> 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-2.md).

# Roboflow 2.0

Roboflow 2.0 एक DeepLabv3-आधारित semantic segmentation मॉडल है। आप Roboflow प्लेटफ़ॉर्म पर Roboflow 2.0 मॉडल्स को train करते हैं और उन्हें हमारे माध्यम से deploy करते हैं [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 2.0 के कोई public default models नहीं हैं। आप अपने स्वयं के trained model को उसके `project/version` identifier का उपयोग करके अपने [Roboflow Project](https://app.roboflow.com/).
{% endhint %}

## कोड उदाहरण

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

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

उस Roboflow 2.0 semantic segmentation मॉडल पर inference चलाएँ जिसे आपने train किया है, per-pixel class map को decode करें, और एक annotated PNG लिखें। 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.

Response में एक `segmentation_mask` (base64-encoded grayscale PNG जिसमें हर pixel value एक class ID है और `0` background है) और एक `class_map` class IDs को class names से mapping करता है। Script इसे एक `sv.Detections` row प्रति class में विभाजित करता है ताकि `sv.MaskAnnotator` masks को source image पर overlay कर सके।

```python
import base64
import os
import urllib.request

import cv2
import numpy as np
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)
OUTPUT_PATH = "annotated.png"

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")
predictions = result["predictions"]

mask_bytes = base64.b64decode(predictions["segmentation_mask"])
class_map = predictions.get("class_map", {})
class_mask = cv2.imdecode(np.frombuffer(mask_bytes, np.uint8), cv2.IMREAD_GRAYSCALE)
class_mask = cv2.resize(class_mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)

class_ids = [cid for cid in np.unique(class_mask).tolist() if cid != 0]
if class_ids:
    masks, xyxy, names = [], [], []
    for cid in class_ids:
        binary = class_mask == cid
        rows = np.where(np.any(binary, axis=1))[0]
        cols = np.where(np.any(binary, axis=0))[0]
        xyxy.append([cols[0], rows[0], cols[-1], rows[-1]])
        masks.append(binary)
        names.append(class_map.get(str(cid), str(cid)))

    detections = sv.Detections(
        xyxy=np.array(xyxy, dtype=np.float64),
        mask=np.array(masks),
        class_id=np.array(class_ids),
        data={"class_name": np.array(names)},
    )
    annotated = sv.MaskAnnotator().annotate(scene=image.copy(), detections=detections)
    annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections)
else:
    annotated = image

cv2.imwrite(OUTPUT_PATH, annotated)
print(f"{OUTPUT_PATH} सहेजा गया")
```

{% 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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