> 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/specifying-a-model/discover-axelera-models.md).

# Discover Axelera models

Start with precompiled Axelera models that run out of the box—learn how to pick the right variant for your device.

*Estimated read time: 3 minutes*

## Where the model comes from: Axelera Model Zoo

In the [First inference](/axelera/basics/first-inference.md) guide, we specified the model by:

* `model_name="yolov8n_coco--640x640_quant_axelera_metis_1"`
* `zoo_url="degirum/axelera"`
* `inference_host_address="@local"`
* `model_properties={"device_type": ["AXELERA/METIS"]}`

That `zoo_url` points to the [Axelera Model Zoo](https://hub.degirum.com/public-models/degirum/axelera?utm_source=docs.degirum.com\&utm_medium=site\&utm_campaign=cookbooks-axelera-cookbook-specifying-a-model-discover-axelera-models), a curated collection of models precompiled for Axelera Metis hardware. We highlight this zoo because it lets you run examples immediately, without any conversion steps.

{% hint style="info" %}
Any model compiled for Axelera can be used with the same pattern. The advanced guide shows how to bring your own model (BYOM).
{% endhint %}

## Why we start with the Axelera Model Zoo

* **Instant results**: models are precompiled and follow predictable I/O, so examples “just work”
* **Consistent setup**: you focus on `model_name`, `zoo_url`, and `device_type`—everything else stays the same
* **Easy to swap**: change only the `model_name` to try a different model

## Example models

{% code overflow="wrap" %}

```python
# ImageNet classification model
model_spec = ModelSpec(
    model_name="yolov8n_imagenet--224x224_quant_axelera_metis_1",
    zoo_url="degirum/axelera",
    inference_host_address="@local",
    model_properties={"device_type": ["AXELERA/METIS"]},
)

# COCO object detection model
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"]},
)

# Face detection model
model_spec = ModelSpec(
    model_name="yolov8n_relu6_face--640x640_quant_axelera_metis_1",
    zoo_url="degirum/axelera",
    inference_host_address="@local",
    model_properties={"device_type": ["AXELERA/METIS"]},
)
```

{% endcode %}

## Bring your own Axelera model (advanced)

When you’re ready, the advanced guide shows how to:

* Package your Axelera-compiled artifacts with a PySDK `model.json` that defines inputs, outputs, preprocessing, and postprocessing.
* Point PySDK to your private or local model zoo.
* Rescan the zoo as you add models.
* Use the same `ModelSpec` pattern—only the `zoo_url` and `model_name` change.

{% hint style="info" %}
**Bottom line**: Start fast with the Axelera Model Zoo, then graduate to bring in your own Axelera models, all using the same stable PySDK workflow.
{% endhint %}