> 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/yolov5.md).

# YOLOv5

हम अपने माध्यम से YOLOv5 inferencing का समर्थन करते हैं [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md). YOLOv5 दो task variants में उपलब्ध है:

* Object detection
* Instance segmentation

{% hint style="info" %}
Roboflow पर नए projects के लिए YOLOv5 को train करना deprecated है। अपना खुद का YOLOv5 weights किसी Roboflow Project पर upload करना और Serverless Hosted API के विरुद्ध inference चलाना अभी भी समर्थित है।
{% endhint %}

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

YOLOv5 input size तब सेट होती है जब आप अपना model train करते हैं (सामान्य मान: 640x640 या 1280x1280)।

## डिफ़ॉल्ट COCO aliases

Serverless Hosted API पर YOLOv5 के लिए कोई pretrained COCO aliases नहीं हैं। YOLOv5 inferencing चलाने के लिए, कहीं और एक model train करें, अपने weights को एक [Roboflow Project](/roboflow/roboflow-hi/workspaces/key-concepts.md), और उसके `model_id` और `version`.

## कोड उदाहरण

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

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

अपना [Roboflow API Key](https://app.roboflow.com/settings/api) के माध्यम से `API_KEY` environment variable, और बदलें `model_id` को अपने workspace, project, और version से बदलें।

### Object detection

```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("output.png", annotated)
```

### Instance segmentation

```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/bicycle.png"
image_path = "bicycle.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)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()

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

cv2.imwrite("output.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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