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update config

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MarcA711 2024-05-20 11:26:27 +00:00
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docs/docs/configuration/object_detectors.md
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@ -306,6 +306,7 @@ To verify that the integration is working correctly, start Frigate and observe t
## Rockchip platform
Hardware accelerated object detection is supported on the following SoCs:
- RK3562
- RK3566
- RK3568
@ -334,20 +335,21 @@ I recommend [Joshua Riek's Ubuntu for Rockchip](https://github.com/Joshua-Riek/u
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:
security_opt:
- apparmor=unconfined
- systempaths=unconfined
devices:
devices:
- /dev/dri:/dev/dri
```
### Configuration
This `config.yml` shows all relevant options to configure the detector and explains them. All values shown are the default values (except for one). Lines that are required at least to use the detector are labeled as required, all other lines are optional.
This `config.yml` shows all relevant options to configure the detector and explains them. All values shown are the default values (except for two). Lines that are required at least to use the detector are labeled as required, all other lines are optional.
```yaml
detectors: # required
@ -361,11 +363,11 @@ detectors: # required
model: # required
# name of model (will be automatically downloaded) or path to your own .rknn model file
# possible values are:
# - default-fp16-yolonas_s
# - default-fp16-yolonas_m
# - default-fp16-yolonas_l
# - deci-fp16-yolonas_s
# - deci-fp16-yolonas_m
# - deci-fp16-yolonas_l
# - /config/model_cache/your_custom_model.rknn
path: default-fp16-yolonas_s
path: deci-fp16-yolonas_s
# width and height of detection frames
width: 320
height: 320
@ -374,13 +376,18 @@ model: # required
input_pixel_format: bgr # required
# shape of detection frame
input_tensor: nhwc
model_type: yolonas # required
```
### Choosing a model
| Model | Size in mb | Inference Time |
| ------- | ---------- | -------------- |
| | | |
The inference time was determined on a rk3588 with 3 NPU cores.
| Model | Size in mb | Inference time in ms |
| ------------------- | ---------- | -------------------- |
| deci-fp16-yolonas_s | 24 | 25 |
| deci-fp16-yolonas_m | 62 | 35 |
| deci-fp16-yolonas_l | 81 | 45 |
:::tip