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# 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 YOLO11 Deployment and Integrations

## Training Integrations

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

- 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.

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

- IBM Watsonx: See how IBM Watsonx simplifies the training and evaluation of Ultralytics models with its cutting-edge AI tools, effortless integration, and advanced model management system.

- JupyterLab: Find out how to use JupyterLab's interactive and customizable environment to train and evaluate Ultralytics models with ease and efficiency.

- 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.

- 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.

- Paperspace Gradient: Paperspace Gradient simplifies working on YOLO11 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.

- 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.

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

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

- VS Code: An extension for VS Code that provides code snippets for accelerating development workflows with Ultralytics and also for anyone looking for examples to help learn or get started with Ultralytics.

- Albumentations: Enhance your Ultralytics models with powerful image augmentations to improve model robustness and generalization.

## Deployment Integrations

- 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.

- MNN: Developed by Alibaba, MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models and has industry-leading performance for inference and training on-device.

- 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.

- SONY IMX500 🚀 NEW: Optimize and deploy Ultralytics YOLOv8 models on Raspberry Pi AI Cameras with the IMX500 sensor for fast, low-power performance.

- Rockchip RKNN: Developed by Rockchip, RKNN is a specialized neural network inference framework optimized for Rockchip's hardware platforms, particularly their NPUs. It facilitates efficient deployment of AI models on edge devices, enabling high-performance inference in real-time applications.

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

- Seeed Studio reCamera: Developed by Seeed Studio, the reCamera is a cutting-edge edge AI device designed for real-time computer vision applications. Powered by the RISC-V-based SG200X processor, it delivers high-performance AI inference with energy efficiency. Its modular design, advanced video processing capabilities, and support for flexible deployment make it an ideal choice for various use cases, including safety monitoring, environmental applications, and manufacturing.

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

## Datasets Integrations

- Roboflow: Facilitate dataset labelling and management for Ultralytics models, offering annotation tools to label images.

### 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 | - | `yolo11n.pt` | ✅ | -
TorchScript | `torchscript` | `yolo11n.torchscript` | ✅ | `imgsz`, `half`, `dynamic`, `optimize`, `nms` , `batch`, `device`
ONNX | `onnx` | `yolo11n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `nms` , `batch`, `device`
OpenVINO | `openvino` | `yolo11n_openvino_model/` | ✅ | `imgsz`, `half`, `dynamic`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TensorRT | `engine` | `yolo11n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
CoreML | `coreml` | `yolo11n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
TF SavedModel | `saved_model` | `yolo11n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `nms` , `batch`, `device`
TF GraphDef | `pb` | `yolo11n.pb` | ❌ | `imgsz`, `batch`, `device`
TF Lite | `tflite` | `yolo11n.tflite` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TF Edge TPU | `edgetpu` | `yolo11n_edgetpu.tflite` | ✅ | `imgsz`, `device`
TF.js | `tfjs` | `yolo11n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
PaddlePaddle | `paddle` | `yolo11n_paddle_model/` | ✅ | `imgsz`, `batch`, `device`
MNN | `mnn` | `yolo11n.mnn` | ✅ | `imgsz`, `batch`, `int8`, `half`, `device`
NCNN | `ncnn` | `yolo11n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch`, `device`
IMX500 | `imx` | `yolo11n_imx_model/` | ✅ | `imgsz`, `int8`, `data`, `fraction`, `device`
RKNN | `rknn` | `yolo11n_rknn_model/` | ✅ | `imgsz`, `batch`, `name`, `device`

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 workflows for Ultralytics models seamless and efficient. By using this tool, you can easily upload datasets, train models, perform real-time tracking, and deploy YOLO models without needing extensive coding skills. The platform serves as a centralized workspace where you can manage your entire ML pipeline from data preparation to deployment. You can explore the key features on the Ultralytics HUB page and get started quickly with our Quickstart guide.

### 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, comparing different training runs, and managing the ML workflow efficiently. MLFlow provides a centralized platform to log parameters, metrics, and artifacts, making it easier to understand model behavior and make data-driven improvements.

### What are the benefits of using Neural Magic for YOLO11 model optimization?

Neural Magic optimizes YOLO11 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 where computational resources are constrained. Neural Magic's DeepSparse engine can deliver up to 6x faster inference on CPUs, making it possible to run complex models without specialized hardware.

### 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. With just a few lines of code, you can build interactive applications that demonstrate your model's performance on custom inputs, facilitating better understanding and evaluation of your computer vision solutions.

📅 Created 1 year ago ✏️ Updated 26 days ago

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