ObjectDetectionChipOptions

Note

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

pydantic model ObjectDetectionChipOptions

Show JSON schema

{
   "title": "ObjectDetectionChipOptions",
   "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": {
      "neg_ratio": {
         "title": "Neg Ratio",
         "description": "The ratio of negative chips (those containing no bounding boxes) to positive chips. This can be useful if the statistics of the background is different in positive chips. For example, in car detection, the positive chips will always contain roads, but no examples of rooftops since cars tend to not be near rooftops.",
         "default": 1.0,
         "type": "number"
      },
      "ioa_thresh": {
         "title": "Ioa Thresh",
         "description": "When a box is partially outside of a training chip, it is not clear if (a clipped version) of the box should be included in the chip. If the IOA (intersection over area) of the box with the chip is greater than ioa_thresh, it is included in the chip.",
         "default": 0.8,
         "type": "number"
      },
      "window_method": {
         "default": "chip",
         "allOf": [
            {
               "$ref": "#/definitions/ObjectDetectionWindowMethod"
            }
         ]
      },
      "label_buffer": {
         "title": "Label Buffer",
         "type": "integer"
      },
      "type_hint": {
         "title": "Type Hint",
         "default": "object_detection_chip_options",
         "enum": [
            "object_detection_chip_options"
         ],
         "type": "string"
      }
   },
   "additionalProperties": false,
   "definitions": {
      "ObjectDetectionWindowMethod": {
         "title": "ObjectDetectionWindowMethod",
         "description": "Enum for window methods\n\n    Attributes:\n        chip: the default method\n    ",
         "enum": [
            "chip",
            "label",
            "image",
            "sliding"
         ]
      }
   }
}

Config

• extra: str = forbid

• validate_assignment: bool = True

Fields

• ioa_thresh (float)

• label_buffer (Optional[int])

• neg_ratio (float)

• type_hint (Literal['object_detection_chip_options'])

• window_method (rastervision.core.rv_pipeline.object_detection_config.ObjectDetectionWindowMethod)

field ioa_thresh: float = 0.8

When a box is partially outside of a training chip, it is not clear if (a clipped version) of the box should be included in the chip. If the IOA (intersection over area) of the box with the chip is greater than ioa_thresh, it is included in the chip.

field label_buffer: Optional[int] = None

field neg_ratio: float = 1.0

The ratio of negative chips (those containing no bounding boxes) to positive chips. This can be useful if the statistics of the background is different in positive chips. For example, in car detection, the positive chips will always contain roads, but no examples of rooftops since cars tend to not be near rooftops.

field type_hint: Literal['object_detection_chip_options'] = 'object_detection_chip_options'

field window_method: ObjectDetectionWindowMethod = ObjectDetectionWindowMethod.chip

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