license: agpl-3.0
pipeline_tag: object-detection
tags:
- rknn
- rknn2
- rockchip
- yolov10
- rk3576
- rk3588
YOLOv10-RKNN2-Ultralytics
Looking for the Rockchip Model Zoo and custom exports? See YOLOv10-RKNN2.
RKNN exports of Ultralytics YOLOv10 for RK3576 and RK3588, compiled with RKNN Toolkit2 2.3.2. Detection variants n, s, m, b, l, and x are available in FP16 and INT8.
Choose a model
Models are organized by toolkit version, task, variant, target chip, and precision:
models/v<toolkit-version>/<task>/<variant>/<chip>/<precision>/
Download model.rknn and metadata.yaml from the same directory. The manifest 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 contains the input checkpoints, conversion scripts, package versions, and calibration hashes. The export used Ultralytics 8.4.184 and RKNN Toolkit2 2.3.2.
python source/convert-model.py source/weights/yolov10n.pt --chip rk3588 --precision fp16 --imgsz 640 --output yolov10n-rk3588
For INT8, use --precision int8 --data /path/to/coco128.yaml --calibration-limit 128. Point the YAML at the extracted COCO128 tree and its train2017 image directory. The calibration manifest links to the source images.
Source and license
The inputs are official Ultralytics checkpoints. Conversion changes the format and quantizes the INT8 graph. The RKNN exports and original checkpoints are distributed under AGPL-3.0; see LICENSE and checkpoint origins.