> 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/hailo/basics/specifying-a-model/discover-hailo-models.md).

# Discover Hailo models

Start with precompiled Hailo 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: Hailo Model Zoo

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

* `model_name="yolov8n_coco--640x640_quant_hailort_multidevice_1"`
* `zoo_url="degirum/hailo"`
* `inference_host_address="@local"`
* `model_properties={"device_type": ["HAILORT/HAILO8", "HAILORT/HAILO8L"]}`

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

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

## Why we start with the Hailo 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="yolov8s_silu_imagenet--224x224_quant_hailort_hailo8l_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={"device_type": ["HAILORT/HAILO8", "HAILORT/HAILO8L"]},
)

# COCO object detection model
model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_hailort_hailo8_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={"device_type": ["HAILORT/HAILO8", "HAILORT/HAILO8L"]},
)

# Face detection model
model_spec = ModelSpec(
    model_name="yolov8n_relu6_face--640x640_quant_hailort_multidevice_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={"device_type": ["HAILORT/HAILO8", "HAILORT/HAILO8L"]},
)
```

{% endcode %}

## Hailo-8 vs. Hailo-8L: picking the right device type

#### **Compatibility rule of thumb**

A model compiled for Hailo-8L will run on Hailo-8, but not the other way around.

That's why our starter examples use 8L-compatible or multidevice builds. We also specify `device_type` as a list—so the same code runs on both boards.

* **For examples**: We prioritize plug-and-play. Your snippet should "just work," whether you use Hailo-8 or Hailo-8L.
* **For deployments**: A model compiled for Hailo-8L will run on Hailo-8, but won't be optimized. For best performance (e.g., FPS, latency, power), use a model compiled specifically for your hardware.

### Example: picking a YOLOv8n variant

* **Runs on both (portable)**: `yolov8n_coco--640x640_quant_hailort_multidevice_1`
  * Great for docs and demos—this model works on Hailo-8 and Hailo-8L with the same code.
* **Optimized for Hailo-8**: `yolov8n_coco--640x640_quant_hailort_hailo8_1`
  * Best for production on Hailo-8; this model does *not* support Hailo-8L.

{% code overflow="wrap" %}

```python
# Portable demo (both Hailo-8 and Hailo-8L)
model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_hailort_multidevice_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={"device_type": ["HAILORT/HAILO8", "HAILORT/HAILO8L"]},
)

# Optimized for Hailo-8 (recommended for real deployments on Hailo-8)
model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_hailort_hailo8_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={"device_type": ["HAILORT/HAILO8"]},
)
```

{% endcode %}

## Bring your own Hailo model (advanced)

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

* Package your Hailo-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 Hailo Model Zoo, then graduate to bring in your own Hailo models, all using the same stable PySDK workflow.
{% endhint %}