Note
This is the latest version of tools CLI. If you are looking for the tools web application, please refer to the web-app branch.
This is a command-line tool that simplifies the conversion process of YOLO models. It supports the conversion of YOLOs ranging from V5 through V11 and Gold Yolo including oriented bounding boxes object detection (OBB), pose estimation, and instance segmentation variants of YOLOv8 and YOLO11 to ONNX format.
Warning
Please note that for the moment, we support conversion of YOLOv9 weights only from Ultralytics.
You can either export a model stored on the cloud (e.g. S3) or locally. You can choose to install the toolkit through pip or using Docker. In the sections below, we'll describe both options.
# Install the package
pip install tools@git+https://github.com/luxonis/tools.git@main
# Running the package
tools yolov6nr4.pt --imgsz "416"This option requires you to have Docker installed on your device. Additionally, to export a local model, please put it inside a shared-component/models/ folder in the root folder of the project.
# Cloning the tools repository and all submodules
git clone --recursive https://github.com/luxonis/tools.git
# Change folder
cd tools# Building Docker image
docker build -t tools_cli .
# Running the image
docker run -v "${PWD}/shared_with_container:/app/shared_with_container" tools_cli shared_with_container/models/yolov8n-seg.pt --imgsz "416"# Building Docker image
docker compose build
# Running the image
docker compose run tools_cli shared_with_container/models/yolov6nr4.ptThe output files are going to be in shared-component/output folder.
model: str= Path to the model.imgsz: str= Image input shape in the formatwidth heightorwidth. Default value"416 416".version: Optional[str]= Version of the YOLO model. Default valueNone. If not specified, the version will be detected automatically. Supported versions:yolov5,yolov6r1,yolov6r3,yolov6r4,yolov7,yolov8,yolov9,yolov10,yolov11,goldyolo.use_rvc2: bool= Whether to export for RVC2 or RVC3 devices. Default valueTrue.class_names: Optional[str]= Optional list of classes separated by a comma, e.g."person, dog, cat"output_remote_url: Optional[str]= Remote output url for the output .onnx model.config_path: Optional[str]= Optional path to an optional config.put_file_plugin: Optional[str]= Which plugin to use. Optional.
Currently, the following models are supported:
| Model Version | Supported versions |
|---|---|
yolov5 |
YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, YOLOv5n6, YOLOv5s6, YOLOv5m6, YOLOv5l6 |
yolov6r1 |
v1.0 release: YOLOv6n, YOLOv6t, YOLOv6s |
yolov6r3 |
v2.0 release: YOLOv6n, YOLOv6t, YOLOv6s, YOLOv6m, YOLOv6l v2.1 release: YOLOv6n, YOLOv6s, YOLOv6m, YOLOv6l v3.0 release: YOLOv6n, YOLOv6s, YOLOv6m, YOLOv6l |
yolov6r4 |
v4.0 release: YOLOv6n, YOLOv6s, YOLOv6m, YOLOv6l |
yolov7 |
YOLOv7-tiny, YOLOv7, YOLOv7x |
yolov8 |
Detection: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x, YOLOv3-tinyu, YOLOv5nu, YOLOv5n6u, YOLOv5s6u, YOLOv5su, YOLOv5m6u, YOLOv5mu, YOLOv5l6u, YOLOv5lu Instance Segmentation, Pose, Oriented Detection, Classification: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x |
yolov9 |
YOLOv9t, YOLOv9s, YOLOv9m, YOLOv9c |
yolov10 |
YOLOv10n, YOLOv10s, YOLOv10m, YOLOv10b, YOLOv10l, YOLOv10x |
yolov11 |
Detection, Instance Segmentation, Pose, Oriented Detection, Classification: YOLO11n, YOLO11s, YOLO11m, YOLO11l, YOLO11x |
goldyolo |
Gold-YOLO-N, Gold-YOLO-S, Gold-YOLO-M, Gold-YOLO-L |
If you don't find your model in the list, it is possible that it can be converted, however, this is not guaranteed.
This application uses source code of the following repositories: YOLOv5, YOLOv6, GoldYOLO YOLOv7, and Ultralytics (see each of them for more information).
This application is available under AGPL-3.0 License license (see LICENSE file for details).
We welcome contributions! Whether it's reporting bugs, adding features or improving documentation, your help is much appreciated. Please create a pull request (here's how to do it) and assign anyone from the Luxonis team to review the suggested changes. Cheers!