Image

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class Image

Format images for logging to W&B.

See https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes for more information on modes.

Args:

  • data_or_path: Accepts numpy array of image data, or a PIL image. The class attempts to infer the data format and converts it.
  • mode: The PIL mode for an image. Most common are “L”, “RGB”, “RGBA”.
  • caption: Label for display of image.

When logging a torch.Tensor as a wandb.Image, images are normalized. If you do not want to normalize your images, convert your tensors to a PIL Image.

Examples:

# Create a wandb.Image from a numpy array
import numpy as np
import wandb

with wandb.init() as run:
   examples = []
   for i in range(3):
        pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
        image = wandb.Image(pixels, caption=f"random field {i}")
        examples.append(image)
   run.log({"examples": examples})

# Create a wandb.Image from a PILImage
import numpy as np
from PIL import Image as PILImage
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(
             low=0, high=256, size=(100, 100, 3), dtype=np.uint8
         )
         pil_image = PILImage.fromarray(pixels, mode="RGB")
         image = wandb.Image(pil_image, caption=f"random field {i}")
         examples.append(image)
    run.log({"examples": examples})

# log .jpg rather than .png (default)
import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
         image = wandb.Image(pixels, caption=f"random field {i}", file_type="jpg")
         examples.append(image)
    run.log({"examples": examples})

method Image.__init__

__init__(
    data_or_path: 'ImageDataOrPathType',
    mode: Optional[str] = None,
    caption: Optional[str] = None,
    grouping: Optional[int] = None,
    classes: Optional[ForwardRef('Classes'), Sequence[dict]] = None,
    boxes: Optional[Dict[str, ForwardRef('BoundingBoxes2D')], Dict[str, dict]] = None,
    masks: Optional[Dict[str, ForwardRef('ImageMask')], Dict[str, dict]] = None,
    file_type: Optional[str] = None
) → None

method Image.guess_mode

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

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


Last modified April 7, 2025