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update rknn toolkit version (#17877)

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Marc Altmann
2025-04-23 22:30:44 +02:00
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parent 212784b68e
commit 4c749e3004
3 changed files with 5 additions and 5 deletions
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docs/docs/configuration/object_detectors.md
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@@ -811,7 +811,7 @@ 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/), version v2.3.0. 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 v2.3.2.
### Prerequisites
@@ -953,7 +953,7 @@ Explanation of the paramters:
- `soc`: the SoC this model was build for (e.g. "rk3588")
- `tk_version`: Version of `rknn-toolkit2` (e.g. "2.3.0")
- **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.0_EN.pdf).
- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
# Models