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Table of Contents Introduction I/O Class List Examples credits Algorithm Console Introduction This algorithm detects clothing items in images; it returns a list of discovered clothing articles as well as annotating the input image with bounding boxes for each found article. I/O Json input variant Input { 
 "threshold": Float,
 image - (required) - an input image as either a url, data connector uri (data://, s3://, etc) or a base 64 encoded string. output - (optional) - the output data connector path to where you want to save the thumbnail to. defaults to data://.algo/temp/UUID.png threshold - (optional) - the minimum confidence for a label to be drawn / returned in the tag list. defaults to 0.65 tags_only - (optional) - set this to true if you don't want the annotated image output. defaults to 'false' Simple input variant Input input: String/Byte[]
 input - (required) - an input image as either a url, data connector uri (data://, s3://, etc), base 64 encoded string, or a binary array. Output { 
 output - If tags_only is false , then this key contains the url to the annotated image. articles - the list of discovered articles. bounding box - the rectangular coordinates of the most relevent bounding box, defined as (x0,y0), (x1,y1). article_name - the name of the discovered article. confidence - the algorithm's confidence that this article exists in the image. Class List Here is a list of the article classes: top handle bag t shirt jewelry boots sunglasses jeans sweater tank top skirt sandals leggings button down shirt pants casual heels pumps or wedges lingerie blouse lightweight jacket casual dress winter jacket formal dress watches swimsuits hat vest sneakers wallets shoulder bag flats overall sweatpants shorts rompers pants suit formal glasses clutches socks scarves or wraps backpack or messenger bag jumpsuit running shoes blazer tunic hosiery denim jacket hoodie belts leather jacket trenchcoat headwrap sweater dress sweatshirt rain jacket polo shirt robe hiking shoes luggage gloves underwear Example Input { 
 Output { 
 Credits This algorithm is a machine learning model based on the faster-rcnn project,which was inspired by the Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks paper. All sample images courtesy of the wikimedia foundation

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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://algorithmiahq/DeepFashion/0.1.1 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

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

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://algorithmiahq/DeepFashion/0.1.1")
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://algorithmiahq/DeepFashion/0.1.1");
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://algorithmiahq/DeepFashion/0.1.1")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript 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('algorithmiahq/DeepFashion/0.1.1')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("algorithmiahq/DeepFashion/0.1.1")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('algorithmiahq/DeepFashion/0.1.1')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("algorithmiahq/DeepFashion/0.1.1");
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: "algorithmiahq/DeepFashion/0.1.1") { resp, error in
View Swift Docs
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