ultralytics/docs/en/integrations/index.md

comments	description	keywords
true	Explore Ultralytics integrations with tools for dataset management, model optimization, ML workflows automation, experiment tracking, version control, and more. Learn about our support for various model export formats for deployment.	Ultralytics integrations, Roboflow, Neural Magic, ClearML, Comet ML, DVC, Ultralytics HUB, MLFlow, Neptune, Ray Tune, TensorBoard, W&B, model export formats, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TF Lite, TF Edge TPU, TF.js, PaddlePaddle, NCNN

Ultralytics Integrations

Welcome to the Ultralytics Integrations page! This page provides an overview of our partnerships with various tools and platforms, designed to streamline your machine learning workflows, enhance dataset management, simplify model training, and facilitate efficient deployment.

Datasets Integrations

• Roboflow: Facilitate seamless dataset management for Ultralytics models, offering robust annotation, preprocessing, and augmentation capabilities.

Training Integrations

• Comet ML: Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.

• ClearML: Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.

• DVC: Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively.

• Ultralytics HUB: Access and contribute to a community of pre-trained Ultralytics models.

• MLFlow: Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment.

• Neptune: Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps.

• Ray Tune: Optimize the hyperparameters of your Ultralytics models at any scale.

• TensorBoard: Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.

• Weights & Biases (W&B): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.

Deployment Integrations

• Neural Magic: Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.

Export Formats

We also support a variety of model export formats for deployment in different environments. Here are the available formats:

Format	format Argument	Model	Metadata	Arguments
PyTorch	-	yolov8n.pt	✅	-
TorchScript	torchscript	yolov8n.torchscript	✅	imgsz, optimize
ONNX	onnx	yolov8n.onnx	✅	imgsz, half, dynamic, simplify, opset
OpenVINO	openvino	yolov8n_openvino_model/	✅	imgsz, half, int8
TensorRT	engine	yolov8n.engine	✅	imgsz, half, dynamic, simplify, workspace
CoreML	coreml	yolov8n.mlpackage	✅	imgsz, half, int8, nms
TF SavedModel	saved_model	yolov8n_saved_model/	✅	imgsz, keras, int8
TF GraphDef	pb	yolov8n.pb	❌	imgsz
TF Lite	tflite	yolov8n.tflite	✅	imgsz, half, int8
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n_web_model/	✅	imgsz
PaddlePaddle	paddle	yolov8n_paddle_model/	✅	imgsz
NCNN	ncnn	yolov8n_ncnn_model/	✅	imgsz, half

Explore the links to learn more about each integration and how to get the most out of them with Ultralytics.

Contribute to Our Integrations

We're always excited to see how the community integrates Ultralytics YOLO with other technologies, tools, and platforms! If you have successfully integrated YOLO with a new system or have valuable insights to share, consider contributing to our Integrations Docs.

By writing a guide or tutorial, you can help expand our documentation and provide real-world examples that benefit the community. It's an excellent way to contribute to the growing ecosystem around Ultralytics YOLO.

To contribute, please check out our Contributing Guide for instructions on how to submit a Pull Request (PR) 🛠️. We eagerly await your contributions!

Let's collaborate to make the Ultralytics YOLO ecosystem more expansive and feature-rich 🙏!