaml
 ```
 
 ## 推理结果
@@ -378,6 +420,26 @@ result:{'top_1 acc':0.802303685897436}
 | 微调检查点| 233.46M（.ckpt文件）                                      |233.46M（.ckpt文件）                          |
 | 脚本                    | [链接](https://gitee.com/mindspore/models/tree/master/research/cv/glore_res) |
 
+#### ImageNet2012上的Glore_resnet101
+
+| 参数                 |          GPU                       |
+| --------------------------|------------------------------------|
+| 模型版本              |Glore_resnet101                     |
+| 资源                   |GPU-V100 PCIE 32G                     |
+| 上传日期              |2021-10-22                         |
+| MindSpore版本          | r1.5                                  |1.5.0                          |
+| 数据集                    | ImageNet2012                      |
+| 训练参数        |epoch=150, steps per epoch=5004, batch_size = 32 |
+| 优化器                  | NAG                                           |
+| 损失函数              |SoftmaxCrossEntropyExpand          |
+| 输出                    |概率                               |
+| 损失                       |1.7463021                        |
+| 速度                      |33 毫秒/步（8卡）             |
+| 总时长                 |30 小时                          |
+| 参数(M)             |57                          |
+| 微调检查点|579.06M（.ckpt文件）                          |
+| 脚本                    | [链接](https://gitee.com/mindspore/models/tree/master/research/cv/glore_res) |
+
 #### ImageNet2012上的Glore_resnet200
 
 | 参数                 | Ascend 910                                   |          GPU                       |
@@ -413,6 +475,19 @@ result:{'top_1 acc':0.802303685897436}
 | 输出             | 概率                     |概率                         |
 | 准确性            | 8卡: 78.44%             |8卡：78.50%                 |
 
+#### ImageNet2012上的Glore_resnet101
+
+| 参数          | GPU                      |
+| ------------------- | ----------------------|
+| 模型版本       | Glore_resnet101              |
+| 资源            | GPU-V100(SXM2)                |
+| 上传日期       | 2021-10-22                  |
+| MindSpore版本   | 1.5.0                 |
+| 数据集             | ImageNet2012测试集(6.4GB)             |
+| batch_size          | 32                   |
+| 输出             | 概率                     |
+| 准确性            | 8卡: 79.663%            |
+
 #### ImageNet2012上的Glore_resnet200
 
 | 参数          | Ascend                      |   GPU                        |
@@ -432,4 +507,4 @@ transform_utils.py中使用数据增强时采用了随机选择策略，train.py
 
 # ModelZoo主页
 
- 请浏览官网[主页](https://gitee.com/mindspore/models)
+ 请浏览官网[主页](https://gitee.com/mindspore/models/)
diff --git a/research/cv/glore_res/config/config_resnet101_ascend.yaml b/research/cv/glore_res/config/config_resnet101_ascend.yaml
deleted file mode 100644
index 601db116000d7c783cae11fd2de1d90038b463e6..0000000000000000000000000000000000000000
--- a/research/cv/glore_res/config/config_resnet101_ascend.yaml
+++ /dev/null
@@ -1,70 +0,0 @@
-# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
-isModelArts: false
-# Url for modelarts
-data_url: ""
-train_url: ""
-checkpoint_url: ""
-# Path for local
-run_distribute: true
-enable_profiling: False
-data_path: "/cache/data"
-output_path: "/cache/train"
-load_path: "/cache/checkpoint_path/"
-device_target: "Ascend"
-checkpoint_path: "./checkpoint/"
-
-# ==============================================================================
-# Training options
-batch_size: 80
-class_num: 1000
-epoch_size: 150
-keep_checkpoint_max: 10
-loss_scale: 1024
-lr_decay_mode: poly
-lr_end: 0
-lr_init: 0.1
-lr_max: 0.4
-momentum: 0.08
-pretrain_epoch_size: 0
-use_glore: true
-save_checkpoint: true
-save_checkpoint_epochs: 5
-save_checkpoint_path: ./
-use_glore: true
-use_label_smooth: false
-warmup_epochs: 0
-weight_decay: 0.0002
-net: "resnet101"
-cast_fp16: true
-
-device_target: "Ascend"
-device_id: 0
-device_num: 8
-data_url: ""
-pretrained_ckpt: ""
-parameter_server: ""
-
-
-# Export options
-device_id: 0
-file_name: "resnet200"
-file_format: "MINDIR"
-ckpt_url: ""
-
-# Image options
-image_size: 224
-
----
-# Help description for each configuration
-enable_modelarts: "Whether training on modelarts, default: False"
-data_url: "Dataset url for obs"
-checkpoint_url: "The location of checkpoint for obs"
-data_path: "Dataset path for local"
-output_path: "Training output path for local"
-load_path: "The location of checkpoint for obs"
-device_target: "Target device type, available: [Ascend, GPU, CPU]"
-enable_profiling: "Whether enable profiling while training, default: False"
-num_classes: "Class for dataset"
-batch_size: "Batch size for training and evaluation"
-epoch_size: "Total training epochs."
-checkpoint_path: "The location of the checkpoint file."
\ No newline at end of file
diff --git a/research/cv/glore_res/config/config_resnet101_gpu.yaml b/research/cv/glore_res/config/config_resnet101_gpu.yaml
index 53b09c787e482c1177d64d9f0aa18b13d4aae03c..d72b2bd04a3fc77048fa9fa21c89583b59d83ed8 100644
--- a/research/cv/glore_res/config/config_resnet101_gpu.yaml
+++ b/research/cv/glore_res/config/config_resnet101_gpu.yaml
@@ -40,6 +40,7 @@ device_target: "GPU"
 device_id: 0
 device_num: 8
 data_url: ""
+eval_data_url: ""
 pretrained_ckpt: ""
 parameter_server: ""
 
diff --git a/research/cv/glore_res/config/config_resnet200_ascend.yaml b/research/cv/glore_res/config/config_resnet200_ascend.yaml
index 72bfee59559fbf5755b5dfb0b7f5b125eefc1103..e9c84ef6338a3763c5de9d2cb6cc82917166cd5b 100644
--- a/research/cv/glore_res/config/config_resnet200_ascend.yaml
+++ b/research/cv/glore_res/config/config_resnet200_ascend.yaml
@@ -41,6 +41,7 @@ device_target: "Ascend"
 device_id: 0
 device_num: 8
 data_url: ""
+eval_data_url: ""
 pretrained_ckpt: ""
 parameter_server: ""
 
diff --git a/research/cv/glore_res/config/config_resnet200_gpu.yaml b/research/cv/glore_res/config/config_resnet200_gpu.yaml
index c8a9af638282652ae91f188850ddb509969bb370..d4c78f463742db8fdf106818b2cff4ecca2f376f 100644
--- a/research/cv/glore_res/config/config_resnet200_gpu.yaml
+++ b/research/cv/glore_res/config/config_resnet200_gpu.yaml
@@ -40,6 +40,7 @@ device_target: "GPU"
 device_id: 0
 device_num: 8
 data_url: ""
+eval_data_url: ""
 pretrained_ckpt: ""
 parameter_server: ""
 
diff --git a/research/cv/glore_res/config/config_resnet50_ascend.yaml b/research/cv/glore_res/config/config_resnet50_ascend.yaml
index 1eb3e9d0d4968b8d4a156ab8c402cbdf7b8d58ab..69b13936421bc2910b893073831e4d8e9cc16f2f 100644
--- a/research/cv/glore_res/config/config_resnet50_ascend.yaml
+++ b/research/cv/glore_res/config/config_resnet50_ascend.yaml
@@ -44,6 +44,7 @@ device_target: "Ascend"
 device_id: 0
 device_num: 8
 data_url: ""
+eval_data_url: ""
 pretrained_ckpt: ""
 parameter_server: ""
 
diff --git a/research/cv/glore_res/config/config_resnet50_gpu.yaml b/research/cv/glore_res/config/config_resnet50_gpu.yaml
index bdfa19dda38acb4de90b83bbadb68c5a88e7b329..e2eede34e13c037659213848d8319840d06a903c 100644
--- a/research/cv/glore_res/config/config_resnet50_gpu.yaml
+++ b/research/cv/glore_res/config/config_resnet50_gpu.yaml
@@ -44,6 +44,7 @@ device_target: "GPU"
 device_id: 0
 device_num: 8
 data_url: ""
+eval_data_url: ""
 pretrained_ckpt: ""
 parameter_server: ""
 
diff --git a/research/cv/glore_res/eval.py b/research/cv/glore_res/eval.py
index 2576a33ffd133dda70ef082c43ad59f615c3788a..e33538e3bd05eb4d1e3ab6f51d83ed777a8a49ee 100644
--- a/research/cv/glore_res/eval.py
+++ b/research/cv/glore_res/eval.py
@@ -25,7 +25,7 @@ from mindspore import context
 from mindspore import dataset as de
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from src.glore_resnet import glore_resnet200, glore_resnet50
+from src.glore_resnet import glore_resnet200, glore_resnet50, glore_resnet101
 from src.dataset import create_eval_dataset
 from src.dataset import create_dataset_ImageNet as ImageNet
 from src.loss import CrossEntropySmooth, SoftmaxCrossEntropyExpand
@@ -50,13 +50,13 @@ if __name__ == '__main__':
                         device_id=device_id)
 
     # dataset
-    eval_dataset_path = os.path.join(config.data_url, 'val')
+    eval_dataset_path = os.path.abspath(config.eval_data_url)
     if config.isModelArts:
-        mox.file.copy_parallel(src_url=config.data_url, dst_url='/cache/dataset')
+        mox.file.copy_parallel(src_url=config.eval_data_url, dst_url='/cache/dataset')
         eval_dataset_path = '/cache/dataset/'
     if config.net == 'resnet50':
         predict_data = create_eval_dataset(dataset_path=eval_dataset_path, repeat_num=1, batch_size=config.batch_size)
-    elif config.net == 'resnet200':
+    else:
         predict_data = ImageNet(dataset_path=eval_dataset_path,
                                 do_train=False,
                                 repeat_num=1,
@@ -71,6 +71,8 @@ if __name__ == '__main__':
         net = glore_resnet50(class_num=config.class_num, use_glore=config.use_glore)
     elif config.net == 'resnet200':
         net = glore_resnet200(class_num=config.class_num, use_glore=config.use_glore)
+    elif config.net == 'resnet101':
+        net = glore_resnet101(class_num=config.class_num, use_glore=config.use_glore)
 
     # load checkpoint
     param_dict = load_checkpoint(config.ckpt_url)
diff --git a/research/cv/glore_res/scripts/run_distribute_train.sh b/research/cv/glore_res/scripts/run_distribute_train.sh
index d686ca0a6aeeed71ef879d4053836f689ebc2a93..8525ec846edb8b849c9d6072dae158afe0a72210 100644
--- a/research/cv/glore_res/scripts/run_distribute_train.sh
+++ b/research/cv/glore_res/scripts/run_distribute_train.sh
@@ -21,9 +21,9 @@ echo "For example: bash run_distribute_train.sh /path/dataset /path/rank_table .
 echo "It is better to use the absolute path."
 echo "=============================================================================================================="
 set -e
-if [ $# != 3 ]
+if [ $# != 4 ]
 then
-    echo "Usage: bash run_distribute_train.sh [DATASET_PATH] [RANK_TABLE] [CONFIG_PATH]"
+    echo "Usage: bash run_distribute_train.sh [TRAIN_DATA_PATH] [RANK_TABLE] [CONFIG_PATH] [EVAL_DATA_PATH]"
     exit 1
 fi
 get_real_path(){
@@ -37,6 +37,7 @@ DATA_PATH=$(get_real_path $1)
 export DATA_PATH=${DATA_PATH}
 RANK_TABLE=$(get_real_path $2)
 CONFIG_PATH=$(get_real_path $3)
+EVAL_DATA_PATH=$(get_real_path $4)
 export RANK_TABLE_FILE=${RANK_TABLE}
 export RANK_SIZE=8
 
@@ -58,7 +59,7 @@ do
     export RANK_ID=$i
     echo "start training for device $i"
     env > env$i.log
-    python3 train.py --data_url $1 --isModelArts False --run_distribute True --config_path=$CONFIG_PATH > train$i.log 2>&1 &
+    python3 train.py --data_url $DATA_PATH --isModelArts False --run_distribute True --config_path=$CONFIG_PATH --eval_data_url $EVAL_DATA_PATH > train$i.log 2>&1 &
     if [ $? -eq 0 ];then
         echo "start training for device$i" 
     else
diff --git a/research/cv/glore_res/scripts/run_distribute_train_gpu.sh b/research/cv/glore_res/scripts/run_distribute_train_gpu.sh
index 5a9b1ef02b21b087c85550eae94a9faf6ef055a0..8aa31b633f5dcb5c5e8453a3788e7e845dea712d 100644
--- a/research/cv/glore_res/scripts/run_distribute_train_gpu.sh
+++ b/research/cv/glore_res/scripts/run_distribute_train_gpu.sh
@@ -20,9 +20,9 @@ echo "bash run_distribute_train_gpu.sh DATA_PATH RANK_SIZE CONFIG_PATH"
 echo "For example: bash run_distribute_train.sh /path/dataset 8 ../config/config_resnet50_gpu.yaml"
 echo "It is better to use the absolute path."
 echo "=============================================================================================================="
-if [ $# != 3 ]
+if [ $# != 4 ]
 then
-    echo "Usage: bash run_distribute_train_gpu.sh [DATASET_PATH] [RANK_SIZE] [CONFIG_PATH]"
+    echo "Usage: bash run_distribute_train_gpu.sh [TRAIN_DATA_PATH] [EVAL_DATA_PATH] [RANK_SIZE] [CONFIG_PATH]"
     exit 1
 fi
 
@@ -35,12 +35,13 @@ get_real_path(){
 }
 
 set -e
-DEVICE_NUM=$2
+DEVICE_NUM=$3
 DATA_PATH=$(get_real_path $1)
-CONFIG_PATH=$(get_real_path $3)
+EVAL_DATA_PATH=$(get_real_path $2)
+CONFIG_PATH=$(get_real_path $4)
 export DATA_PATH=${DATA_PATH}
-export DEVICE_NUM=$2
-export RANK_SIZE=$2
+export DEVICE_NUM=$3
+export RANK_SIZE=$3
 export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 
 cd ../
@@ -56,5 +57,5 @@ env > env.log
 echo "start training"
     mpirun -n $2 --allow-run-as-root \
            python3 train.py --data_url=$DATA_PATH --isModelArts=False --run_distribute=True \
-           --device_target="GPU" --config_path=$CONFIG_PATH --device_num $2 > train.log 2>&1 &
+           --device_target="GPU" --config_path=$CONFIG_PATH --eval_data_url=$EVAL_DATA_PATH --device_num $2 > train.log 2>&1 &
 
diff --git a/research/cv/glore_res/scripts/run_eval.sh b/research/cv/glore_res/scripts/run_eval.sh
index 81711c5ca96606c9f344280d1cb627fc1bee88e0..5ee2be156c136f10fa4e5c57066cfa331e1b01ee 100644
--- a/research/cv/glore_res/scripts/run_eval.sh
+++ b/research/cv/glore_res/scripts/run_eval.sh
@@ -51,7 +51,7 @@ cd ../
 export DEVICE_ID=$2
 export RANK_ID=0
 env > env0.log
-python3 eval.py --data_url $1 --isModelArts False --device_id $2 --ckpt_url $CKPT_PATH --config_path=$CONFIG_PATH > eval.log 2>&1
+python3 eval.py --eval_data_url $1 --isModelArts False --device_id $2 --ckpt_url $CKPT_PATH --config_path=$CONFIG_PATH > eval.log 2>&1
 
 if [ $? -eq 0 ];then
     echo "testing success"
diff --git a/research/cv/glore_res/train.py b/research/cv/glore_res/train.py
index 71dfa416934d6263b80c5e65e4739094963ec28e..ca95099a9029b54d566af160d46772168602f22c 100644
--- a/research/cv/glore_res/train.py
+++ b/research/cv/glore_res/train.py
@@ -86,8 +86,8 @@ if __name__ == '__main__':
     # get device_num, device_id after device init
     device_num, device_id = _get_rank_info()
     #create dataset
-    train_dataset_path = os.path.join(config.data_url, 'train')
-    eval_dataset_path = os.path.join(config.data_url, 'val')
+    train_dataset_path = os.path.abspath(config.data_url)
+    eval_dataset_path = os.path.abspath(config.eval_data_url)
 
     # download dataset from obs to cache if train on ModelArts
     if config.net == 'resnet50':