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1. Introduction This is the CaffeNet image classifier for classifying Imagenet categories. These categories can be found here . Input: (Required) Image Data API Url, Web (http/https) Url, binary image or a base64 encoded image. (Optional)  Number of results. (Default=5) Output: Top N classifications. 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. 2. Examples Example 1. Parameter 1: Data API Url { "image": "data://deeplearning/example_data/bananas.jpg" } Output { "results":[ {"confidence":0.999562680721283,"label":"banana"}, {"confidence":0.00039236515294760466,"label":"mortar"}, {"confidence":0.000008457073818135541,"label":"pitcher, ewer"}, {"confidence":0.000007989773621375207,"label":"lemon"}, {"confidence":0.0000034339670946792467,"label":"clog, geta, patten, sabot"} ] } Example 2. Parameter 1: HTTP Url { "image": "" } Output: { "results": [ {"confidence": 0.5612723231315613,"label": "running shoe"}, {"confidence": 0.09682515263557434,"label": "iron, smoothing iron"}, {"confidence": 0.0658978521823883, "label": "Loafer"}, {"confidence": 0.05883505567908287,"label": "clog, geta, patten, sabot"}, {"confidence": 0.024631954729557037, "label": "mitten"}, {"confidence": 0.020255278795957565, "label": "sock"}, {"confidence": 0.019952893257141113, "label": "stole"}, {"confidence": 0.01743927411735058,"label": "wool, woolen, woollen"}, {"confidence": 0.013626870699226856,"label": "jean, blue jean, denim"}, {"confidence": 0.012965059839189053, "label": "purse"}, {"confidence": 0.009541100822389126,"label": "bow tie, bow-tie, bowtie"}, {"confidence": 0.007822269573807716,"label": "handkerchief, hankie, hanky, hankey"}, {"confidence": 0.0065187751315534115,"label": "sandal"}, {"confidence": 0.0061787632293999195,"label": "bath towel"}, {"confidence": 0.005863594356924295, "label": "buckle"} ] } Example 3. Parameter 1: Base64 image { "image": "data:image/png;base64....", } Output; { "results":[ {"confidence": 0.4345380365848542, "label":"sports car, sport car"}, {"confidence": 0.28446707129478466, "label": "convertible"}, {"confidence": 0.10041847825050354,"label": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon"}, {"confidence": 0.053312629461288466, "label": "car wheel"}, {"confidence": 0.04541682079434396,"label": "racer, race car, racing car"} ] } Example 4. Parameter 1: Data API Url Parameter 2: Number of results. { "image": "data://deeplearning/example_data/bananas.jpg", "numResults": 15 } Output: { "results":[ {"confidence":0.999562680721283,"label":"banana"}, {"confidence":0.00039236515294760466,"label":"mortar"}, {"confidence":0.000008457073818135541,"label":"pitcher, ewer"}, {"confidence":0.000007989773621375207,"label":"lemon"}, {"confidence":0.0000034339670946792467,"label":"clog, geta, patten, sabot"}, {"confidence":0.000002298381104992586,"label":"cup"}, {"confidence":0.000002161226348107448,"label":"mixing bowl"}, {"confidence":0.0000018423750134388683,"label":"pineapple, ananas"}, {"confidence":0.0000018352727693127235,"label":"strainer"}, {"confidence":0.0000016318584812324843,"label":"ladle"}, {"confidence":0.000001281421191379195,"label":"teapot"}, {"confidence":0.0000010122270168722023,"label":"spaghetti squash"}, {"confidence":9.90984858617594e-7,"label":"neck brace"}, {"confidence":9.23445895750774e-7,"label":"bathtub, bathing tub, bath, tub"}, {"confidence":8.778124538366683e-7,"label":"slug"} ] } 3. Credits For more information, please refer to: Demo image(s) were retrieved from:

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curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://deeplearning/CaffeNet/2.0.1 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://deeplearning/CaffeNet/2.0.1")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
View Go Docs
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/CaffeNet/2.0.1");
AlgoResponse result = algo.pipeJson(input);
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/CaffeNet/2.0.1")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://deeplearning/CaffeNet/2.0.1");
var response = algorithm.pipe<object>(input);
View .NET/C# Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/CaffeNet/2.0.1')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/CaffeNet/2.0.1")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/CaffeNet/2.0.1')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("deeplearning/CaffeNet/2.0.1");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "deeplearning/CaffeNet/2.0.1") { resp, error in
View Swift Docs
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