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hugging_face_zero_shot_image_classification

Hugging Face Zero-Shot Image Classification Algorithm.

Classes​

HuggingFaceZeroShotImageClassificationInference​

class HuggingFaceZeroShotImageClassificationInference(    model_id: str,    image_column_name: str,    candidate_labels: List[str],    batch_size: int = 1,    class_outputs: Optional[List[str]] = None,    hypothesis_template: Optional[str] = None,    seed: int = 42,):

Inference for pre-trained Hugging Face zero shot image classification models.

Arguments

  • batch_size: The batch size for inference. Defaults to 1.
  • candidate_labels: The candidate labels for this image.
  • class_outputs: A list of strings corresponding to prediction outputs. If provided, the model will return a dataframe of results with the class outputs list elements as columns. Defaults to None.
  • hypothesis_template: The sentence used in conjunction with candidate_labels to attempt the image classification by replacing the placeholder with the candidate_labels. Then likelihood is estimated by using logits_per_image. Defaults to None.
  • image_column_name: The image column on which the inference should be done.
  • model_id: The model id to use for zero shot image classification inference. The model id is of a pretrained model hosted inside a model repo on huggingface.co.
  • seed: Sets the seed of the algorithm. For reproducible behavior it defaults to 42.

Attributes

  • batch_size: The batch size for inference. Defaults to 1.
  • candidate_labels: The candidate labels for this image.
  • class_name: The name of the algorithm class.
  • class_outputs: A list of strings corresponding to prediction outputs. If provided, the model will return a dataframe of results with the class outputs list elements as columns. Defaults to None.
  • fields_dict: A dictionary mapping all attributes that will be serialized in the class to their marshamllow field type. (e.g. fields_dict = {"class_name": fields.Str()}).
  • hypothesis_template: The sentence used in conjunction with candidate_labels to attempt the image classification by replacing the placeholder with the candidate_labels. Then likelihood is estimated by using logits_per_image. Defaults to None.
  • image_column_name: The image column on which the inference should be done.
  • model_id: The model id to use for zero shot image classification inference. The model id is of a pretrained model hosted inside a model repo on huggingface.co.
  • nested_fields: A dictionary mapping all nested attributes to a registry that contains class names mapped to the respective classes. (e.g. nested_fields = {"datastructure": datastructure.registry})
  • seed: Sets the seed of the algorithm. For reproducible behavior it defaults to 42.

Ancestors​

Variables​

Methods​

create​

def create(self, role: Union[str, Role], **kwargs: Any) ‑> Any:

Create an instance representing the role specified.

modeller​

def modeller(    self,    **kwargs: Any,) ‑> bitfount.federated.algorithms.hugging_face_algorithms.hugging_face_zero_shot_image_classification._ModellerSide:

Returns the modeller side of the HuggingFaceZeroShotImageClassificationInference algorithm.

worker​

def worker(    self,    **kwargs: Any,) ‑> bitfount.federated.algorithms.hugging_face_algorithms.hugging_face_zero_shot_image_classification._WorkerSide:

Returns the worker side of the HuggingFaceZeroShotImageClassificationInference algorithm.