> 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/roboflow/roboflow-hi/deploy/supported-models/yolov12.md).

# YOLOv12

हम अपने माध्यम से YOLOv12 object detection inferencing का समर्थन करते हैं [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md). YOLOv12 पाँच sizes में समर्थित है (`n`, `s`, `m`, `l`, `x`) ऑब्जेक्ट डिटेक्शन टास्क के लिए।

self-hosted deployment के लिए, देखें [Roboflow Inference](https://inference.roboflow.com/).

YOLOv12 input size तब सेट होती है जब आप Roboflow पर अपना model train करते हैं (सामान्य मान: 640x640 या 1280x1280).

{% hint style="info" %}
YOLOv12, Serverless Hosted API पर pretrained COCO aliases के साथ ship नहीं होता। YOLOv12 का उपयोग करने के लिए, Roboflow पर अपना model train करें और उसे अपने model URL के इस form में call करें `your-project/version`.
{% endhint %}

## कोड उदाहरण

इंस्टॉल करें [Inference SDK](https://inference.roboflow.com/inference_helpers/inference_sdk/) और [supervision](https://supervision.roboflow.com/) predictions को decode और visualize करने के लिए:

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

अपने trained YOLOv12 model पर detection चलाएँ, फिर response को decode करें `sv.Detections.from_inference`, bounding boxes और labels draw करें, और annotated image save करें। अपना [Roboflow API Key](https://app.roboflow.com/settings/api) के माध्यम से `API_KEY` environment variable, और इसे replace करें `model_id` अपने स्वयं के `project/version`.

```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)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)

labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(detections.data.get("class_name", []), detections.confidence)
]

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated = box_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections, labels=labels)

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

{% hint style="info" %}
सेट करें `api_url` को अपने deployment target से मिलाने के लिए:

* `https://serverless.roboflow.com` Serverless Hosted API के लिए।
* `http://localhost:9001` एक स्थानीय [Inference](https://inference.roboflow.com/) सर्वर।
* आपका [Dedicated Deployment](/roboflow/roboflow-hi/deploy/dedicated-deployments.md) एक private endpoint के लिए URL।
  {% endhint %}


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