gives a dataset of identified entities in image.


gives a dataset that only contains entities in the specified category.


includes the properties prop for each identified object.

Details and Options

  • ImageContents is also known as object detection and attempts to find instances of an object in an image and returns a dataset of identified properties such as name, bounding box and probability.
  • Possible forms for category include:
  • "concept"named concept, as used in "Concept" entities
    "word"English word, as used in WordData
    wordspecword sense specification, as used in WordData
    Entity[]any appropriate entity
    category1|category2|any of the categoryi
  • The property prop can be any of the following:
  • "BoundingBox"the bounding box given as a Rectangle
    "Dimensions"width and height of the subimage
    "Image"the identified subimage (default)
    "Position"center of the identified bounding box
    "Probability"probability of the identification
    {prop1,prop2,}a list of properties
  • Masked subimage properties:
  • "Mask"binary mask indicating the object
    "MaskBoundingBox"the bounding box of the masked image
    "MaskCentroid"centroid of the mask
    "MaskedImage"subimage masked by the component shape
    "MaskMedoid"medoid of the mask
    "MaskPosition"center of the mask bounding box
  • The following options can be given:
  • AcceptanceThreshold Automaticidentification acceptance threshold
    MaxFeatures Automaticmaximum number of subimages to return
    MaxOverlapFraction Automaticmaximum bounding box overlap
    PerformanceGoal"Balanced"aspects of performance to try to optimize
    TargetDevice "CPU"the target device on which to compute
  • When no content is found at an acceptable threshold, Missing["NotRecognized"] is returned.
  • ImageContents uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.
  • ImageContents may download resources that will be stored in your local object store at $LocalBase, and can be listed using LocalObjects[] and removed using ResourceRemove.


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Basic Examples  (1)

Identify contents of an image:

Get results only in the specified category:

Scope  (5)

Get a summary of all the identified entities in an image:

Get a summary only in the specified category:

Specify which properties to include in the summary:

By default, detection comes back as bounding boxes:

Perform content segmentation:

Options  (4)

AcceptanceThreshold  (1)

Objects with low probability are not returned:

Allowing a lower probability may result in more objects being recognized:

MaxFeatures  (1)

By default, all the detected objects are returned:

Specify a maximum number of results:

MaxOverlapFraction  (1)

The detected bounding boxes may overlap each other:

Find only non-intersecting objects:

TargetDevice  (1)

By default, the function is evaluated on CPU:

Use the TargetDevice option to specify a different device:

Wolfram Research (2019), ImageContents, Wolfram Language function, (updated 2023).


Wolfram Research (2019), ImageContents, Wolfram Language function, (updated 2023).


Wolfram Language. 2019. "ImageContents." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2023.


Wolfram Language. (2019). ImageContents. Wolfram Language & System Documentation Center. Retrieved from


@misc{reference.wolfram_2024_imagecontents, author="Wolfram Research", title="{ImageContents}", year="2023", howpublished="\url{}", note=[Accessed: 18-June-2024 ]}


@online{reference.wolfram_2024_imagecontents, organization={Wolfram Research}, title={ImageContents}, year={2023}, url={}, note=[Accessed: 18-June-2024 ]}