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.

Watch: Ultralytics YOLOv8 Deployment and Integrations

Datasets Integrations

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

Training Integrations

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

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

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

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

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

• 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.

• Amazon SageMaker: Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle.

Deployment Integrations

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

• Gradio 🚀 NEW: Deploy Ultralytics models with Gradio for real-time, interactive object detection demos.

• TorchScript: Developed as part of the PyTorch framework, TorchScript enables efficient execution and deployment of machine learning models in various production environments without the need for Python dependencies.

• ONNX: An open-source format created by Microsoft for facilitating the transfer of AI models between various frameworks, enhancing the versatility and deployment flexibility of Ultralytics models.

• OpenVINO: Intel's toolkit for optimizing and deploying computer vision models efficiently across various Intel CPU and GPU platforms.

• TensorRT: Developed by NVIDIA, this high-performance deep learning inference framework and model format optimizes AI models for accelerated speed and efficiency on NVIDIA GPUs, ensuring streamlined deployment.

• CoreML: CoreML, developed by Apple, is a framework designed for efficiently integrating machine learning models into applications across iOS, macOS, watchOS, and tvOS, using Apple's hardware for effective and secure model deployment.

• TF SavedModel: Developed by Google, TF SavedModel is a universal serialization format for TensorFlow models, enabling easy sharing and deployment across a wide range of platforms, from servers to edge devices.

• TF GraphDef: Developed by Google, GraphDef is TensorFlow's format for representing computation graphs, enabling optimized execution of machine learning models across diverse hardware.

• TFLite: Developed by Google, TFLite is a lightweight framework for deploying machine learning models on mobile and edge devices, ensuring fast, efficient inference with minimal memory footprint.

• TFLite Edge TPU: Developed by Google for optimizing TensorFlow Lite models on Edge TPUs, this model format ensures high-speed, efficient edge computing.

• PaddlePaddle: An open-source deep learning platform by Baidu, PaddlePaddle enables the efficient deployment of AI models and focuses on the scalability of industrial applications.

• NCNN: Developed by Tencent, NCNN is an efficient neural network inference framework tailored for mobile devices. It enables direct deployment of AI models into apps, optimizing performance across various mobile platforms.

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, half, int8
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 🙏!