> 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/models/supported-models/yolo-nas.md).

# YOLO-NAS

YOLO-NAS is an object detection model from Deci, produced by neural architecture search. Roboflow serves COCO-pretrained YOLO-NAS checkpoints under short aliases, and you can [upload your own weights](/models/model-weights/upload-custom-weights.md) to run a model you trained elsewhere.

Training YOLO-NAS is not supported on Roboflow. For a Roboflow-trained detector, see [RF-DETR](/models/supported-models/rf-detr.md), [YOLO26](/models/supported-models/yolo26.md), or [YOLO11](/models/supported-models/yolo11.md).

## Pretrained aliases

Pass one of these IDs as `model_id` to run a COCO-pretrained checkpoint without training anything. The full list lives on the [Pretrained Model Aliases](/models/pretrained-aliases.md) page.

| Model             | Input size | Task             | Model ID         | Test                                                                     |
| ----------------- | ---------- | ---------------- | ---------------- | ------------------------------------------------------------------------ |
| YOLO-NAS (small)  | 640        | Object Detection | `yolo-nas-s-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/14) |
| YOLO-NAS (medium) | 640        | Object Detection | `yolo-nas-m-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/15) |
| YOLO-NAS (large)  | 640        | Object Detection | `yolo-nas-l-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/16) |

## Code sample

{% stepper %}
{% step %}

### Get your API Key

Create a Roboflow account, find your key on the [Roboflow API settings page](https://app.roboflow.com/settings/api) and make it available to your shell:

```bash
export ROBOFLOW_API_KEY="your-key-here"
```

{% endstep %}

{% step %}

### Install the dependencies

Install the Inference SDK and [supervision](https://supervision.roboflow.com/) for decoding and drawing predictions:

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

{% endstep %}

{% step %}

### Run the model

This example runs the pretrained `yolo-nas-s-640` checkpoint. To serve your own weights, swap in your `{workspace}/{model-slug}` ID (see [Versions, Trainings, and Models](/models/versions-trainings-and-models.md)).

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

image = sv.load_image_from_url("https://media.roboflow.com/inference/people-walking.jpg")

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)
results = client.infer(image, model_id="yolo-nas-s-640")

detections = sv.Detections.from_inference(results)

labels = [
    f"{name} {conf:.2f}"
    for name, conf in zip(detections.data["class_name"], detections.confidence)
]
annotated = sv.BoxAnnotator().annotate(image.copy(), detections)
annotated = sv.LabelAnnotator().annotate(annotated, detections, labels=labels)
cv2.imwrite("annotated.png", annotated)
```

{% endstep %}
{% endstepper %}

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

* `https://serverless.roboflow.com` for the Serverless Cloud API.
* `http://localhost:9001` for a local [Inference](https://docs.roboflow.com/deployment/self-hosted/self-hosted) server.
* Your [Dedicated Deployment](https://docs.roboflow.com/deployment/roboflow-cloud/dedicated-deployments) URL for a private endpoint.
  {% endhint %}

You can also load the checkpoint in-process with the [`inference`](https://docs.roboflow.com/deployment/self-hosted/self-hosted) package:

```python
from inference import get_model

model = get_model(model_id="yolo-nas-s-640")
results = model.infer("https://media.roboflow.com/inference/people-walking.jpg")
```