> 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 model architecture है। यह object detection, instance segmentation, classification, और keypoint detection को सपोर्ट करती है। आप Roboflow प्लेटफ़ॉर्म पर Roboflow 3.0 models को train करते हैं और उन्हें हमारे [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md).

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

{% hint style="info" %}
Roboflow 3.0 के लिए कोई public default COCO aliases नहीं हैं। आप अपने trained model को उसके `project/version` आपके [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 आपके द्वारा प्रशिक्षित Roboflow 3.0 model पर inference चलाते हैं। बदलें `your-project/1` को अपने model URL और version से। अपना [Roboflow API Key](https://app.roboflow.com/settings/api) के माध्यम से `API_KEY` environment variable.

### Object detection

```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)
```

### Instance segmentation

```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)
```

### Keypoint detection

```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। top class को सीधे response से 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"Top class: {result['top']} ({result['confidence']:.4f})")
```

{% 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 local [Inference](https://inference.roboflow.com/) server के लिए।
* आपका [Dedicated Deployment](/roboflow/roboflow-hi/deploy/dedicated-deployments.md) private endpoint के लिए URL।
  {% endhint %}


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