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fix some typos in resnet readme file

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Merge pull request  from guoqi/master
pull/7153/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent f5b481d776 9a511d05d4
commit 7126e316bc
1 changed files with 12 additions and 12 deletions

24
model_zoo/official/cv/resnet/README.md
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@ -91,7 +91,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
After installing MindSpore via the official website, you can start training and evaluation as follows:
- Runing on Ascend
- Running on Ascend
```
# distributed training
Usage: sh run_distribute_train.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
@ -104,7 +104,7 @@ Usage: sh run_standalone_train.sh [resnet50|resnet101|se-resnet50] [cifar10|imag
Usage: sh run_eval.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
```
- Runing on GPU
- Running on GPU
```
# distributed training example
sh run_distribute_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
@ -124,7 +124,7 @@ sh run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [C
.
└──resnet
├── README.md
├── script
├── scripts
├── run_distribute_train.sh # launch ascend distributed training(8 pcs)
├── run_parameter_server_train.sh # launch ascend parameter server training(8 pcs)
├── run_eval.sh # launch ascend evaluation
@ -136,7 +136,7 @@ sh run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [C
├── src
├── config.py # parameter configuration
├── dataset.py # data preprocessing
├── crossentropy.py # loss definition for ImageNet2012 dataset
├── CrossEntropySmooth.py # loss definition for ImageNet2012 dataset
├── lr_generator.py # generate learning rate for each step
└── resnet.py # resnet backbone, including resnet50 and resnet101 and se-resnet50
├── eval.py # eval net
@ -172,7 +172,7 @@ Parameters for both training and evaluation can be set in config.py.
```
"class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor
"batch_size": 256, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay
@ -184,10 +184,10 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "Linear", # decay mode for generating learning rate
"label_smooth": True, # label smooth
"use_label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate
"lr_max": 0.1, # maximum learning rate
"lr_max": 0.8, # maximum learning rate
"lr_end": 0.0, # minimum learning rate
```
@ -207,7 +207,7 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"label_smooth": 1, # label_smooth
"use_label_smooth": True, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor
"lr": 0.1 # base learning rate
```
@ -229,7 +229,7 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 3, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"label_smooth": True, # label_smooth
"use_label_smooth": True, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor
"lr_init": 0.0, # initial learning rate
"lr_max": 0.3, # maximum learning rate
@ -421,13 +421,13 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
| uploaded Date | 04/01/2020 (month/day/year) ； | 08/01/2020 (month/day/year)
| MindSpore Version | 0.1.0-alpha |0.6.0-alpha |
| Dataset | ImageNet2012 | ImageNet2012|
| Training Parameters | epoch=90, steps per epoch=5004, batch_size = 32 |epoch=90, steps per epoch=5004, batch_size = 32 |
| Training Parameters | epoch=90, steps per epoch=626, batch_size = 256 |epoch=90, steps per epoch=5004, batch_size = 32 |
| Optimizer | Momentum |Momentum|
| Loss Function | Softmax Cross Entropy |Softmax Cross Entropy |
| outputs | probability | probability |
| Loss | 1.8464266 | 1.9023 |
| Speed | 18.4ms/step（8pcs） |67.1ms/step（8pcs）|
| Total time | 139 mins | 500 mins|
| Speed | 118ms/step（8pcs） |67.1ms/step（8pcs）|
| Total time | 114 mins | 500 mins|
| Parameters (M) | 25.5 | 25.5 |
| 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) |

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