license: other
license_name: upstream-terms-by-artifact
license_link: https://huggingface.co/HanzoHuang/YOLOv5-RKNN2/blob/main/LICENSES.md
pipeline_tag: image-classification
tags:
- rknn
- rknn2
- rockchip
- rk3576
- rk3588
- yolov5
YOLOv5-RKNN2
Looking for Ultralytics RKNN exports? Browse the Ultralytics exports collection.
YOLOv5 RKNN builds for RK3576 and RK3588, including Rockchip Model Zoo builds and custom conversions.
Choose a model
Models are organized under models/v<toolkit-version>/<task>/<variant>/<chip>/<precision>/. Download the model and metadata from the same directory. The manifest records output hashes.
- Toolkit 2.3.2 builds include classification, detection, and segmentation. The classification exports include n, s, and m variants in FP16 and INT8 for both chips.
- Toolkit 2.5.0 builds include detection and segmentation.
- Detection includes n, s, m, and s-ReLU variants in FP16 and INT8 for both chips. Check the conversion receipt for the source graph and compiler; Toolkit 2.3.2 builds were tested on their target board.
Conversion and license
The classification weights came from the YOLOv5 v7.0 release; their URLs and hashes are in source. The custom conversion exports ONNX and compiles it with RKNN Toolkit2. Run python source/convert-extended.py --help and use the v7.0 source checkout with --yolov5-source. INT8 uses the COCO128 calibration list in source/calibration.json.
The classification distribution includes the GPL-3.0 license from the original release. See source and license notices for artifact-specific terms and provenance.