InceptionNet

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Table of Contents Introduction I/O Examples credits Algorithm Console Introduction This algorithm is a direct implementation of Google's InceptionNet , which was trained on the ImageNet 2015 dataset . It is implemented using Google's Tensorflow  python bindings, the InceptionNet model architecture is as shown below: I/O Input source: String
 source - (required) - the source image to classify, this must be in one of the following formats: data connector URI, web http/https resource file http://.. or https://... , or a base64 encoded JPEG String. Output { 
 "tags":[ 
 { 
 "confidence": Double,
 "tag": String
 },
 { 
 "confidence": Double,
 "tag": String
 },
 { 
 "confidence": Double,
 "tag": String
 },
 { 
 "confidence": Double,
 "tag": String
 },
 { 
 "confidence":Double,
 "tag": String
 }
 ]
}
 tags - The top 5 classes that the model predicts are relevant to the image. className - The classname for the class. confidence - The confidence that this label is relevant. Note: The first call to this algorithm will take a bit longer than sequential calls to due algorithm initialization. All following calls will be significantly faster. Examples Example 1 input "http://i.imgur.com/LkrFjJP.jpg"
 output { 
 "tags":[ 
 { 
 "class":"convertible",
 "confidence":0.3217212557792664
 },
 { 
 "class":"sports car, sport car",
 "confidence":0.18678018450737005
 },
 { 
 "class":"racer, race car, racing car",
 "confidence":0.09395135194063188
 },
 { 
 "class":"car wheel",
 "confidence":0.08665172755718234
 },
 { 
 "class":"grille, radiator grille",
 "confidence":0.07477507740259172
 }
 ]
}
 Example 2 input "http://i.imgur.com/YKDmneL.jpg"
 output {
 "tags":[
 {"class":"Samoyed, Samoyede","confidence":0.9004066586494446},
 {"class":"keeshond","confidence":0.004059927538037299},
 {"class":"Pomeranian","confidence":0.0024697233457118277},
 {"class":"Eskimo dog, husky","confidence":0.0013620780082419515},
 {"class":"Loafer","confidence":0.0011537882965058086}
 ]
} Credits For more info please check out Going Deeper With Convolutions by Christian Szegedy, Wei Liu et al All sample images retrived from www.imgur.com on May 26, 2016.

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curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/deeplearning/InceptionNet/1.0.1
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algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://deeplearning/InceptionNet/1.0.1 -d '{{input | formatInput:"cli"}}'
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import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://deeplearning/InceptionNet/1.0.1");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://deeplearning/InceptionNet/1.0.1")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://deeplearning/InceptionNet/1.0.1")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://deeplearning/InceptionNet/1.0.1")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/InceptionNet/1.0.1')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/InceptionNet/1.0.1")
result <- algo$pipe(input)$result
print(result)
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require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/InceptionNet/1.0.1')
puts algo.pipe(input).result
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use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("deeplearning/InceptionNet/1.0.1");
let response = algo.pipe(input);
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import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "deeplearning/InceptionNet/1.0.1") { resp, error in
  print(resp)
}
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