> 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/sam2.md).

# SAM2

We support Meta's [Segment Anything Model 2](https://github.com/facebookresearch/sam2) inferencing via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). SAM2 is a promptable visual segmentation model that accepts points and bounding boxes as prompts. We offer two SAM2 endpoints:

* [/sam2/embed\_image](#post-sam2-embed_image), which generates and caches an image embedding
* [/sam2/segment\_image](#post-sam2-segment_image), which returns instance segmentation masks for the given prompts

## Code sample

Run SAM2 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 `/sam2/segment_image` endpoint with `curl`:

```bash
curl --location 'https://serverless.roboflow.com/sam2/segment_image' \
  --header 'Content-Type: application/json' \
  --data '{
    "api_key": "'"$ROBOFLOW_API_KEY"'",
    "image": {"type": "url", "value": "https://media.roboflow.com/quickstart/traffic.jpg"},
    "prompts": {"prompts": [{"points": [{"x": 520, "y": 470, "positive": true}]}]},
    "sam2_version_id": "hiera_tiny"
  }'
```

{% 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

These packages call the model and draw its results:

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

{% endstep %}

{% step %}

### Run the model

Call the segmentation endpoint with a single positive point prompt, convert the returned polygons to detections with supervision, and save an annotated PNG with the mask drawn over the input image:

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

content = requests.get("https://media.roboflow.com/quickstart/traffic.jpg").content
image = cv2.imdecode(np.frombuffer(content, np.uint8), cv2.IMREAD_COLOR)
height, width = image.shape[:2]

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

result = client.sam2_segment_image(
    inference_input=image,
    prompts=[
        {"points": [{"x": 520, "y": 470, "positive": True}]}
    ],
    sam2_version_id="hiera_tiny",
)

detections = sv.Detections.from_sam3(sam3_result=result, resolution_wh=(width, height))

annotated = sv.MaskAnnotator().annotate(image.copy(), detections)
cv2.imwrite("traffic_annotated.png", annotated)
```

`sv.Detections.from_sam3` reads the polygon predictions that both SAM2 and SAM3 return, so the same call decodes either model's output.

<figure><img src="/files/e8FqE2jVLGb1WOvVJQQJ" alt=""><figcaption></figcaption></figure>
{% 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>sam2</code></td><td>177.7</td></tr></tbody></table>

Measured with `segment_image` on the `hiera_large` checkpoint. SAM2 caches image embeddings, so this figure uses a fresh image each call and reflects the full encode plus decode cost. Re-prompting an already-encoded image is substantially faster.

{% 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 %}

For additional usage details, including embedding caching and box prompts, see the [Inference documentation](https://inference.roboflow.com/).