> 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/roboflow/roboflow-hi/deploy/supported-models/yolo26.md). # YOLO26 हमारे माध्यम से YOLO26 inferencing का समर्थन करते हैं [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md). YOLO26 चार task variants में उपलब्ध है: * Object detection (COCO pretrained) * Instance segmentation (COCO pretrained) * Keypoint/pose detection (COCO pretrained) * Semantic segmentation (Cityscapes pretrained) self-hosted deployment के लिए, देखें [Roboflow Inference](https://inference.roboflow.com/). ## कोड उदाहरण SDK और [supervision](https://supervision.roboflow.com/) annotation के लिए library इंस्टॉल करें: ```bash pip install inference-sdk supervision opencv-python ``` अपना [Roboflow API Key](https://app.roboflow.com/settings/api) के माध्यम से `API_KEY` environment variable. हर sample एक test image डाउनलोड करता है, inference चलाता है `inference-sdk`के माध्यम से, response को डिकोड करता है `supervision`और disk पर एक annotated PNG लिखता है। ### Object detection ```python import os import urllib.request import cv2 import supervision as sv from inference_sdk import InferenceHTTPClient image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png" image_path = "cars-highway.png" urllib.request.urlretrieve(image_url, image_path) client = InferenceHTTPClient( api_url="https://serverless.roboflow.com", api_key=os.getenv("API_KEY"), ) result = client.infer(image_path, model_id="yolo26n-640") detections = sv.Detections.from_inference(result) image = cv2.imread(image_path) labels = [ f"{cls} {conf:.2f}" for cls, conf in zip(detections.data["class_name"], detections.confidence) ] annotated = sv.BoxAnnotator().annotate(scene=image.copy(), detections=detections) annotated = sv.LabelAnnotator().annotate( scene=annotated, detections=detections, labels=labels ) cv2.imwrite("cars-highway-annotated.png", annotated) ```
### Instance segmentation ```python import os import urllib.request import cv2 import supervision as sv from inference_sdk import InferenceHTTPClient image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/bicycle.png" image_path = "bicycle.png" urllib.request.urlretrieve(image_url, image_path) client = InferenceHTTPClient( api_url="https://serverless.roboflow.com", api_key=os.getenv("API_KEY"), ) result = client.infer(image_path, model_id="yolo26n-seg-640") detections = sv.Detections.from_inference(result) image = cv2.imread(image_path) labels = [ f"{cls} {conf:.2f}" for cls, conf in zip(detections.data["class_name"], detections.confidence) ] annotated = sv.MaskAnnotator().annotate(scene=image.copy(), detections=detections) annotated = sv.BoxAnnotator().annotate(scene=annotated, detections=detections) annotated = sv.LabelAnnotator().annotate( scene=annotated, detections=detections, labels=labels ) cv2.imwrite("bicycle-annotated.png", annotated) ```
### Keypoint detection ```python import os import urllib.request import cv2 import supervision as sv from inference_sdk import InferenceHTTPClient image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/person-walking.png" image_path = "person-walking.png" urllib.request.urlretrieve(image_url, image_path) client = InferenceHTTPClient( api_url="https://serverless.roboflow.com", api_key=os.getenv("API_KEY"), ) result = client.infer(image_path, model_id="yolo26n-pose-640") key_points = sv.KeyPoints.from_inference(result) image = cv2.imread(image_path) annotated = sv.VertexAnnotator(color=sv.Color.RED, radius=5).annotate( scene=image.copy(), key_points=key_points ) cv2.imwrite("person-walking-annotated.png", annotated) ```
### Semantic segmentation YOLO26 semantic segmentation (`yolo26-sem`) Roboflow पर semantic segmentation के लिए अनुशंसित architecture है। यह Cityscapes pretrained weights का उपयोग करता है और 1024x1024 के default resolution पर train करता है। पाँच sizes में उपलब्ध: n, s, m, l, x. YOLO26-SEM model train करने के लिए, एक semantic segmentation Project बनाएं और अपनी architecture के रूप में YOLO26 चुनें। आप यह भी [custom-trained weights अपलोड कर सकते हैं](/roboflow/roboflow-hi/deploy/upload-custom-weights.md) YOLO26-SEM models के लिए। Cityscapes pretrained models (19 classes) public models के रूप में भी उपलब्ध हैं जिन्हें आप एक [Workflow](/roboflow/roboflow-hi/workflows/what-is-workflows.md) में बिना training के चला सकते हैं। ## डिफ़ॉल्ट COCO aliases निम्नलिखित में से कोई भी alias पास करें `model_id` जब SDK को कॉल करें। `inference-sdk` हर alias को client-side पर उसके pretrained Roboflow Universe model में resolve करता है। | Task | Alias | | ------------ | ------------------ | | Detection | `yolo26n-640` | | Detection | `yolo26s-640` | | Detection | `yolo26m-640` | | Detection | `yolo26l-640` | | Detection | `yolo26x-640` | | Segmentation | `yolo26n-seg-640` | | Segmentation | `yolo26s-seg-640` | | Segmentation | `yolo26m-seg-640` | | Segmentation | `yolo26l-seg-640` | | Segmentation | `yolo26x-seg-640` | | Pose | `yolo26n-pose-640` | | Pose | `yolo26s-pose-640` | | Pose | `yolo26m-pose-640` | | Pose | `yolo26l-pose-640` | | Pose | `yolo26x-pose-640` | The `yolov26*` prefix variants same models पर resolve होते हैं। {% hint style="info" %} सेट करें `api_url` को अपने deployment target से मिलाने के लिए: * `https://serverless.roboflow.com` Serverless Hosted API के लिए। * `http://localhost:9001` एक स्थानीय [Inference](https://inference.roboflow.com/) सर्वर। * आपका [Dedicated Deployment](/roboflow/roboflow-hi/deploy/dedicated-deployments.md) एक private endpoint के लिए URL। {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://docs.roboflow.com/roboflow/roboflow-hi/deploy/supported-models/yolo26.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.