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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.

Ultralytics YOLO ecosystem and integrations

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.

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 - -
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 🙏!

Created 2023-11-12, Updated 2024-06-10
Authors: glenn-jocher (15), Burhan-Q (5), abirami-vina (14), RizwanMunawar (1)