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Migrate Hailo detector key and support hailo device (#24327)

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* Migrate Hailo detector key and support hailo device

* Fix missing check
This commit is contained in:
Nicolas Mowen
2026-09-14 08:23:36 -06:00
committed by GitHub
parent caa6edecac
commit 7821ecbb43
24 changed files with 417 additions and 61 deletions
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docs/docs/configuration/object_detectors.md
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@@ -22,7 +22,7 @@ Frigate supports multiple different detectors that work on different types of ha
**Most Hardware**
- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
- [Hailo](#hailo): The Hailo-8, Hailo-8L and Hailo-8R AI Acceleration modules are available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
**AMD**
@@ -285,9 +285,9 @@ models:
---
## Hailo-8
## Hailo
This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleration Modules. The integration automatically detects your hardware architecture via the Hailo CLI and selects the appropriate default model if no custom model is specified.
This detector is available for use with the Hailo-8, Hailo-8L and Hailo-8R AI Acceleration Modules. The integration identifies which of them is attached and selects the matching default model if no custom model is specified.
See the [installation docs](../frigate/installation.md#hailo-8) for information on configuring the Hailo hardware.
@@ -308,11 +308,11 @@ The HailoRT runtime is not part of the Frigate image. It is downloaded and insta
When configuring the Hailo detector, you have two options to specify the model: a local **path** or a **URL**.
If both are provided, the detector will first check for the model at the given local path. If the file is not found, it will download the model from the specified URL. The model file is cached under `/config/model_cache/hailo`.
<ModelConfigDropdown detectorTitle="Hailo-8/Hailo-8L" models={objectDetectorsModels.hailo8l.models} />
<ModelConfigDropdown detectorTitle="Hailo" models={objectDetectorsModels.hailo.models} />
For additional ready-to-use models, please visit: https://github.com/hailo-ai/hailo_model_zoo
Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation.
Hailo supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation.
> **Note:**
> The config.path parameter can accept either a local file path or a URL ending with .hef. When provided, the detector will first check if the path is a local file path. If the file exists locally, it will use it directly. If the file is not found locally or if a URL was provided, it will attempt to download the model from the specified URL.
docs/docs/frigate/hardware.md
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@@ -54,7 +54,7 @@ Frigate supports multiple different detectors that work on different types of ha
**Most Hardware**
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices offering a wide range of compatibility with devices.
- [Hailo](#hailo-8): The Hailo-8, Hailo-8L and Hailo-8R AI Acceleration modules are available in m.2 format with a HAT for RPi devices offering a wide range of compatibility with devices.
- [Supports many model architectures](../../configuration/object_detectors#configuration-hailo)
- Runs best with tiny or small size models
@@ -111,12 +111,13 @@ Frigate supports multiple different detectors that work on different types of ha
### Hailo-8
Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms, including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
Frigate supports the Hailo-8, Hailo-8L and Hailo-8R AI Acceleration Modules on compatible hardware platforms, including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate identifies which of them is attached and selects the matching default model when a custom model isn’t provided.
**Default Model Configuration:**
- **Hailo-8L:** Default model is **YOLOv6n**.
- **Hailo-8:** Default model is **YOLOv6n**.
- **Hailo-8L:** Default model is **YOLOv6n**, compiled for the Hailo-8L.
- **Hailo-8:** Default model is **YOLOv6n**, compiled for the Hailo-8.
- **Hailo-8R:** Default model is the **Hailo-8** build of **YOLOv6n**, since the Hailo Model Zoo publishes no Hailo-8R build.
In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms, with dual PCIe lanes, yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
docs/docs/frigate/installation.md
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@@ -122,7 +122,7 @@ Additionally, the USB Coral draws a considerable amount of power. If using any o
### Hailo-8
The Hailo-8 and Hailo-8L AI accelerators are available in both M.2 and HAT form factors for the Raspberry Pi. The M.2 version typically connects to a carrier board for PCIe, which then interfaces with the Raspberry Pi 5 as part of the AI Kit. The HAT version can be mounted directly onto compatible Raspberry Pi models. Both form factors have been successfully tested on x86 platforms as well, making them versatile options for various computing environments.
The Hailo-8, Hailo-8L and Hailo-8R AI accelerators are available in both M.2 and HAT form factors for the Raspberry Pi. The M.2 version typically connects to a carrier board for PCIe, which then interfaces with the Raspberry Pi 5 as part of the AI Kit. The HAT version can be mounted directly onto compatible Raspberry Pi models. Both form factors have been successfully tested on x86 platforms as well, making them versatile options for various computing environments.
The HailoRT runtime is not part of the Frigate image; Frigate downloads and installs it at first start once a Hailo detector is configured. Containers without internet access can provide the files themselves, see [Detector runtimes](/frigate/network_requirements#detector-runtimes).
@@ -300,7 +300,7 @@ If you are using `docker run`, add this option to your command `--device /dev/ha
#### Configuration
Finally, configure [hardware object detection](/configuration/object_detectors#hailo-8) to complete the setup.
Finally, configure [hardware object detection](/configuration/object_detectors#hailo) to complete the setup.
### MemryX MX3
docs/docs/frigate/network_requirements.md
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@@ -47,7 +47,7 @@ If you are using one of the following hardware detectors and have not provided y
| Detector | Model Downloaded | Source |
| ------------------------------------------------------------------ | -------------------- | ------------------------ |
| [Rockchip RKNN](/configuration/object_detectors#rockchip-platform) | RKNN detection model | GitHub |
| [Hailo 8 / 8L](/configuration/object_detectors#hailo-8) | YOLOv6n (.hef) | Hailo Model Zoo (AWS S3) |
| [Hailo 8 / 8L / 8R](/configuration/object_detectors#hailo) | YOLOv6n (.hef) | Hailo Model Zoo (AWS S3) |
| [AXERA AXEngine](/configuration/object_detectors) | Detection model | HuggingFace |
:::note
@@ -60,16 +60,16 @@ The default CPU, EdgeTPU, and OpenVINO object detection models are bundled into
The SDKs for a few hardware detectors are not shipped in the Frigate image. They are downloaded the first time that detector is configured, verified against checksums pinned in the Frigate release, and installed into the Frigate user's home directory (`/config/.local` by default). Once installed they are not downloaded again until a Frigate release pins a new version.
| Detector | Version | Files | Source |
| -------------------------------------------------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
| [Hailo 8 / 8L](/configuration/object_detectors#hailo-8) | 4.21.0 | `hailort-debian12-amd64.tar.gz` and `hailort-4.21.0-cp311-cp311-linux_x86_64.whl` on x86, `hailort-debian12-arm64.tar.gz` and `hailort-4.21.0-cp311-cp311-linux_aarch64.whl` on arm64 | [GitHub release](https://github.com/frigate-nvr/hailort/releases/tag/v4.21.0) |
| [MemryX MX3](/configuration/object_detectors#memryx-mx3) | 2.1.0 | `mx_accl_frigate-2.1.0.zip` (the release source archive, renamed) | [GitHub archive](https://github.com/memryx/mx_accl_frigate/archive/refs/tags/v2.1.0.zip) |
| [AXERA AXEngine](/configuration/object_detectors#axera) | 0.1.3 | `axengine-0.1.3-py3-none-any.whl` | [GitHub release](https://github.com/AXERA-TECH/pyaxengine/releases/tag/0.1.3-frigate) |
| Detector | Version | Files | Source |
| ---------------------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- |
| [Hailo 8 / 8L / 8R](/configuration/object_detectors#hailo) | 4.21.0 | `hailort-debian12-amd64.tar.gz` and `hailort-4.21.0-cp311-cp311-linux_x86_64.whl` on x86, `hailort-debian12-arm64.tar.gz` and `hailort-4.21.0-cp311-cp311-linux_aarch64.whl` on arm64 | [GitHub release](https://github.com/frigate-nvr/hailort/releases/tag/v4.21.0) |
| [MemryX MX3](/configuration/object_detectors#memryx-mx3) | 2.1.0 | `mx_accl_frigate-2.1.0.zip` (the release source archive, renamed) | [GitHub archive](https://github.com/memryx/mx_accl_frigate/archive/refs/tags/v2.1.0.zip) |
| [AXERA AXEngine](/configuration/object_detectors#axera) | 0.1.3 | `axengine-0.1.3-py3-none-any.whl` | [GitHub release](https://github.com/AXERA-TECH/pyaxengine/releases/tag/0.1.3-frigate) |
If the container cannot reach GitHub, provide the files yourself:
1. Download the files for your architecture on a machine with internet access.
2. Place them, with exactly the file names listed above, in `/config/model_cache/runtimes/<detector>/`, where `<detector>` is the detector `type` from your config (`hailo8l`, `memryx`, or `axengine`).
2. Place them, with exactly the file names listed above, in `/config/model_cache/runtimes/<detector>/`, where `<detector>` is the detector named in your config's `devices` (`hailo`, `memryx`, or `axengine`).
3. Start Frigate. Files whose checksum matches are installed without any download; a file with the wrong checksum is discarded and downloaded again, so a failed startup log names the file to replace.
The `GITHUB_ENDPOINT` mirror variable below applies to these downloads as well.
docs/docs/plus/index.md
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@@ -41,7 +41,7 @@ Rockchip models are automatically converted as of 0.17. For 0.16, YOLOv9 onnx mo
## Supported detector types
Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo8l`), and Rockchip (`rknn`) detectors.
Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo`), and Rockchip (`rknn`) detectors.
| Hardware | Recommended Detector Type | Recommended Model Type |
| -------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
@@ -50,7 +50,7 @@ Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVi
| [Intel](/configuration/object_detectors.md#openvino-detector) | `openvino` | `yolov9` |
| [NVidia GPU](/configuration/object_detectors#onnx) | `onnx` | `yolov9` |
| [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector) | `onnx` | `yolov9` |
| [Hailo8/Hailo8L/Hailo8R](/configuration/object_detectors#hailo-8) | `hailo8l` | `yolov9` |
| [Hailo8/Hailo8L/Hailo8R](/configuration/object_detectors#hailo) | `hailo` | `yolov9` |
| [Rockchip NPU](/configuration/object_detectors#rockchip-platform) | `rknn` | `yolov9` |
## Improving your model