> 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/depth-anything-v2.md).

# Depth Anything V2

Depth Anything V2 is a monocular depth estimation model. It returns a normalized depth map (values between 0 and 1) for any input image.

{% hint style="info" %}
Depth Anything V2 is not available on the Serverless Hosted API. Run it on a [Dedicated Deployment](/deploy/dedicated-deployments.md) or [self-hosted Inference](https://inference.roboflow.com/).
{% endhint %}

## Code sample

{% 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

Install the [Inference SDK](https://inference.roboflow.com/):

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

{% endstep %}

{% step %}

### Run the model

Set `api_url` to your Dedicated Deployment URL or a local Inference server. The script colorizes the depth map and writes a side-by-side comparison with the input.

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

content = requests.get("https://media.roboflow.com/notebooks/examples/bicycle.png").content
image = cv2.imdecode(np.frombuffer(content, np.uint8), cv2.IMREAD_COLOR)
client = InferenceHTTPClient(
    api_url="https://your-deployment.roboflow.cloud",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)
result = client.depth_estimation(image)

depth = np.array(result["normalized_depth"], dtype=np.float32)
depth = cv2.resize(depth, (image.shape[1], image.shape[0]))
depth_vis = (depth * 255).astype(np.uint8)
depth_color = cv2.applyColorMap(depth_vis, cv2.COLORMAP_INFERNO)

cv2.imwrite("depth_annotated.png", np.hstack([image, depth_color]))
```

<figure><img src="/files/e9MgW9BFNDzenPtS5wyH" alt=""><figcaption></figcaption></figure>
{% endstep %}
{% endstepper %}

## 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>depth-anything-v2</code></td><td>40.1</td></tr></tbody></table>

Measured on the Small checkpoint.

{% hint style="info" %}
Set `api_url` to match your deployment target:

* `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 %}