> 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/deploy/supported-models/yolo26.md).

# YOLO26

We support YOLO26 inferencing via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). YOLO26 is available in four task variants:

* Object detection (COCO pretrained)
* Instance segmentation (COCO pretrained)
* Keypoint/pose detection (COCO pretrained)
* Semantic segmentation (Cityscapes pretrained)

For self-hosted deployment, see [Roboflow Inference](https://inference.roboflow.com/).

## Code samples

Install the SDK and the [supervision](https://supervision.roboflow.com/) library for annotation:

```bash
pip install inference-sdk supervision opencv-python
```

Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable. Each sample downloads a test image, runs inference through `inference-sdk`, decodes the response with `supervision`, and writes an annotated PNG to disk.

### 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)
```

<figure><img src="/files/QKdkDGTtoC39Ig3VKH24" alt=""><figcaption></figcaption></figure>

### 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)
```

<figure><img src="/files/CL1yVLKzoqElWZQSKgjV" alt=""><figcaption></figcaption></figure>

### 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)
```

<figure><img src="/files/vJrvqemcqmQKWkttl45J" alt=""><figcaption></figcaption></figure>

### 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](/deploy/upload-custom-weights.md) for YOLO26-SEM models.

The Cityscapes pretrained models (19 classes) are also available as public models you can run in a [Workflow](/workflows/what-is-workflows.md) without training.

## Default COCO aliases

Pass any of the following aliases as `model_id` when calling the SDK. The `inference-sdk` resolves each alias to its pretrained Roboflow Universe model client-side.

| 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 resolve to the same models.

{% hint style="info" %}
Set `api_url` to match your deployment target:

* `https://serverless.roboflow.com` for the Serverless Hosted API.
* `http://localhost:9001` for a local [Inference](https://inference.roboflow.com/) server.
* Your [Dedicated Deployment](/deploy/dedicated-deployments.md) URL for a private endpoint.
  {% endhint %}


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