> 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/yolov9.md).

# YOLOv9

हम अपने माध्यम से YOLOv9 object detection inferencing का समर्थन करते हैं [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md). YOLOv9 को train करना Roboflow पर समर्थित नहीं है, लेकिन आप [pretrained weights अपलोड कर सकते हैं](/roboflow/roboflow-hi/deploy/upload-custom-weights.md) किसी मौजूदा Project के लिए और उन्हें Serverless Hosted API के माध्यम से serve कर सकते हैं।

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

YOLOv9 input size तब सेट की जाती है जब आप अपना model Roboflow के बाहर train करते हैं (सामान्य मान: 640x640 या 1280x1280)।

## Aliases

YOLOv9 के लिए कोई default Roboflow Universe aliases नहीं हैं। inference चलाने के लिए, अपना खुद का trained YOLOv9 `model_id` रूप में `project/version` (एक ऐसे Project से जहाँ आपने YOLOv9 weights अपलोड किए हैं)।

## कोड उदाहरण

SDK इंस्टॉल करें और [supervision](https://supervision.roboflow.com/) decoding और annotation के लिए:

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

बदलें `your-project/1` अपने स्वयं के `model_id`. अपना [Roboflow API Key](https://app.roboflow.com/settings/api) के माध्यम से `API_KEY` environment variable.

```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)

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)

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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