Car Make and Model Recognition [Beta]

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Introduction This algorithm provides a solution to classify cars by their make, model, body style and model year from images. The capabilities provided so far are as follows: Recognizing over 2000 car models Detection from multiple angles Coverage of a great portion of Western brands and models, from 2000 (and older) to present Providing top 3 most accurate results Update April 20, 2017 Gathering the data to train the neural net is now automated, with only the quality checks still manual. As a result the data set is expanding at a fast rate (300k+ images), improving the average classification rate considerably. In the coming weeks we will be working on getting a minimum amount of training data for each of the 2663 currently labelled car models, focusing on the newest models from Western markets first. Example This photo of a car, taking by a security camera at a parking lot, is correctly identified as the 2003 Daewoo Lacetti Hatchback: "data://LgoBE/Images/unidentified_seccam2.png" 
 [ {
 "body_style" : "Hatchback" , 
 "confidence" : "0.30" , 
 "make" : "Daewoo" , 
 "model" : "Lacetti" , 
 "model_year" : "2003" 
}, ... ] 
 Input The algorithm takes an image URL as input. The image should be in JPEG or PNG format. Note that all images will be resized to 256x256 pixels. Images are downloaded with Algorithmia's SmartImageDownloader utility ( ). "" Output The algorithm will return a top 3 of car models, ordered by confidence in the prediction. [
 "body_style" : "MPV" ,
 "confidence" : "1.00" ,
 "make" : "Opel" ,
 "model" : "Meriva" ,
 "model_year" : "2010" 
 }, ... ] Make : name of the car brand, e.g. BMW, Ford, Land Rover. Model : name of the car model, e.g. X5, Mustang, Discovery Body_style is one of these values: Convertible, Coupe, Hardtop, Sedan, Wagon, SUV Cargo Van, Club Cab, Crew Cab, Double Cab, Extended Cab, King Cab, Mega Cab, Quad Cab, Regular Cab, SuperCab Fastback, Hatchback Van, Wagon Van, Minivan, Passenger Van MPV, Mini MPV Buggy, Racing, Roadster Truck Model_Year : The model year, or the start-of-production year. Confidence : a number between 0 and 1 indicating the confidence in the prediction, rounded to two decimal places.

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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://LgoBE/CarMakeandModelRecognition/0.3.9 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

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

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://LgoBE/CarMakeandModelRecognition/0.3.9")
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://LgoBE/CarMakeandModelRecognition/0.3.9");
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://LgoBE/CarMakeandModelRecognition/0.3.9")
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://LgoBE/CarMakeandModelRecognition/0.3.9");
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('LgoBE/CarMakeandModelRecognition/0.3.9')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("LgoBE/CarMakeandModelRecognition/0.3.9")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('LgoBE/CarMakeandModelRecognition/0.3.9')
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("LgoBE/CarMakeandModelRecognition/0.3.9");
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: "LgoBE/CarMakeandModelRecognition/0.3.9") { resp, error in
View Swift Docs
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