YOLOv12

Use the YOLOv12 object detection model through our Serverless Cloud API

We support YOLOv12 object detection inferencing via our Serverless Cloud API. YOLOv12 is supported in five sizes (n, s, m, l, x) for the object detection task.

For self-hosted deployment, see Roboflow Inference.

YOLOv12 input size is set when you train your model on Roboflow (typical values: 640x640 or 1280x1280).

YOLOv12 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 Inference SDK and supervision for decoding and visualizing predictions:

pip install -U inference-sdk supervision

3

Run the model

This example runs a public YOLOv12 model trained on a screws dataset (screw, flat-washer, hex-nut). Swap in your own {workspace}/{model-slug} to run your trained weights (see Versions, Trainings, and Models).

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

image = sv.load_image_from_url("https://media.roboflow.com/docs/bolts.jpg")

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="erik-pe6au/bolts-uzqzc-4-yolo12s-t1")

detections = sv.Detections.from_inference(result)

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