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Update docs for rockchip platform (#11503)

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* improve docs for rockchip

* update version info

* fix typo

* fix typo

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* fix typo

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
Marc Altmann
2024-05-25 19:27:44 +02:00
committed by GitHub
parent b97e274b6a
commit adb043e7ae
4 changed files with 68 additions and 63 deletions
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37
docs/docs/configuration/object_detectors.md
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@@ -313,39 +313,11 @@ Hardware accelerated object detection is supported on the following SoCs:
- RK3576
- RK3588
This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/) Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.0.0.beta0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
### Prerequisites
Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and rknpu driver. To check, enter the following commands:
```
$ uname -r
5.10.xxx-rockchip # or 6.1.xxx; the -rockchip suffix is important
$ ls /dev/dri
by-path card0 card1 renderD128 renderD129 # should list renderD129
$ sudo cat /sys/kernel/debug/rknpu/version
RKNPU driver: v0.9.2 # or later version
```
I recommend [Joshua Riek's Ubuntu for Rockchip](https://github.com/Joshua-Riek/ubuntu-rockchip), if your board is supported.
### Setup
Follow Frigate's default installation instructions, but use a docker image with `-rk` suffix for example `ghcr.io/blakeblackshear/frigate:stable-rk`.
Next, you need to grant docker permissions to access your hardware:
- During the configuration process, you should run docker in privileged mode to avoid any errors due to insufficient permissions. To do so, add `privileged: true` to your `docker-compose.yml` file or the `--privileged` flag to your docker run command.
- After everything works, you should only grant necessary permissions to increase security. Add the lines below to your `docker-compose.yml` file or the following options to your docker run command: `--security-opt systempaths=unconfined --security-opt apparmor=unconfined --device /dev/dri:/dev/dri`:
```yaml
security_opt:
- apparmor=unconfined
- systempaths=unconfined
devices:
- /dev/dri:/dev/dri
```
Make sure to follow the [Rockchip specific installation instrucitions](/frigate/installation#rockchip-platform).
### Configuration
@@ -405,6 +377,5 @@ $ cat /sys/kernel/debug/rknpu/load
:::
- By default the rknn detector uses the yolonas_s model (`model: path: default-fp16-yolonas_s`). This model comes with the image, so no further steps than those mentioned above are necessary and no download happens.
- The other choices are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
- Finally, you can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
- All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
- You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.