ultralytics/docs/en/integrations/index.md

comments	description	keywords
true	Discover Ultralytics integrations for streamlined ML workflows, dataset management, optimized model training, and robust deployment solutions.	Ultralytics, machine learning, ML workflows, dataset management, model training, model deployment, Roboflow, ClearML, Comet ML, DVC, MLFlow, Ultralytics HUB, Neptune, Ray Tune, TensorBoard, Weights & Biases, Amazon SageMaker, Paperspace Gradient, Google Colab, Neural Magic, Gradio, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TFLite, TFLite 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.

• Paperspace Gradient: Paperspace Gradient simplifies working on YOLOv8 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.

• Google Colab: Use Google Colab to train and evaluate Ultralytics models in a cloud-based environment that supports collaboration and sharing.

• Kaggle: Explore how you can use Kaggle to train and evaluate Ultralytics models in a cloud-based environment with pre-installed libraries, GPU support, and a vibrant community for collaboration and sharing.

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.

• TF.js: Developed by Google to facilitate machine learning in browsers and Node.js, TF.js allows JavaScript-based deployment of ML models.

• 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, batch
ONNX	onnx	yolov8n.onnx	✅	imgsz, half, dynamic, simplify, opset, batch
OpenVINO	openvino	yolov8n_openvino_model/	✅	imgsz, half, int8, batch
TensorRT	engine	yolov8n.engine	✅	imgsz, half, dynamic, simplify, workspace, int8, batch
CoreML	coreml	yolov8n.mlpackage	✅	imgsz, half, int8, nms, batch
TF SavedModel	saved_model	yolov8n_saved_model/	✅	imgsz, keras, int8, batch
TF GraphDef	pb	yolov8n.pb	❌	imgsz, batch
TF Lite	tflite	yolov8n.tflite	✅	imgsz, half, int8, batch
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n_web_model/	✅	imgsz, half, int8, batch
PaddlePaddle	paddle	yolov8n_paddle_model/	✅	imgsz, batch
NCNN	ncnn	yolov8n_ncnn_model/	✅	imgsz, half, batch

Explore the links to learn more about each integration and how to get the most out of them with Ultralytics. See full export details in the Export page.

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 🙏!

FAQ

What is Ultralytics HUB, and how does it streamline the ML workflow?

Ultralytics HUB is a cloud-based platform designed to make machine learning (ML) workflows for Ultralytics models seamless and efficient. By using this tool, you can easily upload datasets, train models, perform real-time tracking, and deploy YOLOv8 models without needing extensive coding skills. You can explore the key features on the Ultralytics HUB page and get started quickly with our Quickstart guide.

How do I integrate Ultralytics YOLO models with Roboflow for dataset management?

Integrating Ultralytics YOLO models with Roboflow enhances dataset management by providing robust tools for annotation, preprocessing, and augmentation. To get started, follow the steps on the Roboflow integration page. This partnership ensures efficient dataset handling, which is crucial for developing accurate and robust YOLO models.

Can I track the performance of my Ultralytics models using MLFlow?

Yes, you can. Integrating MLFlow with Ultralytics models allows you to track experiments, improve reproducibility, and streamline the entire ML lifecycle. Detailed instructions for setting up this integration can be found on the MLFlow integration page. This integration is particularly useful for monitoring model metrics and managing the ML workflow efficiently.

What are the benefits of using Neural Magic for YOLOv8 model optimization?

Neural Magic optimizes YOLOv8 models by leveraging techniques like Quantization Aware Training (QAT) and pruning, resulting in highly efficient, smaller models that perform better on resource-limited hardware. Check out the Neural Magic integration page to learn how to implement these optimizations for superior performance and leaner models. This is especially beneficial for deployment on edge devices.

How do I deploy Ultralytics YOLO models with Gradio for interactive demos?

To deploy Ultralytics YOLO models with Gradio for interactive object detection demos, you can follow the steps outlined on the Gradio integration page. Gradio allows you to create easy-to-use web interfaces for real-time model inference, making it an excellent tool for showcasing your YOLO model's capabilities in a user-friendly format suitable for both developers and end-users.

By addressing these common questions, we aim to improve user experience and provide valuable insights into the powerful capabilities of Ultralytics products. Incorporating these FAQs will not only enhance the documentation but also drive more organic traffic to the Ultralytics website.