Detectron2’s config system uses yaml and yacs. In addition to the basic operations that access and update a config, we provide the following extra functionalities:
- The config can have
_BASE_: base.yamlfield, which will load a base config first. Values in the base config will be overwritten in sub-configs, if there are any conflicts. We provided several base configs for standard model architectures.
- We provide config versioning, for backward compatibility.
If your config file is versioned with a config line like
VERSION: 2, detectron2 will still recognize it even if we rename some keys in the future.
Some basic usage of the
CfgNode object is shown below:
from detectron2.config import get_cfg cfg = get_cfg() # obtain detectron2's default config cfg.xxx = yyy # add new configs for your own custom components cfg.merge_from_file("my_cfg.yaml") # load values from a file cfg.merge_from_list(["MODEL.WEIGHTS", "weights.pth"]) # can also load values from a list of str
To see a list of available configs in detectron2, see Config References
Best Practice with Configs¶
- Treat the configs you write as “code”: avoid copying them or duplicating them; use “BASE” instead to share common parts between configs.
- Keep the configs you write simple: don’t include keys that do not affect the experimental setting.
- Keep a version number in your configs (or the base config), e.g.,
VERSION: 2, for backward compatibility. The builtin configs do not include version number because they are meant to be always up-to-date.
- Save a full config together with a trained model, and use it to run inference. This is more robust to changes that may happen to the config definition (e.g., if a default value changed).