> 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/deploy/supported-models/yolov7.md).

# YOLOv7

We support YOLOv7 instance segmentation inferencing via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). Training YOLOv7 is not supported on Roboflow, but you can [upload your own weights](/deploy/upload-custom-weights.md) and run inference against them.

For self-hosted deployment, see [Roboflow Inference](https://inference.roboflow.com/).

YOLOv7 input size is set when you train your model outside Roboflow (typical values: 640x640 or 1280x1280).

## Default COCO aliases

There are no pretrained YOLOv7 aliases. You must train YOLOv7 instance segmentation outside of Roboflow, upload the weights to a Project, then call your own `model_id` against the Serverless Hosted API.

## Code sample

Install the Inference SDK and [supervision](https://supervision.roboflow.com/) for decoding and drawing masks:

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

Run inference against your own YOLOv7 instance segmentation model, then use supervision to render the predicted masks and labels onto the source image. Replace `model_id` with your Project's value, and pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `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" %}
Set `api_url` to match your deployment target:

* `https://serverless.roboflow.com` for the Serverless Hosted API.
* `http://localhost:9001` for a local [Inference](https://inference.roboflow.com/) server.
* Your [Dedicated Deployment](/deploy/dedicated-deployments.md) URL for a private endpoint.
  {% endhint %}


---

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