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mmcls.apis.train_model

mmcls.apis.train_model(model, dataset, cfg, distributed=False, validate=False, timestamp=None, device=None, meta=None)[source]

Train a model.

This method will build dataloaders, wrap the model and build a runner according to the provided config.

Parameters

  • model (torch.nn.Module) – The model to be run.

  • dataset (mmcls.datasets.BaseDataset | List[BaseDataset]) – The dataset used to train the model. It can be a single dataset, or a list of dataset with the same length as workflow.

  • cfg (mmcv.utils.Config) – The configs of the experiment.

  • distributed (bool) – Whether to train the model in a distributed environment. Defaults to False.

  • validate (bool) – Whether to do validation with mmcv.runner.EvalHook. Defaults to False.

  • timestamp (str, optional) – The timestamp string to auto generate the name of log files. Defaults to None.

  • device (str, optional) – TODO

  • meta (dict, optional) – A dict records some import information such as environment info and seed, which will be logged in logger hook. Defaults to None.