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+# ResNet-50-THOR Example
+
+## Description
+
+This is an example of training ResNet-50 V1.5 with ImageNet2012 dataset by second-order optimizer THOR. THOR is a novel approximate seond-order optimization method in MindSpore. With fewer iterations, THOR can finish ResNet-50 V1.5 training in 72 minutes to top-1 accuracy of 75.9% using 8 Ascend 910, which is much faster than SGD with Momentum. 
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the dataset ImageNet2012 
+
+> Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:
+> ```
+> .  
+> ├── ilsvrc                  # train dataset
+> └── ilsvrc_eval             # infer dataset
+> ```
+
+
+## Example structure
+
+```shell
+.
+├── crossentropy.py                 # CrossEntropy loss function
+├── config.py                       # parameter configuration
+├── dataset_imagenet.py             # data preprocessing
+├── eval.py                         # infer script
+├── model                           # include model file of the optimizer
+├── run_distribute_train.sh         # launch distributed training(8 pcs)
+├── run_infer.sh                    # launch infering
+└── train.py                        # train script
+```
+
+
+## Parameter configuration
+
+Parameters for both training and inference can be set in config.py.
+
+```
+"class_num": 1000,                # dataset class number
+"batch_size": 32,                 # batch size of input tensor
+"loss_scale": 128,                # loss scale
+"momentum": 0.9,                  # momentum of THOR optimizer
+"weight_decay": 5e-4,             # weight decay 
+"epoch_size": 45,                 # only valid for taining, which is always 1 for inference 
+"buffer_size": 1000,              # number of queue size in data preprocessing
+"image_height": 224,              # image height
+"image_width": 224,               # image width
+"save_checkpoint": True,          # whether save checkpoint or not
+"save_checkpoint_steps": 5004,    # the step interval between two checkpoints. By default, the checkpoint will be saved every epoch
+"keep_checkpoint_max": 20,        # only keep the last keep_checkpoint_max checkpoint
+"save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
+"label_smooth": True,             # label smooth
+"label_smooth_factor": 0.1,       # label smooth factor
+"frequency": 834,                 # the step interval to update second-order information matrix
+```
+
+## Running the example
+
+### Train
+
+#### Usage
+
+```
+# distributed training
+Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [DEVICE_NUM]
+```
+
+
+#### Launch
+
+```bash
+# distributed training example(8 pcs)
+sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
+```
+
+> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
+
+#### Result
+
+Training result will be stored in the example path, whose folder name begins with "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
+
+```
+# distribute training result(8 pcs)
+epoch: 1 step: 5004, loss is 4.4182425
+epoch: 2 step: 5004, loss is 3.740064
+epoch: 3 step: 5004, loss is 4.0546017
+epoch: 4 step: 5004, loss is 3.7598825
+epoch: 5 step: 5004, loss is 3.3744206
+......
+```
+
+### Infer
+
+#### Usage
+
+```
+# infer
+Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
+```
+
+#### Launch
+
+```bash
+# infer with checkpoint
+sh run_infer.sh dataset/ilsvrc_eval train_parallel0/resnet-42_5004.ckpt
+```
+
+> checkpoint can be produced in training process.
+
+#### Result
+
+Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
+
+```
+result: {'acc': 0.759503041} ckpt=train_parallel0/resnet-42_5004.ckpt
+```