> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/deployment/legacy/legacy-serverless/instance-segmentation/semantic-segmentation.md). # Semantic Segmentation Run inference on semantic segmentation models hosted on Roboflow. {% tabs %} {% tab title="cURL" %} **Linux or MacOS** Retrieving JSON predictions for a local file called `YOUR_IMAGE.jpg`: ```bash base64 YOUR_IMAGE.jpg | curl -d @- \ -H "Authorization: Bearer YOUR_KEY" \ "https://segment.roboflow.com/your-model/42" ``` Inferring on an image hosted elsewhere on the web via its URL (don't forget to [URL encode it](https://www.urlencoder.org/)): ```bash curl -X POST \ -H "Authorization: Bearer YOUR_KEY" \ "https://segment.roboflow.com/your-model/42?\ image=https%3A%2F%2Fi.imgur.com%2FPEEvqPN.png" ``` **Windows** You will need to install [curl for Windows](https://curl.se/windows/) and [GNU's base64 tool for Windows](http://gnuwin32.sourceforge.net/packages/coreutils.htm). The easiest way to do this is to use the [git for Windows installer](https://git-scm.com/downloads) 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. {% endtab %} {% tab title="Python" %} **Infer on Local and Hosted Images** To install dependencies, `pip install roboflow`. ```python from roboflow import Roboflow rf = Roboflow(api_key="") project = rf.workspace().project("PROJECT_ID") model = project.version(VERSION).models()[0] prediction = model.predict("YOUR_IMAGE.jpg") ``` {% endtab %} {% tab title="Javascript" %} **Node.js** These examples use the built-in `fetch` API. It works in Node.js 18 and later and in modern browsers, so there is no dependency to install. **Inferring on a Local Image** ```javascript const fs = require("fs"); const image = fs.readFileSync("YOUR_IMAGE.jpg", { encoding: "base64" }); fetch("https://segment.roboflow.com/your-model/42", { method: "POST", headers: { "Authorization": "Bearer YOUR_KEY", "Content-Type": "application/x-www-form-urlencoded" }, body: image }) .then((response) => { if (!response.ok) throw new Error("Request failed with status " + response.status); return response.json(); }) .then((data) => { console.log(data); }) .catch((error) => { console.log(error.message); }); ``` **Inferring on an Image Hosted Elsewhere via URL** ```javascript fetch( "https://segment.roboflow.com/your-model/42?image=" + encodeURIComponent("https://i.imgur.com/PEEvqPN.png"), { method: "POST", headers: { "Authorization": "Bearer YOUR_KEY" } } ) .then((response) => { if (!response.ok) throw new Error("Request failed with status " + response.status); return response.json(); }) .then((data) => { console.log(data); }) .catch((error) => { console.log(error.message); }); ``` **Web** We have realtime on-device inference available via `roboflow.js`; see [the documentation here](/deployment/self-hosted/sdks/web-browser.md). {% endtab %} {% tab title="Swift/iOS" %} **Swift** **Inferring on a Local Image** ```swift 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://segment.roboflow.com/your-model/your-model-version?name=YOUR_IMAGE.jpg")!,timeoutInterval: Double.infinity) request.setValue("Bearer YOUR_APIKEY", forHTTPHeaderField: "Authorization") 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** [Click here to request an Objective-C snippet.](https://app.roboflow.com/request/snippet.inference-objc) {% endtab %} {% tab title="Android" %} **Kotlin** **Inferring on a Local Image** ```kotlin 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://segment.roboflow.com/" + MODEL_ENDPOINT + "?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("Authorization", "Bearer " + API_KEY) 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** ```kotlin 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://segment.roboflow.com/" + MODEL_ENDPOINT + "?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("Authorization", "Bearer " + API_KEY) 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 = 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() ``` **Java** **Inferring on a Local Image** ```java 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://segment.roboflow.com/" + MODEL_ENDPOINT + "?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("Authorization", "Bearer " + API_KEY); 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** ```java 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://segment.roboflow.com/" + MODEL_ENDPOINT + "?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("Authorization", "Bearer " + API_KEY); 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 = 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(); } } } } ``` {% endtab %} {% tab title="Ruby" %} **Gemfile** {% code title="Gemfile" %} ```ruby source "https://rubygems.org" gem "httparty", "~> 0.18.1" gem "base64", "~> 0.1.0" gem "cgi", "~> 0.2.1" ``` {% endcode %} **Gemfile.lock** {% code title="Gemfile.lock" %} ```ruby 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 ``` {% endcode %} **Inferring on a Local Image** ```ruby 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 = "?name=YOUR_IMAGE.jpg" response = HTTParty.post( "https://segment.roboflow.com/" + model_endpoint + params, body: encoded, headers: { 'Authorization' => "Bearer " + api_key, 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` **Inferring on an Image Hosted Elsewhere via URL** ```ruby 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 = "?image=" + img_url response = HTTParty.post( "https://segment.roboflow.com/" + model_endpoint + params, headers: { 'Authorization' => "Bearer " + api_key, 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` {% endtab %} {% tab title="PHP" %} **Inferring on a Local Image** ```php array ( 'header' => "Content-type: application/x-www-form-urlencoded\r\n" . "Authorization: Bearer " . $api_key . "\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 array ( 'header' => "Content-type: application/x-www-form-urlencoded\r\n" . "Authorization: Bearer " . $api_key . "\r\n", 'method' => 'POST' )); $context = stream_context_create($options); $result = file_get_contents($url, false, $context); echo $result; ?> ``` {% endtab %} {% tab title="Go" %} **Inferring on a Local Image** ```go 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://segment.roboflow.com/" + model_endpoint + "?name=YOUR_IMAGE.jpg" req, _ := http.NewRequest("POST", uploadURL, strings.NewReader(data)) req.Header.Set("Authorization", "Bearer "+api_key) 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** ```go 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://segment.roboflow.com/" + model_endpoint + "?image=" + url.QueryEscape(img_url) req, _ := http.NewRequest("POST", uploadURL, nil) req.Header.Set("Authorization", "Bearer "+api_key) 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)) } ``` {% endtab %} {% tab title=".NET" %} **Inferring on a Local Image** ```csharp 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://segment.roboflow.com/" + MODEL_ENDPOINT + "?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.Headers.Add("Authorization", "Bearer " + API_KEY); 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** ```csharp 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://segment.roboflow.com/" + MODEL_ENDPOINT + "?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.Headers.Add("Authorization", "Bearer " + API_KEY); 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); } } } ``` {% endtab %} {% tab title="Elixer" %} 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](https://app.roboflow.com/request/snippet.upload-elixir). {% endtab %} {% endtabs %} ### Response Object Format The hosted API inference route returns a `JSON` object containing an array of predictions. Each prediction has the following properties: * `segmentation_mask` = base64 encoded single channel image with dimensions equal to the input image where each pixel value corresponds to a class ID * `class_map` = object that maps class IDs to class names * `image` = an object with the input image dimensions * height = height of the input image in number of pixels * width = width of the input image in number of pixels ```json // an example JSON object { "segmentation_mask": "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", "class_map": { "0": " background", "1": " object" }, "image": { "width": 1232, "height": 821 } } ``` ## API Reference ## Using the Inference API `POST` `https://segment.roboflow.com/:datasetSlug/:versionNumber` You can POST a base64 encoded image directly to your model endpoint. Or you can pass a URL as the `image` parameter in the query string if your image is already hosted elsewhere. #### Path Parameters | Name | Type | Description | | ----------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | datasetSlug | string | The url-safe version of the dataset name. You can find it in the web UI by looking at the URL on the main project view or by clicking the "Get curl command" button in the train results section of your dataset version after training your model. | | version | number | The version number identifying the version of of your dataset | #### Query Parameters | Name | Type | Description | | ---------- | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | image | string |

URL of the image to add. Use if your image is hosted elsewhere. (Required when you don't POST a base64 encoded image in the request body.)

Note: don't forget to URL-encode it.

| | confidence | number |

A threshold for the returned predictions on a scale of 0-100. A lower number will return more predictions. A higher number will return fewer high-certainty predictions.

Default: 50

| | api\_key | string | Your API key (obtained via your workspace API settings page) | #### Request Body | Name | Type | Description | | ---- | ------ | -------------------------------------------------------------------------------------------- | | | string | A base64 encoded image. (Required when you don't pass an image URL in the query parameters). | {% tabs %} {% tab title="200 JSON format predictions. (x,y) are the box" %} ``` { "segmentation_mask": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAAAAADRE4smAAAIqElEQVR4nO3dyXbb2BJFQfCt+v9f5htYstWwQXNB3pMZMaiaeMkAcjMBSpa0LAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACRLu8+gJf4cZbX9xzFlBoEcPMUNfChfgB3z1ADy1I/gEfnp4ClegDPzk4CpQNYc27tE/jfuw/gPKvarvwCWKXuBVh9Zr2XQNkNsL7suq+BNYqe/bbT6rwDam6AjVkXfRWsUjKAzQNtXEDJALbrW0DFAPZMs20BBQNoO8td6gWwc/5dsykXwO5BNi2gXAD79SxAAP+0LKBaAC2HeESxAMx/q1oBHJx/x3xqBXBUwwJKBXB8fv0KKBUA21UKYMTLt90KqBQAOxQKoN2Ld4g6AQyaf7eM6gTALgL4qdkKKBPAuLn1KqBMAOwjgOaqBDByb7e6B1QJgJ0E0FyRAFpt7aGKBDBWp5wE0FyNADq9ZAerEcBojYISwE19CigRQJ9xjVciAPYTQHMVAnAHOKBCABwggOYE0JwAmhPAbW0eLAXQnACaKxBAm219igIBcIQAmhNAcwJoTgB3dHm0zA+gy6ROkh8AhwigOQE0J4DmBNBcfADeBBwTH8BpmpQlgOYE0JwAmhPAU7UfBv579wHM7rIsl8q/YN4GeOzy7X8F2QD31Z36F/Eb4Lzl3GL+NsA+X+IIfzzIz/wlZ/Blyrf+vuAIBLDG54Af/F2pDcQ/A7zEn8FfHrWW+kryDHDX9etMn8/3krkE8jfAWZf928dd9fqOXAI2wECJnzLM3wAcIoCx4m4DAhgsrQABjPbwzeJ8so72pvlOIelJ0AZorkAA873e5ttJ9xUIYEJBBQjgFDkFCKA5AZwjZgUIoLkKAcz3NmDJWQEVAuAAAZwlZAUI4DQZBZQIYMqHgBAlAphUxAoQQHMCaK5GAJM+BCTcA2oEMGsBAYoEwF4COFPAPaBKAO4BO1UJYFLzrwABNFcmAPeAfcoEwD4CaK5OAO4Bu9QJYM4Cpn8bUCgA9qgUwJQrYHaVAmCHUgFYAduVCoDtBHCy2d8G1ArAPWCzWgEoYLNiAbBVtQCsgI2qBTCfyZ8CJz+8tS7L3xf/dGc091KqsQEuf//DRhWu2r9zuM54PnNvgAkv2EbTn8HcAcTfAqaf/+TCf2GE8R+VvQHM/7DgDWD6I+RugJHzv879oHam2ADGvv77bpPUAMx/kMxTTzrqye8ukRtg7Pwnn9DJEgMY/PpPWifjBQbQe2Cj5QVg/kPFBWD+Y8UFwFhpAcQtgNnfY4QFEDf/6WV9MWjv/K/f/tXQ0Y9WStgGOM78v4u6CgMO9u/8X3XingHmZP4fkgIYuQD4kBQAJ2gWwOXH/wm6EoMO9bXfPDL9PScngJwj/Wr6AJrdAvgpJoDMBTC/mAA4hwBONf0jQEwA7gAnSQkg0/wLQADdhQTgDnCWkAAyBdwBBNBdRgDuAKfJCCBTwh1AAN1FBOAOcJ6IADJF3AEE0J0AzpKxAATQnQCaE8AvY35oYMgdICKA174LvPb6uZEJAbxUo9kvyyKAn/7M//jOiemoZQD3V3zM3IZpGcDdQQ+bf05IHQO4N51/i+Ho/HLm3zKAZbk1om/3hWMTDJp/2M8IGu26LMtyiRrYaAlfaR19jJ9P+g/nfuAvjeqp6y2AD30DePLpvv0v46gF0DKAc297WfNvGcBjn3nsHGTY/AXwy7BPBmQQwH17CoirpnEACe+Az9c3gMtyeZZA3Mt5h74BnCIvmb4BrJnV1nnmzb9xAMv4eSX+KPrGAaz5l3+bEkmcf8+vBl5OWdWfHzRo+kvrDbDGhmFmzl8Ao4TOv+ctYLjft5Rz7jInsAEG+DLstAVgAzy0bpzXLX94NjbAM0/fLd6ef0oOEcc5/CDX3qAvz75V6NFb/4ynABvgsY8p3lkDkZ/6+S7iwN+2Ab49zP86imfv/CJWgA3w0INvFrmmvvP/TgDr3X5FZ8/f28D1Qh/zn8g4jcFHuePmvOcIIp4BbIDHjqQX8elgzwAPZSzIIwTwSP35hwTwpl3aYP4hAbyngA7zTwngHVrMXwD3Hd86CQmlBPCOe0DCu7jDUgIYafVgOxTQMQC+iAngLa/GBisgJoD3OFhAwFNgTgANXo3vkBMApwgKwAo4Q1AAnCEpACvgBEkBKOAEUQGMsfOHPhSVFcCIcZQf6TZZAZjecGEB7HD9/m1dDX7w1yZpATT6bT6vkRZAo9/m8xpxARyaofn/kheAKQ4VGMDmAv5+UVY6vyUGsHGQf38FiPnfUP97Az/mb/q3RW6AHdM0/zsyAzgyz6e/JuLHH9//N0UIDWBrAbuDqT7/2ADs9EFiA9j0UP/9j254VZdfAMEB7F0C1xcuj4A1lRzA7gL4JzoAszwuO4DJC5j76P4ID2DlNa7/LLdbegBrC5DAHSUuzKbf67DxI1/3X6OEO0D+BliW07/OV/rLiCUCOHFG1y//LalIAGfNqPDkP5R4Bvj05GSOTLPsD4suswGWpfjN+iSlAphLRo0CWGXHHSBj/gLorlMA+x946y6AagGkXPZ5FAvgHIUXQLkAzrjwpT5X8lO1AE5Qev71Aphj985xFGuUC2CKaz/DMaxUL4DhVz9omjsUDOD9kpKpGMC96/+quSTNv2QAdyYQNZeXKRnATS+bf1ZoNQO4MYOssbxOzQDGjnvbDxcOK61oAL+Gdmgsu78POUDVAMaKG+t6ZQN4z8zySqn7lY5vZ/aa3wOcN/7CG2D0NFZ8uMT5F94Ay+gfEfrsUkXOv3YAn6c3aDSPr1Xm/KsHMNj9qxU6/srPAGe4O+bY+dsAW926YLnjF8AOPy9Z8vgFsNffn0H/1qMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACARv4PAgMMHbaPKVQAAAAASUVORK5CYII=", "class_map": { "0": " background", "1": " object" }, "image": { "width": 1232, "height": 821 } } ``` {% endtab %} {% tab title="403 If your api\_key is not authorized to access the model." %} ``` { "Message": "User is not authorized to access this resource" } ``` {% endtab %} {% endtabs %}