Links

Hosted API (Remote Server)

Leverage your custom trained model for cloud-hosted inference.

Overview

Each model trained with Roboflow Train is deployed as a custom API you can use to make predictions from any device that has an internet connection. Inference is done on the server so you don't need to worry about the edge device's hardware capabilities.
We automatically scale this API up and down and do load balancing for you so that you can rest assured that your application will be able to handle sudden spikes in traffic without having to pay for GPU time you're not using. Our hosted prediction API has been battle-hardened to handle even the most demanding production applications (including concurrently surviving through the famous Hacker News and Reddit "hugs of death" without so much as batting an eye).

Response Object Format

The hosted API inference route returns a JSON object containing an array of predictions. Each prediction has the following properties:
  • x = the horizontal center point of the detected object
  • y = the vertical center point of the detected object
  • width = the width of the bounding box
  • height = the height of the bounding box
  • class = the class label of the detected object
  • confidence = the model's confidence that the detected object has the correct label and position coordinates
  • points =list of points that make of the polygon outline of the object - each item in the list is an object with keys x and y for the horizontal and vertical coordinate of the point respectively
// an example JSON object
{
"predictions": [
{
"x": 179.2,
"y": 247,
"width": 231,
"height": 147,
"class": "A",
"confidence": 0.98,
"points": [
{
"x": 134,
"y": 314
},
{
"x": 116,
"y": 313
},
{
"x": 103,
"y": 310.1
},
{
"x": 72.7,
"y": 282
},
{
"x": 66.8,
"y": 273
},
]
}
]
}
Note: position (0,0) refers to the top-left corner of the image.

Methods of Deployment

The Example Web App

The easiest way to familiarize yourself with the inference endpoint is to visit the Example Web App. To use the Web App, simply input your model , version and api_key. These will be pre-filled for you after training completes if you click through via the web UI under your versions "Training Results" section.
Then select an image via Browse. After you have chosen the settings you want, click Run Inference.
On the left side of the screen, you will see example JavaScript code for posting a base64-encoded image to the inference endpoint.

Curl Command

Obtaining Your Model Endpoint

To use the inference API, you will need your model url slug, version number and the API key for the workspace the project belongs to. Your API key can be retrieved from in your workspace's settings page under the "Roboflow API" section. Your model url slug is the unique and url-safe version of your dataset name. The easiest way to retrieve it is via the web UI by clicking the "curl command" link:
post
https://outline.roboflow.com
/:datasetSlug/:versionNumber
Using the Inference API

Code Snippets

For your convenience, we've provided code snippets for calling this endpoint in various programming languages. If you need help integrating the inference API into your project don't hesitate to reach out.
All examples upload to an example dataset with a model-endpoint of your-dataset-slug/your-version. You can easily find your dataset's identifier by looking at the curl command shown in the Roboflow web interface after your model has finished training.
cURL
Python
Javascript
Swift/iOS
Android
Ruby
PHP
Go
.NET
Elixer

Linux or MacOS

Retrieving JSON predictions for a local file called YOUR_IMAGE.jpg:
base64 YOUR_IMAGE.jpg | curl -d @- \
"https://outline.roboflow.com/your-model/42?api_key=YOUR_KEY"
Inferring on an image hosted elsewhere on the web via its URL (don't forget to URL encode it):
curl -X POST "https://outline.roboflow.com/your-model/42?\
api_key=YOUR_KEY&\
image=https%3A%2F%2Fi.imgur.com%2FPEEvqPN.png"

Windows

You will need to install curl for Windows and GNU's base64 tool for Windows. The easiest way to do this is to use the git for Windows installer which also includes the curl and base64 command line tools when you select "Use Git and optional Unix tools from the Command Prompt" during installation.
Then you can use the same commands as above.

Infer on Local and Hosted Images

To install dependencies, pip install roboflow
from roboflow import Roboflow
rf = Roboflow(api_key="API_KEY")
project = rf.workspace().project("MODEL_ENDPOINT")
model = project.version(VERSION).model
# infer on a local image
print(model.predict("your_image.jpg").json())
# infer on an image hosted elsewhere
print(model.predict("URL_OF_YOUR_IMAGE").json())
# save an image annotated with your predictions
model.predict("your_image.jpg").save("prediction.jpg")

Node.js

We're using axios to perform the POST request in this example so first run npm install axios to install the dependency.

Inferring on a Local Image

const axios = require("axios");
const fs = require("fs");
const image = fs.readFileSync("YOUR_IMAGE.jpg", {
encoding: "base64"
});
axios({
method: "POST",
url: "https://outline.roboflow.com/your-model/42",
params: {
api_key: "YOUR_KEY"
},
data: image,
headers: {
"Content-Type": "application/x-www-form-urlencoded"
}
})
.then(function(response) {
console.log(response.data);
})
.catch(function(error) {
console.log(error.message);
});

Inferring on an Image Hosted Elsewhere via URL

const axios = require("axios");
axios({
method: "POST",
url: "https://outline.roboflow.com/your-model/42",
params: {
api_key: "YOUR_KEY",
image: "https://i.imgur.com/PEEvqPN.png"
}
})
.then(function(response) {
console.log(response.data);
})
.catch(function(error) {
console.log(error.message);
});

Web

We have realtime on-device inference available via roboflow.js; see the documentation here.

Swift

Inferring on a Local Image

import UIKit
// Load Image and Convert to Base64
let image = UIImage(named: "your-image-path") // path to image to upload ex: image.jpg
let imageData = image?.jpegData(compressionQuality: 1)
let fileContent = imageData?.base64EncodedString()
let postData = fileContent!.data(using: .utf8)
// Initialize Inference Server Request with API_KEY, Model, and Model Version
var request = URLRequest(url: URL(string: "https://detect.roboflow.com/your-model/your-model-version?api_key=YOUR_APIKEY&name=YOUR_IMAGE.jpg")!,timeoutInterval: Double.infinity)
request.addValue("application/x-www-form-urlencoded", forHTTPHeaderField: "Content-Type")
request.httpMethod = "POST"
request.httpBody = postData
// Execute Post Request
URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in
// Parse Response to String
guard let data = data else {
print(String(describing: error))
return
}
// Convert Response String to Dictionary
do {
let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any]
} catch {
print(error.localizedDescription)
}
// Print String Response
print(String(data: data, encoding: .utf8)!)
}).resume()

Objective C

Kotlin

Inferring on a Local Image

import java.io.*
import java.net.HttpURLConnection
import java.net.URL
import java.nio.charset.StandardCharsets
import java.util.*
fun main() {
// Get Image Path
val filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg"
val file = File(filePath)
// Base 64 Encode
val encodedFile: String
val fileInputStreamReader = FileInputStream(file)
val bytes = ByteArray(file.length().toInt())
fileInputStreamReader.read(bytes)
encodedFile = String(Base64.getEncoder().encode(bytes), StandardCharsets.US_ASCII)
val API_KEY = "" // Your API Key
val MODEL_ENDPOINT = "dataset/v" // Set model endpoint (Found in Dataset URL)
// Construct the URL
val uploadURL ="https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg";
// Http Request
var connection: HttpURLConnection? = null
try {
// Configure connection to URL
val url = URL(uploadURL)
connection = url.openConnection() as HttpURLConnection
connection.requestMethod = "POST"
connection.setRequestProperty("Content-Type",
"application/x-www-form-urlencoded")
connection.setRequestProperty("Content-Length",
Integer.toString(encodedFile.toByteArray().size))
connection.setRequestProperty("Content-Language", "en-US")
connection.useCaches = false
connection.doOutput = true
//Send request
val wr = DataOutputStream(
connection.outputStream)
wr.writeBytes(encodedFile)
wr.close()
// Get Response
val stream = connection.inputStream
val reader = BufferedReader(InputStreamReader(stream))
var line: String?
while (reader.readLine().also { line = it } != null) {
println(line)
}
reader.close()
} catch (e: Exception) {
e.printStackTrace()
} finally {
connection?.disconnect()
}
}
main()

Inferring on an Image Hosted Elsewhere via URL

import java.io.BufferedReader
import java.io.DataOutputStream
import java.io.InputStreamReader
import java.net.HttpURLConnection
import java.net.URL
import java.net.URLEncoder
fun main() {
val imageURL = "https://i.imgur.com/PEEvqPN.png" // Replace Image URL
val API_KEY = "" // Your API Key
val MODEL_ENDPOINT = "dataset/v" // Set model endpoint
// Upload URL
val uploadURL = "https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, "utf-8");
// Http Request
var connection: HttpURLConnection? = null
try {
// Configure connection to URL
val url = URL(uploadURL)
connection = url.openConnection() as HttpURLConnection
connection.requestMethod = "POST"
connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded")
connection.setRequestProperty("Content-Length", Integer.toString(uploadURL.toByteArray().size))
connection.setRequestProperty("Content-Language", "en-US")
connection.useCaches = false
connection.doOutput = true
// Send request
val wr = DataOutputStream(connection.outputStream)
wr.writeBytes(uploadURL)
wr.close()
// Get Response
val stream = URL(uploadURL).openStream()
val reader = BufferedReader(InputStreamReader(stream))
var line: String?
while (reader.readLine().also { line = it } != null) {
println(line)
}
reader.close()
} catch (e: Exception) {
e.printStackTrace()
} finally {
connection?.disconnect()
}
}
main()

Java

Inferring on a Local Image

import java.io.*;
import java.net.HttpURLConnection;
import java.net.URL;
import java.nio.charset.StandardCharsets;
import java.util.Base64;
public class InferenceLocal {
public static void main(String[] args) throws IOException {
// Get Image Path
String filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg";
File file = new File(filePath);
// Base 64 Encode
String encodedFile;
FileInputStream fileInputStreamReader = new FileInputStream(file);
byte[] bytes = new byte[(int) file.length()];
fileInputStreamReader.read(bytes);
encodedFile = new String(Base64.getEncoder().encode(bytes), StandardCharsets.US_ASCII);
String API_KEY = ""; // Your API Key
String MODEL_ENDPOINT = "dataset/v"; // model endpoint
// Construct the URL
String uploadURL = "https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY
+ "&name=YOUR_IMAGE.jpg";
// Http Request
HttpURLConnection connection = null;
try {
// Configure connection to URL
URL url = new URL(uploadURL);
connection = (HttpURLConnection) url.openConnection();
connection.setRequestMethod("POST");
connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded");
connection.setRequestProperty("Content-Length", Integer.toString(encodedFile.getBytes().length));
connection.setRequestProperty("Content-Language", "en-US");
connection.setUseCaches(false);
connection.setDoOutput(true);
// Send request
DataOutputStream wr = new DataOutputStream(connection.getOutputStream());
wr.writeBytes(encodedFile);
wr.close();
// Get Response
InputStream stream = connection.getInputStream();
BufferedReader reader = new BufferedReader(new InputStreamReader(stream));
String line;
while ((line = reader.readLine()) != null) {
System.out.println(line);
}
reader.close();
} catch (Exception e) {
e.printStackTrace();
} finally {
if (connection != null) {
connection.disconnect();
}
}
}
}

Inferring on an Image Hosted Elsewhere via URL

import java.io.BufferedReader;
import java.io.DataOutputStream;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.net.HttpURLConnection;
import java.net.URL;
import java.net.URLEncoder;
import java.nio.charset.StandardCharsets;
public class InferenceHosted {
public static void main(String[] args) {
String imageURL = "https://i.imgur.com/PEEvqPN.png"; // Replace Image URL
String API_KEY = ""; // Your API Key
String MODEL_ENDPOINT = "dataset/v"; // model endpoint
// Upload URL
String uploadURL = "https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image="
+ URLEncoder.encode(imageURL, StandardCharsets.UTF_8);
// Http Request
HttpURLConnection connection = null;
try {
// Configure connection to URL
URL url = new URL(uploadURL);
connection = (HttpURLConnection) url.openConnection();
connection.setRequestMethod("POST");
connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded");
connection.setRequestProperty("Content-Length", Integer.toString(uploadURL.getBytes().length));
connection.setRequestProperty("Content-Language", "en-US");
connection.setUseCaches(false);
connection.setDoOutput(true);
// Send request
DataOutputStream wr = new DataOutputStream(connection.getOutputStream());
wr.writeBytes(uploadURL);
wr.close();
// Get Response
InputStream stream = new URL(uploadURL).openStream();
BufferedReader reader = new BufferedReader(new InputStreamReader(stream));
String line;
while ((line = reader.readLine()) != null) {
System.out.println(line);
}
reader.close();
} catch (Exception e) {
e.printStackTrace();
} finally {
if (connection != null) {
connection.disconnect();
}
}
}
}
Gemfile
Gemfile
source "https://rubygems.org"
gem "httparty", "~> 0.18.1"
gem "base64", "~> 0.1.0"
gem "cgi", "~> 0.2.1"

Gemfile.lock

Gemfile.lock
GEM
remote: https://rubygems.org/
specs:
base64 (0.1.0)
cgi (0.2.1)
httparty (0.18.1)
mime-types (~> 3.0)
multi_xml (>= 0.5.2)
mime-types (3.3.1)
mime-types-data (~> 3.2015)
mime-types-data (3.2021.0225)
multi_xml (0.6.0)
PLATFORMS
x64-mingw32
x86_64-linux
DEPENDENCIES
base64 (~> 0.1.0)
cgi (~> 0.2.1)
httparty (~> 0.18.1)
BUNDLED WITH
2.2.15

Inferring on a Local Image

require 'base64'
require 'httparty'
encoded = Base64.encode64(File.open("YOUR_IMAGE.jpg", "rb").read)
model_endpoint = "dataset/v" # Set model endpoint
api_key = "" # Your API KEY Here
params = "?api_key=" + api_key
+ "&name=YOUR_IMAGE.jpg"
response = HTTParty.post(
"https://outline.roboflow.com/" + model_endpoint + params,
body: encoded,
headers: {
'Content-Type' => 'application/x-www-form-urlencoded',
'charset' => 'utf-8'
})
puts response

Inferring on an Image Hosted Elsewhere via URL

require 'httparty'
require 'cgi'
model_endpoint = "dataset/v" # Set model endpoint
api_key = "" # Your API KEY Here
img_url = "https://i.imgur.com/PEEvqPN.png" # Construct the URL
img_url = CGI::escape(img_url)
params = "?api_key=" + api_key + "&image=" + img_url
response = HTTParty.post(
"https://outline.roboflow.com/" + model_endpoint + params,
headers: {
'Content-Type' => 'application/x-www-form-urlencoded',
'charset' => 'utf-8'
})
puts response
Inferring on a Local Image
<?php
// Base 64 Encode Image
$data = base64_encode(file_get_contents("YOUR_IMAGE.jpg"));
$api_key = ""; // Set API Key
$model_endpoint = "dataset/v"; // Set model endpoint (Found in Dataset URL)
// URL for Http Request
$url = "https://outline.roboflow.com/" . $model_endpoint
. "?api_key=" . $api_key
. "&name=YOUR_IMAGE.jpg";
// Setup + Send Http request
$options = array(
'http' => array (
'header' => "Content-type: application/x-www-form-urlencoded\r\n",
'method' => 'POST',
'content' => $data
));
$context = stream_context_create($options);
$result = file_get_contents($url, false, $context);
echo $result;
?>

Inferring on an Image Hosted Elsewhere via URL

<?php
$api_key = ""; // Set API Key
$model_endpoint = "dataset/v"; // Set model endpoint (Found in Dataset URL)
$img_url = "https://i.imgur.com/PEEvqPN.png";
// URL for Http Request
$url = "https://outline.roboflow.com/" . $model_endpoint
. "?api_key=" . $api_key
. "&image=" . urlencode($img_url);
// Setup + Send Http request
$options = array(
'http' => array (
'header' => "Content-type: application/x-www-form-urlencoded\r\n",
'method' => 'POST'
));
$context = stream_context_create($options);
$result = file_get_contents($url, false, $context);
echo $result;
?>
Inferring on a Local Image
package main
import (
"bufio"
"encoding/base64"
"fmt"
"io/ioutil"
"os"
"net/http"
"strings"
)
func main() {
api_key := "" // Your API Key
model_endpoint := "dataset/v" // Set model endpoint
// Open file on disk.
f, _ := os.Open("YOUR_IMAGE.jpg")
// Read entire JPG into byte slice.
reader := bufio.NewReader(f)
content, _ := ioutil.ReadAll(reader)
// Encode as base64.
data := base64.StdEncoding.EncodeToString(content)
uploadURL := "https://outline.roboflow.com/" + model_endpoint + "?api_key=" + api_key + "&name=YOUR_IMAGE.jpg"
req, _ := http.NewRequest("POST", uploadURL, strings.NewReader(data))
req.Header.Set("Accept", "application/json")
client := &http.Client{}
resp, _ := client.Do(req)
defer resp.Body.Close()
bytes, _ := ioutil.ReadAll(resp.Body)
fmt.Println(string(bytes))
}

Inferring on an Image Hosted Elsewhere via URL

package main
import (
"fmt"
"net/http"
"net/url"
"io/ioutil"
)
func main() {
api_key := "" // Your API Key
model_endpoint := "dataset/v" // Set model endpoint
img_url := "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg"
uploadURL := "https://outline.roboflow.com/" + model_endpoint + "?api_key=" + api_key + "&image=" + url.QueryEscape(img_url)
req, _ := http.NewRequest("POST", uploadURL, nil)
req.Header.Set("Accept", "application/json")
client := &http.Client{}
resp, _ := client.Do(req)
defer resp.Body.Close()
bytes, _ := ioutil.ReadAll(resp.Body)
fmt.Println(string(bytes))
}
Inferring on a Local Image
using System;
using System.IO;
using System.Net;
using System.Text;
namespace InferenceLocal
{
class InferenceLocal
{
static void Main(string[] args)
{
byte[] imageArray = System.IO.File.ReadAllBytes(@"YOUR_IMAGE.jpg");
string encoded = Convert.ToBase64String(imageArray);
byte[] data = Encoding.ASCII.GetBytes(encoded);
string API_KEY = ""; // Your API Key
string MODEL_ENDPOINT = "dataset/v"; // Set model endpoint
// Construct the URL
string uploadURL =
"https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY
+ "&name=YOUR_IMAGE.jpg";
// Service Request Config
ServicePointManager.Expect100Continue = true;
ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12;
// Configure Request
WebRequest request = WebRequest.Create(uploadURL);
request.Method = "POST";
request.ContentType = "application/x-www-form-urlencoded";
request.ContentLength = data.Length;
// Write Data
using (Stream stream = request.GetRequestStream())
{
stream.Write(data, 0, data.Length);
}
// Get Response
string responseContent = null;
using (WebResponse response = request.GetResponse())
{
using (Stream stream = response.GetResponseStream())
{
using (StreamReader sr99 = new StreamReader(stream))
{
responseContent = sr99.ReadToEnd();
}
}
}
Console.WriteLine(responseContent);
}
}
}

Inferring on an Image Hosted Elsewhere via URL

using System;
using System.IO;
using System.Net;
using System.Web;
namespace InferenceHosted
{
class InferenceHosted
{
static void Main(string[] args)
{
string API_KEY = ""; // Your API Key
string imageURL = "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg";
string MODEL_ENDPOINT = "dataset/v"; // Set model endpoint
// Construct the URL
string uploadURL =
"https://outline.roboflow.com/" + MODEL_ENDPOINT
+ "?api_key=" + API_KEY
+ "&image=" + HttpUtility.UrlEncode(imageURL);
// Service Point Config
ServicePointManager.Expect100Continue = true;
ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12;
// Configure Http Request
WebRequest request = WebRequest.Create(uploadURL);
request.Method = "POST";
request.ContentType = "application/x-www-form-urlencoded";
request.ContentLength = 0;
// Get Response
string responseContent = null;
using (WebResponse response = request.GetResponse())
{
using (Stream stream = response.GetResponseStream())
{
using (StreamReader sr99 = new StreamReader(stream))
{
responseContent = sr99.ReadToEnd();
}
}
}
Console.WriteLine(responseContent);
}
}
}
We are adding code snippets as they are requested by users. If you'd like to integrate the inference API into your Elixir app, please click here to record your upvote.

Video Inference

To get predictions from your model on a video, split it into frames, perform inference on each frame, then composite the predictions back into a rendered video.
We have an open source video inference utility script that performs these steps with ffmpeg and the Roboflow Inference API: