• Guides
  • CAREamist API
  • Configuration

Configuration

The configuration summarizes all the parameters used internally by CAREamics. It is used to create a CAREamist instance and is saved together with the checkpoints and saved models.

It is composed of four members:

Anatomy of the configuration
from careamics import Configuration

config_as_dict = {
    "experiment_name": "my_experiment",  # (1)!
    "algorithm_config": {  # (2)!
        "algorithm_type": "fcn",
        "algorithm": "n2v",
        "loss": "n2v",
        "model": {  # (3)!
            "architecture": "UNet",
        },
    },
    "data_config": {  # (4)!
        "data_type": "array",
        "patch_size": [128, 128],
        "axes": "YX",
    },
    "training_config": {
        "num_epochs": 1,
    },
}
config = Configuration(**config_as_dict)  # (5)!
  1. The name of the experiment, used to differentiate trained models.
  2. Configuration specific to the model.
  3. Configuration related to the data.
  4. Training parameters.
  5. The configuration is an object! 💣

If the number of parameters looks too limited, it is because the configuration is hiding a lot of default values! But don't be afraid, we have designed convenience functions to help you create a configuration for each of the algorithm CAREamics offers.

In the next sections, you can dive deeper on how to use CAREamics configuration with different levels of expertise.