Rockchip RKNN
RKNN-Toolkit2, RKNN-Toolkit-Lite2 and the C runtime API.
RKNN-Toolkit2 runs on an x86 Linux host and converts ONNX, TFLite, PyTorch and other models to .rknn, with INT8 quantisation. On the board you run models with RKNN-Toolkit-Lite2 (Python) or the C API (librknnrt). Older RV1109/RV1126 SDKs use the original RKNN-Toolkit (v1).
Key points
- Quantise with a representative dataset (a dataset.txt listing 20–200 real images).
- Match mean/std normalisation in rknn.config() to what the model was trained with.
- Use accuracy_analysis() to find layers that lose precision after quantisation.