> 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/roboflow-3.md).

# Roboflow 3.0

Roboflow 3.0 Roboflow की इन-हाउस मॉडल आर्किटेक्चर है। यह object detection, instance segmentation, classification, और keypoint detection का समर्थन करता है। आप Roboflow प्लेटफ़ॉर्म पर Roboflow 3.0 मॉडल ट्रेन करते हैं और उन्हें हमारे [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md).

स्व-होस्टेड डिप्लॉयमेंट के लिए, देखें [Roboflow Inference](https://inference.roboflow.com/).

{% hint style="info" %}
Roboflow 3.0 के कोई सार्वजनिक डिफ़ॉल्ट COCO aliases नहीं हैं। आप अपने प्रशिक्षित मॉडल को इसके `project/version` पहचानकर्ता [Roboflow Project](https://app.roboflow.com/).
{% endhint %}

## कोड नमूने

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

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

नीचे दिए गए नमूने उस Roboflow 3.0 मॉडल के विरुद्ध inference चलाते हैं जिसे आपने ट्रेन किया है। बदलें `your-project/1` को अपने model URL और version से बदलें। अपना [Roboflow API Key](https://app.roboflow.com/settings/api) के माध्यम से `API_KEY` environment variable.

### ऑब्जेक्ट डिटेक्शन

```python
import os
import cv2
import urllib.request
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)
]

annotated = sv.BoxAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite("output.png", annotated)
```

### इंस्टेंस सेगमेंटेशन

```python
import os
import cv2
import urllib.request
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)
labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(detections.data.get("class_name", []), detections.confidence)
]

annotated = sv.MaskAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite("output.png", annotated)
```

### कीपॉइंट डिटेक्शन

```python
import os
import cv2
import urllib.request
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/person-walking.png"
image_path = "person-walking.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")

key_points = sv.KeyPoints.from_inference(result)

annotated = sv.EdgeAnnotator(color=sv.Color.BLUE, thickness=2).annotate(
    scene=image.copy(), key_points=key_points
)
annotated = sv.VertexAnnotator(color=sv.Color.GREEN, radius=5).annotate(
    scene=annotated, key_points=key_points
)
cv2.imwrite("output.png", annotated)
```

### Classification

Classification responses में confidences के साथ class predictions की एक सूची होती है, इसलिए visualization लागू नहीं होती। response से top class सीधे पढ़ें।

```python
import os
import cv2
import urllib.request
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")

print(f"शीर्ष वर्ग: {result['top']} ({result['confidence']:.4f})")
```

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

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


---

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