--- license: agpl-3.0 tags: - rknn - rknn2 - rockchip - rk3576 - rk3588 --- # YOLOv8-RKNN2-Ultralytics > **Looking for the Rockchip Model Zoo and custom exports?** See [YOLOv8-RKNN2](https://huggingface.co/HanzoHuang/YOLOv8-RKNN2). 100 FP16 and INT8 YOLOv8 exports for **RK3576** and **RK3588**, produced with the Ultralytics export workflow and task-specific RKNN conversion tools. Supported tasks include classification, detection, oriented bounding boxes, pose, and instance segmentation. ## Choose a model Models are organized by toolkit version, task, variant, target chip, and precision: ```text models/v///// ``` Download `model.rknn` and `metadata.yaml` from the same directory. The [manifest](manifest.json) lists the available models and checksums. ## INT8 detection Use the `metadata.yaml` from the same model directory. INT8 detection outputs normalized box coordinates and requires `coordinates: normalized`. Import both files together in the warehouse, or keep them in the same directory for the CLI. Treating normalized coordinates as pixels can result in no boxes. ## Reproduce the conversion The [source directory](tree/main/source) contains the original checkpoints, conversion scripts, and environment and calibration records. Conversion used [RKNN Toolkit2 2.3.2](https://github.com/airockchip/rknn-toolkit2); upstream Ultralytics source is available on [PyPI](https://pypi.org/project/ultralytics/#history). ```bash python source/convert-model.py --help ``` For INT8 detection, pass a COCO128 dataset YAML. For other INT8 tasks, pass a COCO128 image list. Use `--calibration-limit 128` and the input size listed in the manifest. ## Source and license The inputs are official Ultralytics checkpoints. Conversion changes the format and quantizes the INT8 graphs. The RKNN files and original weights are distributed under AGPL-3.0; see [LICENSE](LICENSE) and the [checkpoint URLs and hashes](source/weights.json).