ObjectDetectionPredictOptions#

Note

All Configs are derived from rastervision.pipeline.config.Config, which itself is a pydantic Model.

pydantic model ObjectDetectionPredictOptions[source]#

Show JSON schema

{
   "title": "ObjectDetectionPredictOptions",
   "description": "Base class that can be extended to provide custom configurations.\n\nThis adds some extra methods to Pydantic BaseModel.\nSee https://pydantic-docs.helpmanual.io/\n\nThe general idea is that configuration schemas can be defined by\nsubclassing this and adding class attributes with types and\ndefault values for each field. Configs can be defined hierarchically,\nie. a Config can have fields which are of type Config.\nValidation, serialization, deserialization, and IDE support is\nprovided automatically based on this schema.",
   "type": "object",
   "properties": {
      "type_hint": {
         "title": "Type Hint",
         "default": "object_detection_predict_options",
         "enum": [
            "object_detection_predict_options"
         ],
         "type": "string"
      },
      "merge_thresh": {
         "title": "Merge Thresh",
         "description": "If predicted boxes have an IOA (intersection over area) greater than merge_thresh, then they are merged into a single box during postprocessing. This is needed since the sliding window approach results in some false duplicates.",
         "default": 0.5,
         "type": "number"
      },
      "score_thresh": {
         "title": "Score Thresh",
         "description": "Predicted boxes are only output if their score is above score_thresh.",
         "default": 0.5,
         "type": "number"
      }
   },
   "additionalProperties": false
}

Config

extra: str = forbid
validate_assignment: bool = True

Fields

merge_thresh (float)
score_thresh (float)
type_hint (Literal['object_detection_predict_options'])

field merge_thresh: float = 0.5#: If predicted boxes have an IOA (intersection over area) greater than merge_thresh, then they are merged into a single box during postprocessing. This is needed since the sliding window approach results in some false duplicates.

field score_thresh: float = 0.5#: Predicted boxes are only output if their score is above score_thresh.

field type_hint: Literal['object_detection_predict_options'] = 'object_detection_predict_options'#

build()#

Build an instance of the corresponding type of object using this config.

For example, BackendConfig will build a Backend object. The arguments to this method will vary depending on the type of Config.

recursive_validate_config()#

Recursively validate hierarchies of Configs.

This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.

revalidate()#

Re-validate an instantiated Config.

Runs all Pydantic validators plus self.validate_config().

Adapted from: https://github.com/samuelcolvin/pydantic/issues/1864#issuecomment-679044432

update(*args, **kwargs)#

Update any fields before validation.

Subclasses should override this to provide complex default behavior, for example, setting default values as a function of the values of other fields. The arguments to this method will vary depending on the type of Config.

validate_config()#

Validate fields that should be checked after update is called.

This is to complement the builtin validation that Pydantic performs at the time of object construction.

validate_list(field: str, valid_options: List[str])#

Validate a list field.

Parameters

field (str) – name of field to validate
valid_options (List[str]) – values that field is allowed to take

Raises

ConfigError – if field is invalid