> 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/yololite.md).

# YOLOLite

YOLOlite Roboflow का एक हल्का object detection model family है, जिसे कम-लेटेंसी deployments और edge hardware के लिए डिज़ाइन किया गया है। आप YOLOlite को एक [Project](/roboflow/roboflow-hi/workspaces/key-concepts.md) Roboflow में और इसे हमारे माध्यम से deploy करें [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md).

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

YOLOlite input size Roboflow पर training के दौरान configured किया जाता है।

## उपलब्ध वेरिएंट्स

YOLOlite दो scaling families में उपलब्ध है: एक standard set और एक edge-optimized set. प्रत्येक पाँच sizes में उपलब्ध है.

| परिवार | वेरिएंट्स                                                                                      |
| ------ | ---------------------------------------------------------------------------------------------- |
| मानक   | `yololite-n`, `yololite-s`, `yololite-m`, `yololite-l`, `yololite-xl`                          |
| एज     | `yololite-edge-n`, `yololite-edge-s`, `yololite-edge-m`, `yololite-edge-l`, `yololite-edge-xl` |

जब आप एक Project पर YOLOlite model train करते हैं, तो आप एक variant चुनते हैं। फिर trained model Serverless Hosted API के तहत आपके `workspace/project/version` पथ.

## कोड उदाहरण

इंस्टॉल करें [Inference SDK](https://inference.roboflow.com/inference_helpers/inference_sdk/) और [supervision](https://supervision.roboflow.com/):

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

निम्नलिखित sample Roboflow Project पर प्रशिक्षित YOLOlite model के विरुद्ध detection चलाता है, और response को decode करने के लिए `supervision`, बॉक्स और labels बनाता है, और annotated PNG सेव करता है. Replace `your-project/1` अपने स्वयं के `project/version`. 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"),
)
results = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(results)

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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