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Domen
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ImageIdentify uses a neural network that was trained on a specific set of images, classifying them to a finite predefined set of "words" and "concepts". While this set is quite large, it does not contain all available entities.

Spelunking into code for ImageIdentify, one can obtain the learning set by:

ontologyData = 
  NeuralFunctions`Private`LoadPaclet["CNN_GraphData4", "Construct.m"];

Length[ontologyData["ObjectInterpretation"]]

50258

Looking at some of them:

Take[Keys[ontologyData["ObjectInterpretation"]], 50]

{"Aircraft", "Amphibian", "Animal", "Arachnid", "Bird", "Book",
"Bridge", "Building", "Canal", "Castle", "Comet", "Dam", "Dinosaur",
"Fish", "Image", "Insect", "Knot", "Lattice", "Mammal", "Package",
"Park", "Plant", "Pulsar", "Reptile", "Rocket", "Satellite",
"School", "Ship", "Shipwreck", "Stadium", "Star", "Supernova",
"Surface", "Table", "Tunnel", "instrumentality", "artefact", "",
"edifice", "structure", "ant bear", "anteater", "Orycteropus afer",
"eutherian", "eutherian mammal", "placental", "mammal", "craniate",
"animate being", "beast"}

We can see that the Harrier Jump Jet was part of the learning set, but BAe Jetstream unfortunately wasn't.

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "HarrierJumpJet"]]
(* True *)

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "BAeJetstream"]]
(* False *)

We can also list all available aircrafts.

KeySelect[ontologyData["ObjectInterpretation"], 
 EntityTypeName[#] == "Aircraft" &]

Aircrafts

ImageIdentify uses a neural network that was trained on a specific set of images, classifying them to a finite predefined set of "words" and "concepts". While this set is quite large, it does not contain all available entities.

Spelunking into code for ImageIdentify, one can obtain the learning set by:

ontologyData = 
  NeuralFunctions`Private`LoadPaclet["CNN_GraphData4", "Construct.m"];

Length[ontologyData["ObjectInterpretation"]]

50258

Looking at some of them:

Take[Keys[ontologyData["ObjectInterpretation"]], 50]

{"Aircraft", "Amphibian", "Animal", "Arachnid", "Bird", "Book",
"Bridge", "Building", "Canal", "Castle", "Comet", "Dam", "Dinosaur",
"Fish", "Image", "Insect", "Knot", "Lattice", "Mammal", "Package",
"Park", "Plant", "Pulsar", "Reptile", "Rocket", "Satellite",
"School", "Ship", "Shipwreck", "Stadium", "Star", "Supernova",
"Surface", "Table", "Tunnel", "instrumentality", "artefact", "",
"edifice", "structure", "ant bear", "anteater", "Orycteropus afer",
"eutherian", "eutherian mammal", "placental", "mammal", "craniate",
"animate being", "beast"}

We can see that the Harrier Jump Jet was part of the learning set, but BAe Jetstream unfortunately wasn't.

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "HarrierJumpJet"]]
(* True *)

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "BAeJetstream"]]
(* False *)

ImageIdentify uses a neural network that was trained on a specific set of images, classifying them to a finite predefined set of "words" and "concepts". While this set is quite large, it does not contain all available entities.

Spelunking into code for ImageIdentify, one can obtain the learning set by:

ontologyData = 
  NeuralFunctions`Private`LoadPaclet["CNN_GraphData4", "Construct.m"];

Length[ontologyData["ObjectInterpretation"]]

50258

Looking at some of them:

Take[Keys[ontologyData["ObjectInterpretation"]], 50]

{"Aircraft", "Amphibian", "Animal", "Arachnid", "Bird", "Book",
"Bridge", "Building", "Canal", "Castle", "Comet", "Dam", "Dinosaur",
"Fish", "Image", "Insect", "Knot", "Lattice", "Mammal", "Package",
"Park", "Plant", "Pulsar", "Reptile", "Rocket", "Satellite",
"School", "Ship", "Shipwreck", "Stadium", "Star", "Supernova",
"Surface", "Table", "Tunnel", "instrumentality", "artefact", "",
"edifice", "structure", "ant bear", "anteater", "Orycteropus afer",
"eutherian", "eutherian mammal", "placental", "mammal", "craniate",
"animate being", "beast"}

We can see that the Harrier Jump Jet was part of the learning set, but BAe Jetstream unfortunately wasn't.

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "HarrierJumpJet"]]
(* True *)

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "BAeJetstream"]]
(* False *)

We can also list all available aircrafts.

KeySelect[ontologyData["ObjectInterpretation"], 
 EntityTypeName[#] == "Aircraft" &]

Aircrafts

Source Link
Domen
  • 33.4k
  • 3
  • 47
  • 66

ImageIdentify uses a neural network that was trained on a specific set of images, classifying them to a finite predefined set of "words" and "concepts". While this set is quite large, it does not contain all available entities.

Spelunking into code for ImageIdentify, one can obtain the learning set by:

ontologyData = 
  NeuralFunctions`Private`LoadPaclet["CNN_GraphData4", "Construct.m"];

Length[ontologyData["ObjectInterpretation"]]

50258

Looking at some of them:

Take[Keys[ontologyData["ObjectInterpretation"]], 50]

{"Aircraft", "Amphibian", "Animal", "Arachnid", "Bird", "Book",
"Bridge", "Building", "Canal", "Castle", "Comet", "Dam", "Dinosaur",
"Fish", "Image", "Insect", "Knot", "Lattice", "Mammal", "Package",
"Park", "Plant", "Pulsar", "Reptile", "Rocket", "Satellite",
"School", "Ship", "Shipwreck", "Stadium", "Star", "Supernova",
"Surface", "Table", "Tunnel", "instrumentality", "artefact", "",
"edifice", "structure", "ant bear", "anteater", "Orycteropus afer",
"eutherian", "eutherian mammal", "placental", "mammal", "craniate",
"animate being", "beast"}

We can see that the Harrier Jump Jet was part of the learning set, but BAe Jetstream unfortunately wasn't.

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "HarrierJumpJet"]]
(* True *)

KeyExistsQ[ontologyData["ObjectInterpretation"], 
 Entity["Aircraft", "BAeJetstream"]]
(* False *)