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---
license: other
license_name: upstream-terms-by-artifact
license_link: https://huggingface.co/HanzoHuang/YOLOX-RKNN2/blob/main/LICENSES.md
pipeline_tag: object-detection
tags:
- rknn
- rknn2
- rockchip
- rk3576
- rk3588
- yolox
---

# YOLOX-RKNN2

> **Looking for Ultralytics RKNN exports?** Browse the [Ultralytics exports collection](https://huggingface.co/collections/HanzoHuang/ultralytics-rknn-exports-6af1d088ba04ccf2dcbf81c2).

Rockchip Model Zoo conversions of YOLOX Nano for **RK3576** and **RK3588**, with FP16 and INT8 variants.

## Choose a model

For Toolkit 2.5.0, select a directory under `models/v2.5.0/detect/yolox_nano/<chip>/<precision>/` and download `model.rknn` and `metadata.yaml` together. The [manifest](manifest.json) records model hashes.

- [Toolkit 2.5.0 builds](tree/main/models/v2.5.0) use RKNN Toolkit2 2.5.0. Conversion receipts identify the original ONNX model and converter.
- [Toolkit 2.3.2 builds](tree/main/models/v2.3.2) were tested on their target board. Each receipt identifies its source graph and compiler.

## Conversion and license

The [original YOLOX Nano checkpoint](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_nano.pth) is included in [source](tree/main/source). It was exported to ONNX with the [Rockchip YOLOX exporter](https://github.com/airockchip/YOLOX/tree/fc69c1e2ccb30fce7e18250bddc13a68090dca09) and compiled with the [Model Zoo YOLOX converter](https://github.com/airockchip/rknn_model_zoo/tree/837db8e992815ff8647eadb709f07067f4bc3a71/examples/yolox) and RKNN Toolkit2 2.5.0. The INT8 variant was quantized during conversion.

YOLOX is from [Megvii-BaseDetection](https://github.com/Megvii-BaseDetection/YOLOX). See [LICENSE](LICENSE) and [source and license notices](LICENSES.md) for the applicable terms.