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add imagenet config description

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panfengfeng 2020-11-22 22:44:47 +08:00
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model_zoo/official/cv/googlenet/README.md
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@ -158,7 +158,7 @@ Parameters for both training and evaluation can be set in config.py
```python
'pre_trained': 'False' # whether training based on the pre-trained model
'nump_classes': 10 # the number of classes in the dataset
'num_classes': 10 # the number of classes in the dataset
'lr_init': 0.1 # initial learning rate
'batch_size': 128 # training batch size
'epoch_size': 125 # total training epochs
@ -175,6 +175,39 @@ Parameters for both training and evaluation can be set in config.py
'air_filename': 'googlenet.air' # file name of the air model used in export.py
```
- config for GoogleNet, ImageNet dataset
```python
'pre_trained': 'False' # whether training based on the pre-trained model
'num_classes': 1000 # the number of classes in the dataset
'lr_init': 0.1 # initial learning rate
'batch_size': 256 # training batch size
'epoch_size': 300 # total training epochs
'momentum': 0.9 # momentum
'weight_decay': 1e-4 # weight decay value
'image_height': 224 # image height used as input to the model
'image_width': 224 # image width used as input to the model
'data_path': './ImageNet_Original/train/' # absolute full path to the train datasets
'val_data_path': './ImageNet_Original/val/' # absolute full path to the evaluation datasets
'device_target': 'Ascend' # device running the program
'device_id': 0 # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training
'keep_checkpoint_max': 10 # only keep the last keep_checkpoint_max checkpoint
'checkpoint_path': './train_googlenet_cifar10-125_390.ckpt' # the absolute full path to save the checkpoint file
'onnx_filename': 'googlenet.onnx' # file name of the onnx model used in export.py
'air_filename': 'googlenet.air' # file name of the air model used in export.py
'lr_scheduler': 'exponential' # learning rate scheduler
'lr_epochs': [70, 140, 210, 280] # epoch of lr changing
'lr_gamma': 0.3 # decrease lr by a factor of exponential lr_scheduler
'eta_min': 0.0 # eta_min in cosine_annealing scheduler
'T_max': 150 # T-max in cosine_annealing scheduler
'warmup_epochs': 0 # warmup epoch
'is_dynamic_loss_scale': 0 # dynamic loss scale
'loss_scale': 1024 # loss scale
'label_smooth_factor': 0.1 # label_smooth_factor
'use_label_smooth': True # label smooth
```
For more configuration details, please refer the script `config.py`.
## [Training Process](#contents)