> 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/yolov8.md). # YOLOv8 YOLOv8 is an object detection, instance segmentation, and keypoint detection model family from Ultralytics. Roboflow serves COCO-pretrained YOLOv8 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 YOLOv8 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). ## YOLOv8 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 | | ----------------------------- | ---------- | --------------------- | ------------------- | -------------------------------------------------------------------------------------- | | YOLOv8n | 640 | Object Detection | `yolov8n-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/3) | | YOLOv8n | 1280 | Object Detection | `yolov8n-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/9) | | YOLOv8s | 640 | Object Detection | `yolov8s-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/6) | | YOLOv8s | 1280 | Object Detection | `yolov8s-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/10) | | YOLOv8m | 640 | Object Detection | `yolov8m-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/8) | | YOLOv8m | 1280 | Object Detection | `yolov8m-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/11) | | YOLOv8l | 640 | Object Detection | `yolov8l-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/7) | | YOLOv8l | 1280 | Object Detection | `yolov8l-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/12) | | YOLOv8x | 640 | Object Detection | `yolov8x-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/5) | | YOLOv8x | 1280 | Object Detection | `yolov8x-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco/model/13) | | YOLOv8n Instance Segmentation | 640 | Instance Segmentation | `yolov8n-seg-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/2) | | YOLOv8n Instance Segmentation | 1280 | Instance Segmentation | `yolov8n-seg-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/7) | | YOLOv8s Instance Segmentation | 640 | Instance Segmentation | `yolov8s-seg-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/4) | | YOLOv8m Instance Segmentation | 1280 | Instance Segmentation | `yolov8s-seg-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/8) | | YOLOv8m Instance Segmentation | 640 | Instance Segmentation | `yolov8m-seg-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/5) | | YOLOv8m Instance Segmentation | 1280 | Instance Segmentation | `yolov8m-seg-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/9) | | YOLOv8l Instance Segmentation | 640 | Instance Segmentation | `yolov8l-seg-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/6) | | YOLOv8l Instance Segmentation | 1280 | Instance Segmentation | `yolov8l-seg-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/10) | | YOLOv8x Instance Segmentation | 640 | Instance Segmentation | `yolov8x-seg-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/3) | | YOLOv8x Instance Segmentation | 1280 | Instance Segmentation | `yolov8x-seg-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco-dataset-vdnr1/model/11) | | YOLOv8x Keypoint Detection | 1280 | Keypoint Detection | `yolov8x-pose-1280` | [Test in browser](https://universe.roboflow.com/microsoft/coco-pose-detection/6) | | YOLOv8x Keypoint Detection | 640 | Keypoint Detection | `yolov8x-pose-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-pose-detection/5) | | YOLOv8l Keypoint Detection | 640 | Keypoint Detection | `yolov8l-pose-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-pose-detection/4) | | YOLOv8m Keypoint Detection | 640 | Keypoint Detection | `yolov8m-pose-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-pose-detection/3) | | YOLOv8s Keypoint Detection | 640 | Keypoint Detection | `yolov8s-pose-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-pose-detection/2) | | YOLOv8n Keypoint Detection | 640 | Keypoint Detection | `yolov8n-pose-640` | [Test in browser](https://universe.roboflow.com/microsoft/coco-pose-detection/1) | ## YOLOv8 API {% 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 `yolov8n-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="yolov8n-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="yolov8n-640") results = model.infer("https://media.roboflow.com/inference/people-walking.jpg") ```