# Semantic Segmentation {% tabs %} {% tab title="cURL" %} **Linux या MacOS** नाम वाली local file के लिए JSON predictions प्राप्त करना `YOUR_IMAGE.jpg`: ```bash base64 YOUR_IMAGE.jpg | curl -d @- \ "https://segment.roboflow.com/your-model/42?api_key=YOUR_KEY" ``` web पर कहीं और hosted image पर उसके URL के माध्यम से inference चलाना (यह न भूलें कि [इसे URL encode करें](https://www.urlencoder.org/)): ```bash curl -X POST "https://segment.roboflow.com/your-model/42?\ api_key=YOUR_KEY&\ image=https%3A%2F%2Fi.imgur.com%2FPEEvqPN.png" ``` **Windows** आपको install करना होगा [Windows के लिए curl](https://curl.se/windows/) और [Windows के लिए GNU का base64 tool](http://gnuwin32.sourceforge.net/packages/coreutils.htm)। ऐसा करने का सबसे आसान तरीका है [Windows के लिए git installer](https://git-scm.com/downloads) जिसमें यह भी शामिल है `curl` और `base64` command line tools, जब आप installation के दौरान "Use Git and optional Unix tools from the Command Prompt" चुनते हैं। फिर आप ऊपर वाले वही commands उपयोग कर सकते हैं। {% endtab %} {% tab title="Python" %} **लोकल और होस्टेड इमेज पर Infer करें** Dependencies install करने के लिए, `pip install roboflow`. ``` import roboflow rf = roboflow.Roboflow(api_key="") project = rf.workspace().project("PROJECT_ID") model = project.version("VERSION").model prediction = model.predict("YOUR_IMAGE.jpg") ``` {% endtab %} {% tab title="Javascript" %} **Node.js** हम उपयोग कर रहे हैं [axios](https://github.com/axios/axios) इस example में POST request perform करने के लिए, इसलिए पहले चलाएँ `npm install axios` dependency install करने के लिए। **Local Image पर inference चलाना** ```javascript const axios = require("axios"); const fs = require("fs"); const image = fs.readFileSync("YOUR_IMAGE.jpg", { encoding: "base64" }); axios({ method: "POST", url: "https://segment.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); }); ``` **URL के माध्यम से कहीं और hosted Image पर inference चलाना** ```javascript const axios = require("axios"); axios({ method: "POST", url: "https://segment.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** हमारे पास realtime on-device inference उपलब्ध है `roboflow.js`के माध्यम से; देखें [दस्तावेज़ीकरण यहाँ](/roboflow/roboflow-hi/deploy/sdks/web-browser.md). {% endtab %} {% tab title="Swift/iOS" %} **Swift** **Local Image पर inference चलाना** ```swift import UIKit // Image लोड करें और Base64 में बदलें let image = UIImage(named: "your-image-path") // upload करने के लिए image का path, उदाहरण: image.jpg let imageData = image?.jpegData(compressionQuality: 1) let fileContent = imageData?.base64EncodedString() let postData = fileContent!.data(using: .utf8) // API_KEY, Model, और Model Version के साथ Inference Server Request initialize करें var request = URLRequest(url: URL(string: "https://segment.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 // Post Request execute करें URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in // Response को String में parse करें guard let data = data else { print(String(describing: error)) return } // Response String को Dictionary में convert करें do { let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any] } catch { print(error.localizedDescription) } // String Response प्रिंट करें print(String(data: data, encoding: .utf8)!) }).resume() ``` **Objective C** [Objective-C snippet अनुरोध करने के लिए यहाँ क्लिक करें।](https://app.roboflow.com/request/snippet.inference-objc) {% endtab %} {% tab title="Android" %} **Kotlin** **Local Image पर inference चलाना** ```kotlin import java.io.* import java.net.HttpURLConnection import java.net.URL import java.nio.charset.StandardCharsets import java.util.* fun main() { // Image Path प्राप्त करें 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 = "" // आपका API Key val MODEL_ENDPOINT = "dataset/v" // model endpoint सेट करें (Dataset URL में मिलेगा) // URL बनाएं val uploadURL ="https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http Request var connection: HttpURLConnection? = null try { // URL से connection configure करें 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 // अनुरोध भेजें val wr = DataOutputStream( connection.outputStream) wr.writeBytes(encodedFile) wr.close() // Response प्राप्त करें 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() ``` **URL के माध्यम से कहीं और hosted Image पर inference चलाना** ```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" // Image URL बदलें val API_KEY = "" // आपका API Key val MODEL_ENDPOINT = "dataset/v" // model endpoint सेट करें // Upload URL val uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, "utf-8"); // Http Request var connection: HttpURLConnection? = null try { // URL से connection configure करें 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 // अनुरोध भेजें val wr = DataOutputStream(connection.outputStream) wr.writeBytes(uploadURL) wr.close() // Response प्राप्त करें 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** **Local Image पर inference चलाना** ```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 { // Image Path प्राप्त करें 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 = ""; // आपका API Key String MODEL_ENDPOINT = "dataset/v"; // model endpoint // URL बनाएं String uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http Request HttpURLConnection connection = null; try { // URL से connection configure करें 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); // अनुरोध भेजें DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(encodedFile); wr.close(); // Response प्राप्त करें 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(); } } } } ``` **URL के माध्यम से कहीं और hosted Image पर inference चलाना** ```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"; // Image URL बदलें String API_KEY = ""; // आपका API Key String MODEL_ENDPOINT = "dataset/v"; // model endpoint // Upload URL String uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, StandardCharsets.UTF_8); // Http Request HttpURLConnection connection = null; try { // URL से connection configure करें 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); // अनुरोध भेजें DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(uploadURL); wr.close(); // Response प्राप्त करें 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(); } } } } ``` {% 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 %} **Local Image पर inference चलाना** ```ruby require 'base64' require 'httparty' encoded = Base64.encode64(File.open("YOUR_IMAGE.jpg", "rb").read) model_endpoint = "dataset/v" # model endpoint सेट करें api_key = "" # आपका API KEY यहाँ params = "?api_key=" + api_key + "&name=YOUR_IMAGE.jpg" response = HTTParty.post( "https://segment.roboflow.com/" + model_endpoint + params, body: encoded, headers: { 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` **URL के माध्यम से कहीं और hosted Image पर inference चलाना** ```ruby require 'httparty' require 'cgi' model_endpoint = "dataset/v" # model endpoint सेट करें api_key = "" # आपका API KEY यहाँ img_url = "https://i.imgur.com/PEEvqPN.png" # URL बनाएं img_url = CGI::escape(img_url) params = "?api_key=" + api_key + "&image=" + img_url response = HTTParty.post( "https://segment.roboflow.com/" + model_endpoint + params, headers: { 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` {% endtab %} {% tab title="PHP" %} **Local Image पर inference चलाना** ```php 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; ?> ``` **URL के माध्यम से कहीं और hosted Image पर inference चलाना** ```php 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; ?> ``` {% endtab %} {% tab title="Go" %} **Local Image पर inference चलाना** ```go package main import ( "bufio" "encoding/base64" "fmt" "io/ioutil" "os" "net/http" "strings" ) func main() { api_key := "" // आपका API Key model_endpoint := "dataset/v" // model endpoint सेट करें // Disk पर file खोलें। f, _ := os.Open("YOUR_IMAGE.jpg") // पूरी JPG को byte slice में पढ़ें। reader := bufio.NewReader(f) content, _ := ioutil.ReadAll(reader) // Base64 के रूप में encode करें। data := base64.StdEncoding.EncodeToString(content) uploadURL := "https://segment.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)) } ``` **URL के माध्यम से कहीं और hosted Image पर inference चलाना** ```go package main import ( "fmt" "net/http" "net/url" "io/ioutil" ) func main() { api_key := "" // आपका API Key model_endpoint := "dataset/v" // model endpoint सेट करें img_url := "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg" uploadURL := "https://segment.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)) } ``` {% endtab %} {% tab title=".NET" %} **Local Image पर inference चलाना** ```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 = ""; // आपका API Key string MODEL_ENDPOINT = "dataset/v"; // model endpoint सेट करें // URL बनाएं string uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Service Request Config ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // Request configure करें WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = data.Length; // Data लिखें using (Stream stream = request.GetRequestStream()) { stream.Write(data, 0, data.Length); } // Response प्राप्त करें 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); } } } ``` **URL के माध्यम से कहीं और hosted Image पर inference चलाना** ```csharp using System; using System.IO; using System.Net; using System.Web; namespace InferenceHosted { class InferenceHosted { static void Main(string[] args) { string API_KEY = ""; // आपका API Key string imageURL = "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg"; string MODEL_ENDPOINT = "dataset/v"; // model endpoint सेट करें // URL बनाएं string uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + HttpUtility.UrlEncode(imageURL); // Service Point Config ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // Http Request configure करें WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = 0; // Response प्राप्त करें 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" %} हम उपयोगकर्ताओं के अनुरोध के अनुसार कोड स्निपेट जोड़ रहे हैं। यदि आप inference API को अपने Elixir app में integrate करना चाहते हैं, तो कृपया [अपवोट दर्ज करने के लिए यहाँ क्लिक करें](https://app.roboflow.com/request/snippet.upload-elixir). {% endtab %} {% endtabs %} ### Response Object Format hosted API inference route एक `JSON` object जिसमें predictions की एक array होती है। प्रत्येक prediction में निम्न properties होती हैं: * `segmentation_mask` = base64 encoded single channel image, जिसके dimensions input image के बराबर हैं, जहाँ हर pixel value एक class ID से मेल खाती है * `class_map` = एक object जो class IDs को class names से map करता है * `image` = input image dimensions वाला एक object * height = input image की height, pixels की संख्या में * width = input image की width, pixels की संख्या में ```json // एक उदाहरण JSON object { "segmentation_mask": "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", "class_map": { "0": " background", "1": " object" }, "image": { "width": 1232, "height": 821 } } ``` ## API Reference ## Inference API का उपयोग करना `POST` `https://segment.roboflow.com/:datasetSlug/:versionNumber` आप एक base64 encoded image सीधे अपने model endpoint पर POST कर सकते हैं। या आप query string में `image` parameter पास कर सकते हैं यदि आपकी image पहले से कहीं और hosted है। #### Path Parameters | Name | Type | Description | | ----------- | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | datasetSlug | string | dataset नाम का URL-safe version। आप इसे web UI में मुख्य project view पर URL देखकर, या model train करने के बाद अपने dataset version के train results section में "Get curl command" बटन पर क्लिक करके पा सकते हैं। | | version | number | आपके dataset के version की पहचान करने वाला version number | #### Query Parameters | Name | Type | Description | | ---------- | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | image | string |

जो image जोड़नी है उसका URL। इसका उपयोग करें यदि आपकी image कहीं और hosted है। (जब आप request body में base64 encoded image POST नहीं करते हैं, तब आवश्यक है.)

नोट: इसे URL-encode करना न भूलें।

| | confidence | number |

returned predictions के लिए 0-100 scale पर एक threshold। कम संख्या अधिक predictions लौटाएगी। अधिक संख्या कम, अधिक-विश्वास वाली predictions लौटाएगी।

डिफ़ॉल्ट: 50

| | api\_key | string | आपकी API key (आपके workspace API settings page से प्राप्त) | #### रिक्वेस्ट बॉडी | Name | Type | Description | | ---- | ------ | ------------------------------------------------------------------------------------------------ | | | string | एक base64 encoded image. (जब आप query parameters में image URL पास नहीं करते हैं, तब आवश्यक है). | {% 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 यदि आपका api\_key model तक पहुँचने के लिए authorized नहीं है।" %} ``` { "Message": "User is not authorized to access this resource" } ``` {% endtab %} {% endtabs %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.roboflow.com/roboflow/roboflow-hi/deploy/serverless/instance-segmentation/semantic-segmentation.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.