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The config files of different models are saved in PaddleSeg/configs. PaddleSeg use the config files to train, validate and export models.

Configuration items


train_dataset

Training datasset

  • parameter
    • type: Dataset type, please refer to the training configuration file for more details of supported values
    • others: Please refer to the corresponding model training configuration file

val_dataset

Evaluation dataset

  • parameter
    • type: Dataset type, please refer to the training configuration file for more details of supported values
    • others: Please refer to the corresponding model training configuration file

batch_size

On a single card, the amount of data during each iteration of training


iters

Training steps


optimizer

Training optimizer

  • parameter
    • type : supports all official optimizers of PaddlePaddle
    • weight_decay : L2 regularization value
    • others : Please refer to Optimizer

lr_scheduler

Learning rate

  • parameter
    • type : learning rate type, supports 10 strategies, namely 'PolynomialDecay', 'PiecewiseDecay', 'StepDecay', 'CosineAnnealingDecay', 'ExponentialDecay', 'InverseTimeDecay', 'LinearWarmup', 'MultiStepDecay', 'NaturalExpDecay', 'NoamDecay'.
    • others : Please refer to Paddle official LRScheduler document

learning_rate(this configuration is not recommended, it will be discarded in the future, we recommend to use lr_scheduler instead)

Learning rate

  • parameter
    • value: initial learning rate value
    • decay: decay configuration
      • type: attenuation type, currently only supports poly
      • power: attenuation rate
      • end_lr: final learning rate

loss

Loss function

  • parameter
    • types: list of loss functions
      • type: Loss function type, please refer to the loss function library for more details
      • ignore_index : The category that needs to be ignored during the training process. The default value is the same train_datasetas ignore_index. It is recommended not to set this item . If you set this, "ignore_index" in loss and train_datasetthe must be the same.
    • coef : a list of coefficients corresponding to corresponding loss functions

model

Model to be trained

  • parameter
    • type : model type, please refer to the model library for the more details
    • others: Please refer to the corresponding model training configuration file

export

Model export configuration

  • parameter
    • transforms: Preprocessing operations during prediction. The transforms are the same as train_dataset, val_datasetetc. If you do not fill in this item, the data will be normalized by default.

For more details, please refer to detailed configuration file