YOLO26
YOLO26 Object Detection
YOLO26 object detection runs through the Serverless Cloud API, pretrained on COCO. For self-hosted deployment, see Roboflow Inference.
YOLO26 Object Detection API
This sample downloads a test image, runs inference through inference-sdk, decodes the response with supervision, and writes an annotated PNG to disk.
Get your API Key
Create a Roboflow account, find your key on the Roboflow API settings page and make it available to your shell:
export ROBOFLOW_API_KEY="your-key-here"Install the dependencies
Install the SDK and the supervision library for annotation:
pip install -U inference-sdk supervision opencv-pythonRun the model
Run yolo26n-640 on a sample image and annotate boxes and labels:
import os
import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient, InferenceConfiguration
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"],
).configure(InferenceConfiguration(api_key_transport="header"))
result = client.infer(image, model_id="yolo26n-640")
detections = sv.Detections.from_inference(result)
annotated = sv.BoxAnnotator().annotate(image.copy(), detections)
annotated = sv.LabelAnnotator().annotate(annotated, detections)
cv2.imwrite("traffic-annotated.png", annotated)
YOLO26 Object Detection models and benchmarks
Pass any of these aliases as the model_id. The inference-sdk resolves each alias to its pretrained Roboflow Universe model client-side; the yolov26* prefix variants resolve to the same models.
yolo26n-640
640x640
40.9
3.5
2.2
yolo26s-640
640x640
48.6
4.7
3.0
yolo26m-640
640x640
53.1
8.4
4.4
yolo26l-640
640x640
55.0
10.7
5.6
yolo26x-640
640x640
57.5
18.4
8.0
YOLO26 Instance Segmentation
YOLO26 instance segmentation runs through the Serverless Cloud API, pretrained on COCO. For self-hosted deployment, see Roboflow Inference.
YOLO26 Instance Segmentation API
Set your API key and install the dependencies as shown above, then run yolo26n-seg-640 and annotate masks and labels:
YOLO26 Instance Segmentation models and benchmarks
Pass any of these aliases as the model_id; the yolov26* prefix variants resolve to the same models. Box and mask mAP are end-to-end (NMS-free) COCO val values.
yolo26n-seg-640
640x640
39.6
33.9
8.3
6.8
yolo26s-seg-640
640x640
47.3
40.0
10.8
8.5
yolo26m-seg-640
640x640
52.5
44.1
16.4
11.9
yolo26l-seg-640
640x640
54.4
45.5
18.4
12.8
yolo26x-seg-640
640x640
56.5
47.0
28.9
15.7
YOLO26 Keypoint Detection
YOLO26 keypoint/pose detection runs through the Serverless Cloud API, pretrained on COCO. For self-hosted deployment, see Roboflow Inference.
YOLO26 Keypoint Detection API
Set your API key and install the dependencies as shown above, then run yolo26n-pose-640 and annotate keypoints:
YOLO26 Keypoint Detection models and benchmarks
Pass any of these aliases as the model_id; the yolov26* prefix variants resolve to the same models. mAP is the end-to-end (NMS-free) COCO val value.
yolo26n-pose-640
640x640
57.2
3.8
2.3
yolo26s-pose-640
640x640
63.0
5.1
3.3
yolo26m-pose-640
640x640
68.8
9.0
4.6
yolo26l-pose-640
640x640
70.4
11.2
5.8
yolo26x-pose-640
640x640
71.6
19.1
8.3
YOLO26 Semantic Segmentation
YOLO26 semantic segmentation (yolo26-sem) is the recommended architecture for semantic segmentation on Roboflow. It uses Cityscapes pretrained weights and trains at a default resolution of 1024x1024. Available in five sizes: n, s, m, l, x.
To train a YOLO26-SEM model, create a semantic segmentation Project and select YOLO26 as your architecture. You can also upload custom-trained weights for YOLO26-SEM models.
The Cityscapes pretrained models (19 classes) are also available as public models you can run in a Workflow without training.
YOLO26 Semantic Segmentation models and benchmarks
Pass any of these Cityscapes-pretrained aliases as the model_id; the yolov26* prefix variants resolve to the same models. They run on the ONNX backend only; no prebuilt TensorRT engine is published.
yolo26n-sem-1024
1024x1024
23.3
yolo26s-sem-1024
1024x1024
25.9
yolo26m-sem-1024
1024x1024
34.6
yolo26l-sem-1024
1024x1024
35.8
yolo26x-sem-1024
1024x1024
53.2
* 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 val spec (source).
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