Roboflow Docs

YOLOv9

Use YOLOv9 object detection through our Serverless Cloud API

We support YOLOv9 object detection inferencing via our Serverless Cloud API. Training YOLOv9 is not supported on Roboflow, but you can upload pretrained weights for an existing Project and serve them through the Serverless Cloud API.

For self-hosted deployment, see Roboflow Inference.

YOLOv9 input size is set when you train your model outside Roboflow (typical values: 640x640 or 1280x1280).

YOLOv9 API

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:

export ROBOFLOW_API_KEY="your-key-here"
2

Install the dependencies

Install the SDK and supervision for decoding and annotation:

pip install -U inference-sdk supervision
3

Run the model

This example runs Roboflow's public YOLOv9 model trained on COCO (coco/18), so it works as-is. To serve your own weights, swap in your {workspace}/{model-slug} ID (see Versions, Trainings, and Models).

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

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

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)
result = client.infer(image, model_id="coco/18")

detections = sv.Detections.from_inference(result)

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

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

Set api_url to match your deployment target:

  • https://serverless.roboflow.com for the Serverless Cloud API.
  • http://localhost:9001 for a local Inference server.
  • Your Dedicated Deployment URL for a private endpoint.