# YOLOv5

We support YOLOv5 inferencing via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). YOLOv5 is available in two task variants:

* Object detection
* Instance segmentation

{% hint style="info" %}
Training YOLOv5 on Roboflow is deprecated for new projects. Uploading your own YOLOv5 weights to a Roboflow Project and running inference against the Serverless Hosted API remains supported.
{% endhint %}

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

YOLOv5 input size is set when you train your model (typical values: 640x640 or 1280x1280).

## Default COCO aliases

YOLOv5 has no pretrained COCO aliases on the Serverless Hosted API. To run YOLOv5 inferencing, train a model elsewhere, upload your weights to a [Roboflow Project](/workspaces/key-concepts.md), and call its `model_id` and `version`.

## Code samples

Install the SDK and [supervision](https://supervision.roboflow.com/) for visualization:

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

Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable, and replace `model_id` with your workspace, project, and version.

### Object detection

```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/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)

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

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

cv2.imwrite("output.png", annotated)
```

### Instance segmentation

```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"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)

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

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

cv2.imwrite("output.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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```
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