0
and 255
. If you want to normalize your images differently, you can specify the mode
manually or just supply a PIL.Image
, as described in the "Logging PIL Images" tab of this panel.PIL.Image
yourself and provide it directly.masks
keyword argument of wandb.Image
:"mask_data"
: a 2D numpy array containing an integer class label for each pixel"path"
: (string) a path to a saved image mask file"class_labels"
: (optional) a dictionary mapping the integer class labels in the image mask to their readable class nameswandb.Image
:box_data
: a list of dictionaries, one for each box. The box dictionary format is described below.position
: a dictionary representing the position and size of the box in one of two formats, as described below. Boxes need not all use the same format.{"minX", "maxX", "minY", "maxY"}
. Provide a set of coordinates defining the upper and lower bounds of each box dimension.{"middle", "width", "height"}
. Provide a set of coordinates specifying the middle
coordinates as [x,y]
, and width
and height
as scalars.class_id
: an integer representing the class identity of the box. See class_labels
key below.scores
: a dictionary of string labels and numeric values for scores. Can be used for filtering boxes in the UI.domain
: specify the units/format of the box coordinates. Set this to "pixel" if the box coordinates are expressed in pixel space (i.e. as integers within the bounds of the image dimensions). By default, the domain is assumed to be a fraction/percentage of the image (a floating point number between 0 and 1).box_caption
: (optional) a string to be displayed as the label text on this boxclass_labels
: (optional) A dictionary mapping class_id
s to strings. By default we will generate class labels class_0
, class_1
, etc.wandb.Image
object for each row in the table.
An example is provided in the Code snippet below:wandb.Image
object for each row in the table.
An example is provided in the code snippet below:np.histogram
. Note that all arrays/tensors are flattened. You can use the optional num_bins
keyword argument to override the default of 64
bins. The maximum number of bins supported is 512
.np.histogram
and pass the returned tuple to the np_histogram
keyword argument.'obj', 'gltf', 'glb', 'babylon', 'stl', 'pts.json'
, and we will render them in the UI when your run finishes.[[x, y, z], ...]
nx3
[[x, y, z, c], ...]
nx4
| c is a category
in the range [1, 14]
(Useful for segmentation)[[x, y, z, r, g, b], ...]
nx6 | r,g,b
are values in the range [0,255]
for red, green, and blue color channels.points
is a numpy array with the same format as the simple point cloud renderer shown above.boxes
is a numpy array of python dictionaries with three attributes:corners
- a list of eight cornerslabel
- a string representing the label to be rendered on the box (Optional)color
- rgb values representing the color of the boxtype
is a string representing the scene type to render. Currently the only supported value is lidar/beta
pdb
, pqr
, mmcif
, mcif
, cif
, sdf
, sd
, gro
, mol2
, or mmtf.
rdkit
mol
files, and rdkit.Chem.rdchem.Mol
objects.ffmpeg
and the moviepy
python library are required when passing numpy objects). Supported formats are "gif"
, "mp4"
, "webm"
, and "ogg"
. If you pass a string to wandb.Video
we assert the file exists and is a supported format before uploading to wandb. Passing a BytesIO
object will create a tempfile with the specified format as the extension.wandb.Table
to log text in tables to show up in the UI. By default, the column headers are ["Input", "Output", "Expected"]
. To ensure optimal UI performance, the default maximum number of rows is set to 10,000. However, users can explicitly override the maximum with wandb.Table.MAX_ROWS = {DESIRED_MAX}
.DataFrame
object.inject=False
.