diff --git a/model_zoo/official/cv/resnet/README.md b/model_zoo/official/cv/resnet/README.md
index 55d02a73057..384d4741501 100644
--- a/model_zoo/official/cv/resnet/README.md
+++ b/model_zoo/official/cv/resnet/README.md
@@ -101,14 +101,14 @@ After installing MindSpore via the official website, you can start training and
 
 ```bash
 # distributed training
-Usage: bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
+Usage: bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
 
 # standalone training
-Usage: bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
+Usage: bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
 [PRETRAINED_CKPT_PATH](optional)
 
 # run evaluation example
-Usage: bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+Usage: bash run_eval.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 ```
 
 - Running on GPU
@@ -220,6 +220,29 @@ Parameters for both training and evaluation can be set in config.py.
 "lr_end": 0.0,                    # minimum learning rate
 ```
 
+- Config for ResNet34, ImageNet2012 dataset
+
+```bash
+"class_num": 1001,                # dataset class number
+"batch_size": 256,                 # batch size of input tensor
+"loss_scale": 1024,               # loss scale
+"momentum": 0.9,                  # momentum optimizer
+"weight_decay": 1e-4,             # weight decay
+"epoch_size": 90,                 # only valid for taining, which is always 1 for inference
+"pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
+"save_checkpoint": True,          # whether save checkpoint or not
+"save_checkpoint_epochs": 5,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
+"keep_checkpoint_max": 1,        # only keep the last keep_checkpoint_max checkpoint
+"save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
+"warmup_epochs": 0,               # number of warmup epoch
+"optimizer": 'Momentum',          # optimizer
+"use_label_smooth": True,         # label smooth
+"label_smooth_factor": 0.1,       # label smooth factor
+"lr_init": 0,                     # initial learning rate
+"lr_max": 1.0,                    # maximum learning rate
+"lr_end": 0.0,                    # minimum learning rate
+```
+
 - Config for ResNet101, ImageNet2012 dataset
 
 ```bash
@@ -273,14 +296,14 @@ Parameters for both training and evaluation can be set in config.py.
 
 ```bash
 # distributed training
-Usage: bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
+Usage: bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
 
 # standalone training
-Usage: bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
+Usage: bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
 [PRETRAINED_CKPT_PATH](optional)
 
 # run evaluation example
-Usage: bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+Usage: bash run_eval.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 
 ```
 
@@ -333,10 +356,10 @@ bash run_parameter_server_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet201
 
 ```bash
 # evaluation with distributed training Ascend example:
-bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
+bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 
 # evaluation with standalone training Ascend example:
-bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
+bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 
 # evaluation with distributed training GPU example:
 bash run_distribute_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012]  [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
@@ -378,6 +401,17 @@ epoch: 5 step: 625, loss is 3.597817
 ...
 ```
 
+- Training ResNet34 with ImageNet2012 dataset
+
+```text
+# 分布式训练结果（8P）
+epoch: 2 step: 625, loss is 4.181185
+epoch: 3 step: 625, loss is 3.8856044
+epoch: 4 step: 625, loss is 3.423355
+epoch: 5 step: 625, loss is 3.506971
+...
+```
+
 - Training ResNet50 with CIFAR-10 dataset
 
 ```bash
@@ -446,7 +480,7 @@ epoch: [0/1] step: [100/5004], loss is 6.814013Epoch time: 3437.154 ms, fps: 148
 
 ```bash
 # evaluation
-Usage: bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+Usage: bash run_eval.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 ```
 
 ```bash
@@ -490,6 +524,12 @@ result: {'acc': 0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/res
 result: {'acc': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt
 ```
 
+- Evaluating ResNet34 with ImageNet2012 dataset
+
+```bash
+result: {'top_1_accuracy': 0.736758814102564} ckpt=train_parallel0/resnet-90_625.ckpt
+```
+
 - Evaluating ResNet101 with ImageNet2012 dataset
 
 ```bash
@@ -524,7 +564,7 @@ Current batch_Size can only be set to 1. The precision calculation process needs
 bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET]  [DATA_PATH] [DEVICE_ID]
 ```
 
-- `NET_TYPE` can choose from [resnet18, se-resnet50, resnet50, resnet101].
+- `NET_TYPE` can choose from [resnet18, se-resnet50, resnet34, resnet50, resnet101].
 - `DATASET` can choose from [cifar10, imagenet].
 - `DEVICE_ID` is optional, default value is 0.
 
@@ -544,6 +584,12 @@ Total data: 10000, top1 accuracy: 0.94.26, top5 accuracy: 0.9987.
 Total data: 50000, top1 accuracy: 0.70668, top5 accuracy: 0.89698.
 ```
 
+- Evaluating ResNet34 with ImageNet2012 dataset
+
+```bash
+Total data: 50000, top1 accuracy: 0.7342.
+```
+
 - Evaluating ResNet50 with CIFAR-10 dataset
 
 ```bash
@@ -654,6 +700,26 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Checkpoint for Fine tuning | 197M (.ckpt file)                                         |197M (.ckpt file)     |
 | Scripts                    | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
 
+#### ResNet34 on ImageNet2012
+
+| Parameters                 | Ascend 910                                                   |
+| -------------------------- | -------------------------------------- |---------------------------------- |
+| Model Version              | ResNet50-v1.5                                                |
+| Resource                   | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8  |
+| uploaded Date              | 04/01/2020 (month/day/year)  ；                        |
+| MindSpore Version          | 0.1.0-alpha                                                       |
+| Dataset                    | ImageNet2012                                                    |
+| Training Parameters        | epoch=90, steps per epoch=626, batch_size = 256             |
+| Optimizer                  | Momentum                                                         |
+| Loss Function              | Softmax Cross Entropy                                       |
+| outputs                    | probability                                                 |
+| Loss                       | 1.9575993                                                    |
+| Speed                      | 111ms/step（8pcs）                     |
+| Total time                 | 112 mins                          |
+| Parameters (M)             | 20.79                                                         |
+| Checkpoint for Fine tuning | 166M (.ckpt file)                                         |
+| Scripts                    | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
+
 #### ResNet101 on ImageNet2012
 
 | Parameters                 | Ascend 910                                                   |   GPU |
@@ -724,6 +790,20 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | Accuracy            | 70.53%                      |
 | Model for inference | 45M (.air file)             |
 
+#### ResNet34 on ImageNet2012
+
+| Parameters          | Ascend                      |
+| ------------------- | --------------------------- |
+| Model Version       | ResNet18               |
+| Resource            | Ascend 910; OS Euler2.8                   |
+| Uploaded Date       | 02/25/2021 (month/day/year) |
+| MindSpore Version   | 1.1.1-alpha                |
+| Dataset             | ImageNet2012                |
+| batch_size          | 256                         |
+| outputs             | probability                 |
+| Accuracy            | 73.67%                      |
+| Model for inference | 70M (.air file)             |
+
 #### ResNet50 on CIFAR-10
 
 | Parameters          | Ascend                      | GPU                         |
diff --git a/model_zoo/official/cv/resnet/README_CN.md b/model_zoo/official/cv/resnet/README_CN.md
index 517d16d9556..3888e32b314 100755
--- a/model_zoo/official/cv/resnet/README_CN.md
+++ b/model_zoo/official/cv/resnet/README_CN.md
@@ -104,14 +104,14 @@ ResNet的总体网络架构如下：
 
 ```text
 # 分布式训练
-用法：bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
+用法：bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
 
 # 单机训练
-用法：bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
+用法：bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
 [PRETRAINED_CKPT_PATH]（可选）
 
 # 运行评估示例
-用法：bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+用法：bash run_eval.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 ```
 
 - GPU处理器环境运行
@@ -204,6 +204,29 @@ bash run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH]
 "lr_end":0.0,                    # 最小学习率
 ```
 
+- 配置ResNet34和ImageNet2012数据集。
+
+```text
+"class_num":1001,                # 数据集类数
+"batch_size":256,                # 输入张量的批次大小
+"loss_scale":1024,               # 损失等级
+"momentum":0.9,                  # 动量优化器
+"weight_decay":1e-4,             # 权重衰减
+"epoch_size":90,                 # 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":1,         # 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
+"optimizer":"Momentum",          # 优化器
+"use_label_smooth":True,         # 标签平滑
+"label_smooth_factor":0.1,       # 标签平滑因子
+"lr_init":0,                     # 初始学习率
+"lr_max":1.0,                    # 最大学习率
+"lr_end":0.0,                    # 最小学习率
+```
+
 - 配置ResNet101和ImageNet2012数据集。
 
 ```text
@@ -257,14 +280,14 @@ bash run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH]
 
 ```text
 # 分布式训练
-用法：bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
+用法：bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]（可选）
 
 # 单机训练
-用法：bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
+用法：bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH]
 [PRETRAINED_CKPT_PATH]（可选）
 
 # 运行评估示例
-用法：bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+用法：bash run_eval.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 
 ```
 
@@ -307,10 +330,10 @@ bash run_parameter_server_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet201
 
 ```bash
 # Ascend 分布式训练时推理示例:
-bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
+bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 
 # Ascend 单机训练时推理示例:
-bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
+bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 
 # GPU 分布式训练时推理示例:
 bash run_distribute_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012]  [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
@@ -351,6 +374,17 @@ epoch: 5 step: 625, loss is 3.597817
 ...
 ```
 
+- 使用ImageNet2012数据集训练ResNet34
+
+```text
+# 分布式训练结果（8P）
+epoch: 2 step: 625, loss is 4.181185
+epoch: 3 step: 625, loss is 3.8856044
+epoch: 4 step: 625, loss is 3.423355
+epoch: 5 step: 625, loss is 3.506971
+...
+```
+
 - 使用CIFAR-10数据集训练ResNet50
 
 ```text
@@ -412,7 +446,7 @@ epoch:5 step:5004, loss is 3.3501816
 
 ```bash
 # 评估
-Usage: bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
+Usage: bash run_eval.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
 ```
 
 ```bash
@@ -456,6 +490,12 @@ result:{'acc':0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/resne
 result:{'acc':0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt
 ```
 
+- 使用ImageNet2012数据集评估ResNet34
+
+```text
+result: {'top_1_accuracy': 0.736758814102564} ckpt=train_parallel0/resnet-90_625 .ckpt
+```
+
 - 使用ImageNet2012数据集评估ResNet101
 
 ```text
@@ -490,7 +530,7 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [
 bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET] [DATA_PATH] [DEVICE_ID]
 ```
 
-- `NET_TYPE` 选择范围：[resnet18, se-resnet50, resnet50, resnet101]。
+- `NET_TYPE` 选择范围：[resnet18, resnet34, se-resnet50, resnet50, resnet101]。
 - `DATASET` 选择范围：[cifar10, imagenet]。
 - `DEVICE_ID` 可选，默认值为0。
 
@@ -519,7 +559,13 @@ Total data: 10000, top1 accuracy: 0.9310, top5 accuracy: 0.9980.
 - 使用ImageNet2012数据集评估ResNet50
 
 ```text
-Total data: 50000, top1 accuracy: 0.0.7696, top5 accuracy: 0.93432.
+Total data: 50000, top1 accuracy: 0.7696, top5 accuracy: 0.93432.
+```
+
+- 使用ImageNet2012数据集评估ResNet34
+
+```text
+Total data: 50000, top1 accuracy: 0.7367.
 ```
 
 - 使用ImageNet2012数据集评估ResNet101
@@ -620,6 +666,26 @@ Total data: 50000, top1 accuracy: 0.76844, top5 accuracy: 0.93522.
 | 微调检查点| 197M（.ckpt文件）                                         | 197M（.ckpt文件）     |
 | 脚本                    | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
 
+#### ImageNet2012上的ResNet34
+
+| 参数                 | Ascend 910                                                   |
+| -------------------------- | -------------------------------------- |
+| 模型版本              | ResNet34                                               |
+| 资源                   |  Ascend 910；CPU 2.60GHz，192核；内存 755G；系统 Euler2.8 |
+| 上传日期              | 2021-05-08  ;                        |
+| MindSpore版本          | 1.1.1-alpha                                                       |
+| 数据集                    | ImageNet2012                                                    |
+| 训练参数        | epoch=90, steps per epoch=625, batch_size = 256             |
+| 优化器                  | Momentum                                                         |
+| 损失函数              | Softmax交叉熵                                       |
+| 输出                    | 概率                                                 |
+| 损失                       | 1.9575993                                                       |
+| 速度                      | 111毫秒/步（8卡）                     |
+| 总时长                 | 112分钟                          |
+| 参数(M)             | 20.79                                                         |
+| 微调检查点| 166M（.ckpt文件）                                         |
+| 脚本                    | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
+
 #### ImageNet2012上的ResNet101
 
 | 参数                 | Ascend 910                                                   |   GPU |
diff --git a/model_zoo/official/cv/resnet/eval.py b/model_zoo/official/cv/resnet/eval.py
index e67d87bd8f3..1536a36f898 100755
--- a/model_zoo/official/cv/resnet/eval.py
+++ b/model_zoo/official/cv/resnet/eval.py
@@ -23,7 +23,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.CrossEntropySmooth import CrossEntropySmooth
 
 parser = argparse.ArgumentParser(description='Image classification')
-parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet18, '
+parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet18,resnet34'
                                                           'resnet50 or resnet101')
 parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
 
@@ -46,7 +46,10 @@ if args_opt.net in ("resnet18", "resnet50"):
     else:
         from src.config import config2 as config
         from src.dataset import create_dataset2 as create_dataset
-
+elif args_opt.net == "resnet34":
+    from src.resnet import resnet34 as resnet
+    from src.config import config_resnet34 as config
+    from src.dataset import create_dataset_resnet34 as create_dataset
 elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config
diff --git a/model_zoo/official/cv/resnet/export.py b/model_zoo/official/cv/resnet/export.py
index 34348b4896b..80272824567 100644
--- a/model_zoo/official/cv/resnet/export.py
+++ b/model_zoo/official/cv/resnet/export.py
@@ -24,6 +24,7 @@ from mindspore import Tensor, load_checkpoint, load_param_into_net, export, cont
 parser = argparse.ArgumentParser(description='resnet export')
 parser.add_argument('--network_dataset', type=str, default='resnet50_cifar10', choices=['resnet18_cifar10',
                                                                                         'resnet18_imagenet2012',
+                                                                                        'resnet34_imagenet2012',
                                                                                         'resnet50_cifar10',
                                                                                         'resnet50_imagenet2012',
                                                                                         'resnet101_imagenet2012',
@@ -52,6 +53,9 @@ if __name__ == '__main__':
     elif args.network_dataset == 'resnet18_imagenet2012':
         from src.config import config2 as config
         from src.resnet import resnet18 as resnet
+    elif args.network_dataset == 'resnet34_imagenet2012':
+        from src.config import config2 as config
+        from src.resnet import resnet34 as resnet
     elif args.network_dataset == 'resnet50_cifar10':
         from src.config import config1 as config
         from src.resnet import resnet50 as resnet
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 662a4274c94..14e3b93bd04 100755
--- a/model_zoo/official/cv/resnet/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/resnet/scripts/run_distribute_train.sh
@@ -19,12 +19,12 @@ CURPATH="$(dirname "$0")"
 
 if [ $# != 4 ] && [ $# != 5 ] && [ $# != 6 ]
 then
-  echo "Usage: bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
-  echo "       bash run_distribute_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)"
+  echo "Usage: bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+  echo "       bash run_distribute_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)"
   exit 1
 fi
 
-if [ $1 != "resnet18" ] && [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
+if [ $1 != "resnet18" ] && [ $1 != "resnet34" ] && [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
 then 
   echo "error: the selected net is neither resnet50 nor resnet101 and se-resnet50"
 exit 1
diff --git a/model_zoo/official/cv/resnet/scripts/run_eval.sh b/model_zoo/official/cv/resnet/scripts/run_eval.sh
index 8398cc856ca..8759581e64e 100755
--- a/model_zoo/official/cv/resnet/scripts/run_eval.sh
+++ b/model_zoo/official/cv/resnet/scripts/run_eval.sh
@@ -16,11 +16,11 @@
 
 if [ $# != 4 ]
 then 
-    echo "Usage: bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
+    echo "Usage: bash run_eval.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
 exit 1
 fi
 
-if [ $1 != "resnet18" ] && [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
+if [ $1 != "resnet18" ] && [ $1 != "resnet34" ] && [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
 then 
     echo "error: the selected net is neither resnet50 nor resnet101 nor se-resnet50"
 exit 1
diff --git a/model_zoo/official/cv/resnet/scripts/run_infer_310.sh b/model_zoo/official/cv/resnet/scripts/run_infer_310.sh
index f94dad4766a..bf98f09dc05 100644
--- a/model_zoo/official/cv/resnet/scripts/run_infer_310.sh
+++ b/model_zoo/official/cv/resnet/scripts/run_infer_310.sh
@@ -16,7 +16,7 @@
 
 if [[ $# -lt 4 || $# -gt 5 ]]; then
     echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET] [DATA_PATH] [DEVICE_ID]
-    NET_TYPE can choose from [resnet18, se-resnet50, resnet50, resnet101]
+    NET_TYPE can choose from [resnet18, resnet34, se-resnet50, resnet50, resnet101]
     DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
 exit 1
 fi
@@ -29,7 +29,7 @@ get_real_path(){
     fi
 }
 model=$(get_real_path $1)
-if [ $2 == 'resnet18' ] || [ $2 == 'se-resnet50' ] || [ $2 == 'resnet50' ] || [ $2 == 'resnet101' ]; then
+if [ $2 == 'resnet18' ] || [ $2 == 'resnet34' ] || [ $2 == 'se-resnet50' ] || [ $2 == 'resnet50' ] || [ $2 == 'resnet101' ]; then
   network=$2
 else
   echo "NET_TYPE can choose from [resnet18, se-resnet50]"
diff --git a/model_zoo/official/cv/resnet/scripts/run_standalone_train.sh b/model_zoo/official/cv/resnet/scripts/run_standalone_train.sh
index 44d3c7555c7..51fec969bbe 100755
--- a/model_zoo/official/cv/resnet/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/resnet/scripts/run_standalone_train.sh
@@ -19,12 +19,12 @@ CURPATH="$(dirname "$0")"
 
 if [ $# != 3 ] && [ $# != 4 ] && [ $# != 5 ]
 then 
-    echo "Usage: bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
-    echo "       bash run_standalone_train.sh [resnet18|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)"
+    echo "Usage: bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+    echo "       bash run_standalone_train.sh [resnet18|resnet34|resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)"
 exit 1
 fi
 
-if [ $1 != "resnet18" ] && [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
+if [ $1 != "resnet18" ] && [ $1 != "resnet34" ] && [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
 then 
     echo "error: the selected net is neither resnet50 nor resnet101 and se-resnet50"
 exit 1
diff --git a/model_zoo/official/cv/resnet/src/config.py b/model_zoo/official/cv/resnet/src/config.py
index 3868774ec36..b2eed0dc6f9 100755
--- a/model_zoo/official/cv/resnet/src/config.py
+++ b/model_zoo/official/cv/resnet/src/config.py
@@ -153,3 +153,24 @@ config_thor_gpu = ed({
     "damping_decay": 0.5467,
     "frequency": 834,
 })
+
+# config for resnet34, imagenet2012, Ascend
+config_resnet34 = ed({
+    "class_num": 1001,
+    "batch_size": 256,
+    "loss_scale": 1024,
+    "momentum": 0.9,
+    "weight_decay": 1e-4,
+    "epoch_size": 90,
+    "pretrain_epoch_size": 0,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 5,
+    "keep_checkpoint_max": 1,
+    "save_checkpoint_path": "./",
+    "warmup_epochs": 0,
+    "optimizer": 'Momentum',
+    "use_label_smooth": True,
+    "label_smooth_factor": 0.1,
+    "lr_init": 0.0,
+    "lr_max": 1.0,
+    "lr_end": 0.0})
diff --git a/model_zoo/official/cv/resnet/src/dataset.py b/model_zoo/official/cv/resnet/src/dataset.py
index 5c2b438b5d3..4aaeb1da594 100755
--- a/model_zoo/official/cv/resnet/src/dataset.py
+++ b/model_zoo/official/cv/resnet/src/dataset.py
@@ -400,6 +400,64 @@ def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target=
 
     return data_set
 
+def create_dataset_resnet34(dataset_path, do_train, repeat_num=1, batch_size=32):
+    """
+        create a train or eval imagenet2012 dataset for resnet34
+
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+
+    Returns:
+        data_set
+    """
+    device_id = int(os.getenv('DEVICE_ID'))
+    device_num = int(os.getenv('RANK_SIZE'))
+
+    if device_num == 1:
+        data_set = ds.ImageFolderDataset(dataset_path)
+    else:
+        if do_train:
+            data_set = ds.ImageFolderDataset(dataset_path, shuffle=True,
+                                             num_shards=device_num, shard_id=device_id)
+        else:
+            data_set = ds.ImageFolderDataset(dataset_path)
+
+    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.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
+    data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    data_set = data_set.repeat(repeat_num)
+
+    return data_set
 
 def _get_rank_info():
     """
diff --git a/model_zoo/official/cv/resnet/src/lr_generator.py b/model_zoo/official/cv/resnet/src/lr_generator.py
index 9d01284428b..b236513ac61 100755
--- a/model_zoo/official/cv/resnet/src/lr_generator.py
+++ b/model_zoo/official/cv/resnet/src/lr_generator.py
@@ -238,3 +238,20 @@ def get_thor_damping(global_step, damping_init, decay_rate, total_epochs, steps_
     damping_each_step = np.array(damping_each_step).astype(np.float32)
     damping_now = damping_each_step[current_step:]
     return damping_now
+
+def get_resnet34_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
+
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+
+    lr_each_step = []
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr = lr_init + (lr_max - lr_init) * i / warmup_steps
+        else:
+            lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
+        lr_each_step.append(lr)
+
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+
+    return lr_each_step
diff --git a/model_zoo/official/cv/resnet/src/resnet.py b/model_zoo/official/cv/resnet/src/resnet.py
index c160f48d981..0405e38cafa 100755
--- a/model_zoo/official/cv/resnet/src/resnet.py
+++ b/model_zoo/official/cv/resnet/src/resnet.py
@@ -508,6 +508,26 @@ def resnet18(class_num=10):
                   class_num,
                   res_base=True)
 
+def resnet34(class_num=10):
+    """
+    Get ResNet34 neural network.
+
+    Args:
+        class_num (int): Class number.
+
+    Returns:
+        Cell, cell instance of ResNet34 neural network.
+
+    Examples:
+        >>> net = resnet18(10)
+    """
+    return ResNet(ResidualBlockBase,
+                  [3, 4, 6, 3],
+                  [64, 64, 128, 256],
+                  [64, 128, 256, 512],
+                  [1, 2, 2, 2],
+                  class_num,
+                  res_base=True)
 
 def resnet50(class_num=10):
     """
diff --git a/model_zoo/official/cv/resnet/train.py b/model_zoo/official/cv/resnet/train.py
index 31a18996a84..dc3250a9532 100755
--- a/model_zoo/official/cv/resnet/train.py
+++ b/model_zoo/official/cv/resnet/train.py
@@ -32,14 +32,14 @@ from mindspore.parallel import set_algo_parameters
 import mindspore.nn as nn
 import mindspore.common.initializer as weight_init
 import mindspore.log as logger
-from src.lr_generator import get_lr, warmup_cosine_annealing_lr
+from src.lr_generator import get_lr, warmup_cosine_annealing_lr, get_resnet34_lr
 from src.CrossEntropySmooth import CrossEntropySmooth
 from src.config import cfg
 from src.eval_callback import EvalCallBack
 from src.metric import DistAccuracy, ClassifyCorrectCell
 
 parser = argparse.ArgumentParser(description='Image classification')
-parser.add_argument('--net', type=str, default=None, help='Resnet Model, resnet18, resnet50 or resnet101')
+parser.add_argument('--net', type=str, default=None, help='Resnet Model, resnet18, resnet34, resnet50 or resnet101')
 parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
 parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
 parser.add_argument('--device_num', type=int, default=1, help='Device num.')
@@ -83,7 +83,10 @@ if args_opt.net in ("resnet18", "resnet50"):
             from src.dataset import create_dataset2 as create_dataset
         else:
             from src.dataset import create_dataset_pynative as create_dataset
-
+elif args_opt.net == "resnet34":
+    from src.resnet import resnet34 as resnet
+    from src.config import config_resnet34 as config
+    from src.dataset import create_dataset_resnet34 as create_dataset
 elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config
@@ -189,13 +192,20 @@ if __name__ == '__main__':
         from src.lr_generator import get_thor_lr
         lr = get_thor_lr(0, config.lr_init, config.lr_decay, config.lr_end_epoch, step_size, decay_epochs=39)
     else:
-        if args_opt.net in ("resnet18", "resnet50", "se-resnet50"):
+        if args_opt.net in ("resnet18", "resnet34", "resnet50", "se-resnet50"):
             lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
                         warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
                         lr_decay_mode=config.lr_decay_mode)
         else:
             lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size,
                                             config.pretrain_epoch_size * step_size)
+    if args_opt.net == "resnet34":
+        lr = get_resnet34_lr(lr_init=config.lr_init,
+                             lr_end=config.lr_end,
+                             lr_max=config.lr_max,
+                             warmup_epochs=config.warmup_epochs,
+                             total_epochs=config.epoch_size,
+                             steps_per_epoch=step_size)
     lr = Tensor(lr)
 
     # define opt