Gender Classification

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1. Introduction This algorithm classifies gender for each person in any given image. Currently it only supports two genders. (male & female) Input: (Required) Image Data API Url, Web (http/https) Url, binary image or a base64 encoded image. Output: Gender and bounding-box information of each person in the given image 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/m_ali.jpg" } Output { "results": [ { "bbox": { "bottom": 2908, "left": 700, "right": 2356, "top": 1252 }, "gender": [ {"confidence": 0.997405171394348, "gender": "Male"}, {"confidence": 0.0025948521215468645, "gender": "Female"} ], "person": 0 } ] } Example 2. Parameter 1: HTTP Url { "image": "" } Output: { "results": [ { "bbox": { "bottom": 2908, "left": 700, "right": 2356, "top": 1252 }, "gender": [ {"confidence": 0.997405171394348, "gender": "Male"}, {"confidence": 0.0025948521215468645, "gender": "Female"} ], "person": 0 } ] } Example 3. Parameter 1: Base64 image { "image": "data:image/png;base64....", } Output; { "results": [ { "bbox": { "bottom": 2908, "left": 700, "right": 2356, "top": 1252 }, "gender": [ {"confidence": 0.997405171394348, "gender": "Male"}, {"confidence": 0.0025948521215468645, "gender": "Female"} ], "person": 0 } ] } 3. Credits for more information, please refer to: Gil Levi and Tal Hassner, Age and Gender Classification using Convolutional Neural Networks, IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015 dlib/FaceDetection was used to detect faces in given images. Demo is was taken from:

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3. Use this algorithm

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/GenderClassification/2.0.0 -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/GenderClassification/2.0.0")
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/GenderClassification/2.0.0");
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/GenderClassification/2.0.0")
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/GenderClassification/2.0.0");
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/GenderClassification/2.0.0')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/GenderClassification/2.0.0")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/GenderClassification/2.0.0')
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/GenderClassification/2.0.0");
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/GenderClassification/2.0.0") { resp, error in
View Swift Docs
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