YOLO11 object detection runs through the Serverless Cloud API, pretrained on COCO at 640 input size. For self-hosted deployment, see Roboflow Inference.

YOLO11 Object Detection API

This sample runs inference through the Serverless Cloud API, decodes the response with supervision, and writes an annotated image to disk.

1

Get your API Key

Create a Roboflow account, find your key on the Roboflow API settings page and make it available to your shell:

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export ROBOFLOW_API_KEY="your-key-here"

2

Install the dependencies

These two packages call the model and draw its results:

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pip install -U inference-sdk supervision

3

Run the model

Run yolov11n-640 on a sample image and annotate boxes and labels:

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import os
import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://media.roboflow.com/quickstart/traffic.jpg"
image = sv.load_image_from_url(image_url)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)

results = client.infer(image, model_id="yolov11n-640")
detections = sv.Detections.from_inference(results)

annotated = sv.BoxAnnotator().annotate(image.copy(), detections)
annotated = sv.LabelAnnotator().annotate(annotated, detections)

cv2.imwrite("traffic-annotated.png", annotated)

YOLO11 Object Detection models and benchmarks

Pass any of these aliases as the model_id. The inference-sdk resolves each alias to a pretrained Roboflow Universe model; the yolo11* prefix variants resolve to the same models.

YOLO11 Instance Segmentation

YOLO11 instance segmentation runs through the Serverless Cloud API, pretrained on COCO at 640 input size. For self-hosted deployment, see Roboflow Inference.

YOLO11 Instance Segmentation API

Set your API key and install the dependencies as shown above, then run yolov11n-seg-640 and annotate masks and labels:

import os
import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://media.roboflow.com/quickstart/traffic.jpg"
image = sv.load_image_from_url(image_url)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)

results = client.infer(image, model_id="yolov11n-seg-640")
detections = sv.Detections.from_inference(results)

annotated = sv.MaskAnnotator().annotate(image.copy(), detections)
annotated = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS).annotate(annotated, detections)

cv2.imwrite("traffic-annotated.png", annotated)

YOLO11 Instance Segmentation models and benchmarks

Pass any of these aliases as the model_id; the yolo11* prefix variants resolve to the same models.

* Latency is measured with Roboflow Inference on 1x NVIDIA L4, batch size 1, mean of 1,000 inferences (100 warmup). The default inference-gpu install runs ONNX on the CUDA execution provider; adding the inference-models[trt10] extra selects a prebuilt TensorRT FP16 engine automatically. FP16 matches FP32 accuracy within 0.1 mAP on COCO val2017. Accuracy is the published COCO val2017 spec (source).