({{, (RefLink[ImageIdentify, paclet:ref/ImageIdentify])[image] 
yields the result of attempting to identify what image is a picture of. }, {, (RefLink[ImageIdentify, paclet:ref/ImageIdentify])[image,category]
restricts the identification of image to objects within the specified category.}, {, (RefLink[ImageIdentify, paclet:ref/ImageIdentify])[image,category,n]
gives a list of up to n possible identifications.}, {, (RefLink[ImageIdentify, paclet:ref/ImageIdentify])[image,category,n,"prop"]
gives the specified property for each identification.}})

OpenersNotesSection(Spacer1)Details and Options

(RefLink[ImageIdentify, paclet:ref/ImageIdentify])[{(Subscript[image, 1]),(Subscript[image, 2]),…},…] can be used to identify objects in multiple images.

In (RefLink[ImageIdentify, paclet:ref/ImageIdentify])[image,category], possible forms for category include:

By default, (RefLink[ImageIdentify, paclet:ref/ImageIdentify]) returns objects of the form (RefLink[Entity, paclet:ref/Entity])["Concept",…].

The property "prop" can be one of the following:

The following options can be given:

Possible settings for (RefLink[SpecificityGoal, paclet:ref/SpecificityGoal]) include:

When no content is found at an acceptable threshold, (RefLink[Missing, paclet:ref/Missing])["Unidentified"] is returned.

(RefLink[ImageIdentify, paclet:ref/ImageIdentify]) uses machine learning, and its training set and methods may change in different versions of the Wolfram Language, yielding different results.

(RefLink[ImageIdentify, paclet:ref/ImageIdentify]) may download resources that will be stored in your local object store at (RefLink[$LocalBase, paclet:ref/$LocalBase]), and can be listed using (RefLink[LocalObjects, paclet:ref/LocalObjects])[] and removed using (RefLink[ResourceRemove, paclet:ref/ResourceRemove]).

OpenersPrimaryExamplesSection(Spacer1)Examples  (11)

OpenersExampleSection0(Spacer1)Basic Examples  (2)

Identify the object present in the image:

ImageIdentify[]

Identify what type of dog is present in the image:

ImageIdentify[,(Entity["dog", Entity["Species","Infraspecies:CanisLupusFamiliaris"], "Entity[\"Species\", \"Infraspecies:CanisLupusFamiliaris\"]", "species specification"])]

OpenersExampleSection1(Spacer1)Scope  (3)

Return a result in a specific category:

ImageIdentify[,(Entity["fruit", Entity["Concept","Fruit::855t9"], "Entity[\"Concept\", \"Fruit::855t9\"]", "concept"])]

Return a list of results:

ImageIdentify[,All,6]

Return a list of results and their associated probability:

ImageIdentify[,(Entity["fruit", Entity["Concept","Fruit::855t9"], "Entity[\"Concept\", \"Fruit::855t9\"]", "concept"]),10,"Probability"]

OpenersExampleSection2(Spacer1)Options  (3)

OpenersExampleSubsection0(Spacer1)RecognitionThreshold  (1)

The (RefLink[AcceptanceThreshold, paclet:ref/AcceptanceThreshold]) is selected automatically:

ImageIdentify[]

Specify a custom threshold:

ImageIdentify[,AcceptanceThreshold->.95]

If no identification is above the threshold, a (RefLink[Missing, paclet:ref/Missing]) object is returned:

ImageIdentify[,AcceptanceThreshold->1]

OpenersExampleSubsection1(Spacer1)SpecificityGoal  (2)

Privilege a result with a low specificity:

ImageIdentify[,SpecificityGoal->"Low"]

Privilege a result with a high specificity:

ImageIdentify[,SpecificityGoal->"High"]

Use a custom value:

ImageIdentify[,SpecificityGoal->0]

Get a table of identifications for different values of specificity:

DeleteDuplicatesBy[Table[{s,ImageIdentify[,SpecificityGoal->s]},{s,0,1,.1}],Last]

OpenersExampleSection3(Spacer1)Properties & Relations  (1)

The neural net used by (RefLink[ImageIdentify, paclet:ref/ImageIdentify]) can be accessed using (RefLink[NetModel, paclet:ref/NetModel]):

net=NetModel["Wolfram ImageIdentify Net V1"]

net[,"TopProbabilities"]

OpenersExampleSection4(Spacer1)Possible Issues  (1)

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

ImageIdentify[,"tree",3,"Probability"]

Remove the category constraint to recognize the object correctly:

ImageIdentify[,All,3,"Probability"]

OpenersExampleSection5(Spacer1)Neat Examples  (1)

Get 10 different identifications, along with their probabilities:

data=ImageIdentify[,(Entity["car", Entity["Word","car"], "Entity[\"Word\", \"car\"]", "word"]),10,"Probability"]

Visualize the result of object identification using a (RefLink[WordCloud, paclet:ref/WordCloud]):

WordCloud[data]

({{DocumentationFooterIconsRelatedFunction.png, ({{See Also}, {(RefLink[ImageCases, paclet:ref/ImageCases])  ▪  (RefLink[ImageContents, paclet:ref/ImageContents])  ▪  (RefLink[ImageInstanceQ, paclet:ref/ImageInstanceQ])  ▪  (RefLink[ImageContainsQ, paclet:ref/ImageContainsQ])  ▪  (RefLink[Classify, paclet:ref/Classify])  ▪  (RefLink[FeatureExtract, paclet:ref/FeatureExtract])  ▪  (RefLink[FindFaces, paclet:ref/FindFaces])  ▪  (RefLink[TextRecognize, paclet:ref/TextRecognize])  ▪  (RefLink[ImageKeypoints, paclet:ref/ImageKeypoints])  ▪  (RefLink[Interpreter, paclet:ref/Interpreter])  ▪  (RefLink[NetModel, paclet:ref/NetModel])  ▪  (RefLink[AudioIdentify, paclet:ref/AudioIdentify])}})}})

({{DocumentationFooterIconsRelatedGuide.png, ({{Related Guides}, {▪(RefLinkPlain[Machine Learning, paclet:guide/MachineLearning])}, {▪(RefLinkPlain[Image Processing & Analysis, paclet:guide/ImageProcessing])}, {▪(RefLinkPlain[Computer Vision, paclet:guide/ComputerVision])}, {▪(RefLinkPlain[Computational Photography, paclet:guide/ComputationalPhotography])}, {▪(RefLinkPlain[Earth Sciences: Data & Computation, paclet:guide/EarthSciencesDataAndComputation])}, {▪(RefLinkPlain[Neural Networks, paclet:guide/NeuralNetworks])}})}})

({{(Spacer1), ({{(Spacer1)Related Links}, {(WebLink[DocumentationFooterIconsRelatedLinks-EIWL.png(Spacer1)An Elementary Introduction to the Wolfram Language, https://www.wolfram.com/language/elementary-introduction/22-machine-learning.html])(WebLink[: Machine Learning, https://www.wolfram.com/language/elementary-introduction/22-machine-learning.html])}})}})