Image

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A class for logging images to W&B.

Image(
    data_or_path: "ImageDataOrPathType",
    mode: Optional[str] = None,
    caption: Optional[str] = None,
    grouping: Optional[int] = None,
    classes: Optional[Union['Classes', Sequence[dict]]] = None,
    boxes: Optional[Union[Dict[str, 'BoundingBoxes2D'], Dict[str, dict]]] = None,
    masks: Optional[Union[Dict[str, 'ImageMask'], Dict[str, dict]]] = None,
    file_type: Optional[str] = None,
    normalize: bool = (True)
) -> None
Args
data_or_path Accepts numpy array/pytorch tensor of image data, a PIL image object, or a path to an image file. If a numpy array or pytorch tensor is provided, the image data will be saved to the given file type. If the values are not in the range [0, 255] or all values are in the range [0, 1], the image pixel values will be normalized to the range [0, 255] unless normalize is set to False. - pytorch tensor should be in the format (channel, height, width) - numpy array should be in the format (height, width, channel)
mode The PIL mode for an image. Most common are “L”, “RGB”, “RGBA”. Full explanation at https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes
caption Label for display of image.
grouping The grouping number for the image.
classes A list of class information for the image, used for labeling bounding boxes, and image masks.
boxes A dictionary containing bounding box information for the image. see: https://docs.wandb.ai/ref/python/data-types/boundingboxes2d/
masks A dictionary containing mask information for the image. see: https://docs.wandb.ai/ref/python/data-types/imagemask/
file_type The file type to save the image as. This parameter has no effect if data_or_path is a path to an image file.
normalize If True, normalize the image pixel values to fall within the range of [0, 255]. Normalize is only applied if data_or_path is a numpy array or pytorch tensor.
Attributes

Methods

all_boxes

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@classmethod
all_boxes(
    images: Sequence['Image'],
    run: "LocalRun",
    run_key: str,
    step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

all_captions

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@classmethod
all_captions(
    images: Sequence['Media']
) -> Union[bool, Sequence[Optional[str]]]

all_masks

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@classmethod
all_masks(
    images: Sequence['Image'],
    run: "LocalRun",
    run_key: str,
    step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

guess_mode

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guess_mode(
    data: Union['np.ndarray', 'torch.Tensor'],
    file_type: Optional[str] = None
) -> str

Guess what type of image the np.array is representing.

Class Variables
MAX_DIMENSION 65500
MAX_ITEMS 108

Last modified June 13, 2025