> 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/yolov7.md).

# YOLOv7

हम अपने माध्यम से YOLOv7 instance segmentation inferencing का समर्थन करते हैं [Serverless Hosted API](/roboflow/roboflow-hi/deploy/serverless-hosted-api-v2.md). YOLOv7 training Roboflow पर समर्थित नहीं है, लेकिन आप [अपनी खुद की weights अपलोड करें](/roboflow/roboflow-hi/deploy/upload-custom-weights.md) और उन पर inference चलाएँ।

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

YOLOv7 input size तब सेट किया जाता है जब आप अपना model Roboflow के बाहर train करते हैं (सामान्य मान: 640x640 या 1280x1280)।

## डिफ़ॉल्ट COCO aliases

कोई pretrained YOLOv7 aliases नहीं हैं। आपको Roboflow के बाहर YOLOv7 instance segmentation train करनी होगी, weights को किसी Project में upload करना होगा, फिर अपना स्वयं का `model_id` Serverless Hosted API पर।

## कोड उदाहरण

Inference SDK इंस्टॉल करें और [supervision](https://supervision.roboflow.com/) मास्क को decode और draw करने के लिए:

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

अपने स्वयं के YOLOv7 instance segmentation model पर inference चलाएँ, फिर predicted masks और labels को source image पर render करने के लिए supervision का उपयोग करें। Replace `model_id` को अपने Project के मान से बदलें, और 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/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"),
)
results = client.infer(image_path, model_id="your-project/1")

detections = sv.Detections.from_inference(results)

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

labels = [
    f"{cls} {conf:.2f}"
    for cls, conf in zip(detections.data.get("class_name", []), detections.confidence)
]

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

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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