> For the complete documentation index, see [llms.txt](https://docs.degirum.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.degirum.com/axelera/basics/running-inference/images.md).

# Images

Run inference on a single image using a URL, file path, or NumPy array. This page shows how to use each input type with a loaded model.

*Estimated read time: 2 minutes*

The simplest way to run inference on an image is to call the model like a function. Model objects are callable—they implement `__call__`, which delegates to `predict`. In other words:

{% code overflow="wrap" %}

```python
result = model(
    image_source
)  # exactly the same as: result = model.predict(image_source)
```

{% endcode %}

This page shows how to run inference on three common input types: a URL, a local file path, and a NumPy array.

## Common setup (used in all cases)

{% code overflow="wrap" %}

```python
from degirum_tools import ModelSpec, Display

# Configure & load once
model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_axelera_metis_1",
    zoo_url="degirum/axelera",
    inference_host_address="@local",
    model_properties={"device_type": ["AXELERA/METIS"]},
)
model = model_spec.load_model()
```

{% endcode %}

## Image URL

{% code overflow="wrap" %}

```python
from degirum_tools import remote_assets  # small gallery of public sample URLs

image_url = remote_assets.three_persons  # or any reachable image URL (string)
result = model(image_url)

with Display("AI Camera — URL") as output_display:
    output_display.show_image(result.image_overlay)
```

{% endcode %}

{% hint style="info" %}
URLs behind authentication or hosted on an intranet may fail if your runtime can't access them.
{% endhint %}

## Local file path

{% code overflow="wrap" %}

```python
from pathlib import Path

image_path = Path.home() / "Pictures" / "test.jpg"  # change as needed
result = model(str(image_path))  # file paths are accepted

with Display("AI Camera — File") as output_display:
    output_display.show_image(result.image_overlay)
```

{% endcode %}

{% hint style="info" %}
Windows: Use raw strings for paths, e.g., `r"C:\Users\you\Pictures\test.jpg"`.
{% endhint %}

## NumPy array

{% code overflow="wrap" %}

```python
# pip install opencv-python
import cv2

frame_bgr = cv2.imread("test.jpg")  # returns BGR ndarray
if frame_bgr is None:
    raise FileNotFoundError("Could not read test.jpg")

result = model(frame_bgr)

with Display("AI Camera — Array") as output_display:
    output_display.show_image(result.image_overlay)
```

{% endcode %}

That’s it—choose the type that matches your input, reuse the common setup, and call `model(...)` or `model.predict(...)` to get results.