> 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/l2cs-net.md).

# L2Cs-Net

L2Cs-Net is a gaze direction estimation model that detects faces and predicts each face's yaw and pitch angles. You can run it through our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md).

## Code sample

Run L2Cs-Net through the HTTP endpoint directly with `curl`, or with the [`inference-sdk`](https://inference.roboflow.com/inference_helpers/inference_sdk/) wrapper.

{% tabs %}
{% tab title="HTTP (curl)" icon="webhook" %}
{% stepper %}
{% step %}

### Get your API Key

Create a Roboflow account, find your key on the [Roboflow API settings page](https://app.roboflow.com/settings/api) and make it available to your shell:

```bash
export ROBOFLOW_API_KEY="your-key-here"
```

{% endstep %}

{% step %}

### Run the model

Call the `/gaze/gaze_detection` endpoint with `curl`:

```bash
curl --location 'https://serverless.roboflow.com/gaze/gaze_detection' \
  --header 'Content-Type: application/json' \
  --data '{
    "api_key": "'"$ROBOFLOW_API_KEY"'",
    "image": {"type": "url", "value": "https://media.roboflow.com/inference/man.jpg"}
  }'
```

{% endstep %}
{% endstepper %}
{% endtab %}

{% tab title="SDK (Python)" icon="python" %}
{% stepper %}
{% step %}

### Get your API Key

Create a Roboflow account, find your key on the [Roboflow API settings page](https://app.roboflow.com/settings/api) and make it available to your shell:

```bash
export ROBOFLOW_API_KEY="your-key-here"
```

{% endstep %}

{% step %}

### Install the dependencies

This package calls the model:

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

{% endstep %}

{% step %}

### Run the model

The code sample below calls `detect_gazes`, which hits the same `/gaze/gaze_detection` endpoint:

```python
import os
import cv2
import numpy as np
import requests
from inference_sdk import InferenceHTTPClient

content = requests.get("https://media.roboflow.com/inference/man.jpg").content
image = cv2.imdecode(np.frombuffer(content, np.uint8), cv2.IMREAD_COLOR)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.environ["ROBOFLOW_API_KEY"],
).select_api_v1()

result = client.detect_gazes(image)

for prediction in result[0]["predictions"]:
    face = prediction["face"]
    yaw = prediction["yaw"]
    pitch = prediction["pitch"]
    print(f"Face at ({face['x']}, {face['y']}) - yaw: {yaw:.3f}, pitch: {pitch:.3f}")
```

{% endstep %}
{% endstepper %}
{% endtab %}
{% endtabs %}

## Inference speed

Latency measured with [Roboflow Inference](https://inference.roboflow.com/) on 1x NVIDIA L4, batch size 1, mean after warmup.

<table data-search="false"><thead><tr><th>Model</th><th>Latency (ms)</th></tr></thead><tbody><tr><td><code>l2cs-net</code></td><td>6.0</td></tr></tbody></table>

Measured on a single face crop, which is what the model expects as input.

{% 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 %}

The response contains a list with `predictions` (each with `face` bounding box, `landmarks`, `yaw`, and `pitch` in radians), `time`, `time_face_det`, and `time_gaze_det`.

For self-hosted deployments and additional examples, see the [Roboflow Inference docs](https://inference.roboflow.com/).