yields the result of attempting to identify what image is a picture of.


restricts the identification of image to objects within the specified category.


gives a list of up to n possible identifications.


gives the specified property for each identification.

Details and Options

  • ImageIdentify[{image1,image2,},] can be used to identify objects in multiple images.
  • In ImageIdentify[image,category], possible forms for category include:
  • "type"entity type, as used in Interpreter
    "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
  • By default, ImageIdentify returns objects of the form Entity["Concept",].
  • The property "prop" can be one of the following:
  • "Concept"a concept entity object
    "Entity"when possible, a concrete entity object
    "Probability"an association of concepts and probabilities
    "cprop"a property supported by "Concept" entities
    {prop1,}a list of property specifications
  • The following options can be given:
  • AcceptanceThreshold Automaticidentification acceptance threshold
    PerformanceGoal Automaticwhat to optimize in the identification
    SpecificityGoal Automaticwhat specificity of object type to seek
    TargetDevice"CPU"the target device on which to compute
  • Possible settings for PerformanceGoal include "Speed" and "Quality".
  • Possible settings for SpecificityGoal include:
  • "Low"favor general categories of objects
    "High"favor specific kinds of objects
    sspecificity between 0 (lowest) and 1 (highest)
  • When no content is found at an acceptable threshold, Missing["Unidentified"] is returned.
  • ImageIdentify uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.
  • ImageIdentify may download resources that will be stored in your local object store at $LocalBase, and can be listed using LocalObjects[] and removed using ResourceRemove.


open allclose all

Basic Examples  (2)

Identify the object present in the image:

Identify what type of dog is present in the image:

Scope  (3)

Return a result in a specific category:

Return a list of results:

Return a list of results and their associated probability:

Options  (4)

AcceptanceThreshold  (1)

The AcceptanceThreshold is selected automatically:

Specify a custom threshold:

If no identification is above the threshold, a Missing object is returned:

PerformanceGoal  (1)

Use PerformanceGoal"Speed" to get a result as fast as possible:

Use a slower, more accurate recognition:

SpecificityGoal  (2)

Privilege a result with a low specificity:

Privilege a result with a high specificity:

Use a custom value:

Get a table of identifications for different values of specificity:

Properties & Relations  (1)

The neural net used by ImageIdentify can be accessed using NetModel:

Possible Issues  (1)

If a recognition category is specified, probabilities are renormalized in that category:

Remove the category constraint to recognize the object correctly:

Neat Examples  (1)

Get 10 different identifications, along with their probabilities:

Visualize the result of object identification using a WordCloud:

Wolfram Research (2015), ImageIdentify, Wolfram Language function, (updated 2023).


Wolfram Research (2015), ImageIdentify, Wolfram Language function, (updated 2023).


Wolfram Language. 2015. "ImageIdentify." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2023.


Wolfram Language. (2015). ImageIdentify. Wolfram Language & System Documentation Center. Retrieved from


@misc{reference.wolfram_2024_imageidentify, author="Wolfram Research", title="{ImageIdentify}", year="2023", howpublished="\url{}", note=[Accessed: 24-May-2024 ]}


@online{reference.wolfram_2024_imageidentify, organization={Wolfram Research}, title={ImageIdentify}, year={2023}, url={}, note=[Accessed: 24-May-2024 ]}