gives an association of lists of subimages for each identified category of objects in image.


gives a list of subimages identified as an instance of the specified category.


gives the specified property prop for each identified subimage.


gives an association with lists of subimages identified as being instances of each of the categoryi.

Details and Options

  • ImageCases attempts to find instances of an object category present in an image and returns a list of subimages per category.
  • 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
  • The following options can be given:
  • AcceptanceThresholdAutomaticidentification acceptance threshold
    MaxFeaturesAutomaticmaximum number of subimages to return
    MaxOverlapFractionAutomaticmaximum bounding box overlap
    TargetDevice"CPU"the target device on which to compute
  • ImageCases uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.
  • ImageCases 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  (2)

Find identified subimages of an image:

Find subimages identified as a dog:

Scope  (7)

Find all the detectable objects in an image:

Find all birds in the image:

Find the birds' bounding boxes:

Return multiple properties:

Query properties of the concept:

Group together the detection for multiple categories:

Return the specified properties for all the detectable categories:

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:

Applications  (1)

Build an image captioning function using the counts of detected objects:

Transform the object counts in a string:

Package all the code in a function to automatically generate captions:

Try the function on a different image:

Caption an image with no identified objects:

Wolfram Research (2019), ImageCases, Wolfram Language function,


Wolfram Research (2019), ImageCases, Wolfram Language function,


@misc{reference.wolfram_2020_imagecases, author="Wolfram Research", title="{ImageCases}", year="2019", howpublished="\url{}", note=[Accessed: 22-January-2021 ]}


@online{reference.wolfram_2020_imagecases, organization={Wolfram Research}, title={ImageCases}, year={2019}, url={}, note=[Accessed: 22-January-2021 ]}


Wolfram Language. 2019. "ImageCases." Wolfram Language & System Documentation Center. Wolfram Research.


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