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docs/en/integrations/index.md
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@ -27,65 +27,65 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
## Training Integrations ## Training Integrations
- [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle.
- [ClearML](clearml.md): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration. - [ClearML](clearml.md): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.
- [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments. - [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
- [DVC](dvc.md): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively. - [DVC](dvc.md): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively.
- [Google Colab](google-colab.md): Use Google Colab to train and evaluate Ultralytics models in a cloud-based environment that supports collaboration and sharing.
- [IBM Watsonx](ibm-watsonx.md): 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](jupyterlab.md): Find out how to use JupyterLab's interactive and customizable environment to train and evaluate Ultralytics models with ease and efficiency.
- [Kaggle](kaggle.md): 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](mlflow.md): Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment. - [MLFlow](mlflow.md): Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment.
- [Ultralytics HUB](https://hub.ultralytics.com/): Access and contribute to a community of pre-trained Ultralytics models.
- [Neptune](https://neptune.ai/): Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps. - [Neptune](https://neptune.ai/): Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps.
- [Paperspace Gradient](paperspace.md): Paperspace Gradient simplifies working on YOLO11 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.
- [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale. - [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale.
- [TensorBoard](tensorboard.md): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration. - [TensorBoard](tensorboard.md): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
- [Ultralytics HUB](https://hub.ultralytics.com/): Access and contribute to a community of pre-trained Ultralytics models.
- [Weights & Biases (W&B)](weights-biases.md): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects. - [Weights & Biases (W&B)](weights-biases.md): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.
- [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle.
- [Paperspace Gradient](paperspace.md): Paperspace Gradient simplifies working on YOLO11 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.
- [Google Colab](google-colab.md): Use Google Colab to train and evaluate Ultralytics models in a cloud-based environment that supports collaboration and sharing.
- [Kaggle](kaggle.md): 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.
- [JupyterLab](jupyterlab.md): Find out how to use JupyterLab's interactive and customizable environment to train and evaluate Ultralytics models with ease and efficiency.
- [IBM Watsonx](ibm-watsonx.md): 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.
## Deployment Integrations ## Deployment Integrations
- [Neural Magic](neural-magic.md): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size. - [CoreML](coreml.md): CoreML, developed by [Apple](https://www.apple.com/), 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](https://www.ultralytics.com/glossary/model-deployment).
- [Gradio](gradio.md) 🚀 NEW: Deploy Ultralytics models with Gradio for real-time, interactive object detection demos. - [Gradio](gradio.md) 🚀 NEW: Deploy Ultralytics models with Gradio for real-time, interactive object detection demos.
- [TorchScript](torchscript.md): Developed as part of the [PyTorch](https://pytorch.org/) framework, TorchScript enables efficient execution and deployment of machine learning models in various production environments without the need for Python dependencies. - [NCNN](ncnn.md): Developed by [Tencent](http://www.tencent.com/), NCNN is an efficient [neural network](https://www.ultralytics.com/glossary/neural-network-nn) inference framework tailored for mobile devices. It enables direct deployment of AI models into apps, optimizing performance across various mobile platforms.
- [Neural Magic](neural-magic.md): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
- [ONNX](onnx.md): An open-source format created by [Microsoft](https://www.microsoft.com/) for facilitating the transfer of AI models between various frameworks, enhancing the versatility and deployment flexibility of Ultralytics models. - [ONNX](onnx.md): An open-source format created by [Microsoft](https://www.microsoft.com/) for facilitating the transfer of AI models between various frameworks, enhancing the versatility and deployment flexibility of Ultralytics models.
- [OpenVINO](openvino.md): Intel's toolkit for optimizing and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models efficiently across various Intel CPU and GPU platforms. - [OpenVINO](openvino.md): Intel's toolkit for optimizing and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models efficiently across various Intel CPU and GPU platforms.
- [TensorRT](tensorrt.md): Developed by [NVIDIA](https://www.nvidia.com/), this high-performance [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference framework and model format optimizes AI models for accelerated speed and efficiency on NVIDIA GPUs, ensuring streamlined deployment. - [PaddlePaddle](paddlepaddle.md): An open-source deep learning platform by [Baidu](https://www.baidu.com/), PaddlePaddle enables the efficient deployment of AI models and focuses on the scalability of industrial applications.
- [CoreML](coreml.md): CoreML, developed by [Apple](https://www.apple.com/), 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](https://www.ultralytics.com/glossary/model-deployment). - [TF GraphDef](tf-graphdef.md): Developed by [Google](https://www.google.com/), GraphDef is TensorFlow's format for representing computation graphs, enabling optimized execution of machine learning models across diverse hardware.
- [TF SavedModel](tf-savedmodel.md): Developed by [Google](https://www.google.com/), TF SavedModel is a universal serialization format for [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) models, enabling easy sharing and deployment across a wide range of platforms, from servers to edge devices. - [TF SavedModel](tf-savedmodel.md): Developed by [Google](https://www.google.com/), TF SavedModel is a universal serialization format for [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) models, enabling easy sharing and deployment across a wide range of platforms, from servers to edge devices.
- [TF GraphDef](tf-graphdef.md): Developed by [Google](https://www.google.com/), GraphDef is TensorFlow's format for representing computation graphs, enabling optimized execution of machine learning models across diverse hardware. - [TF.js](tfjs.md): Developed by [Google](https://www.google.com/) to facilitate machine learning in browsers and Node.js, TF.js allows JavaScript-based deployment of ML models.
- [TFLite](tflite.md): Developed by [Google](https://www.google.com/), TFLite is a lightweight framework for deploying machine learning models on mobile and edge devices, ensuring fast, efficient inference with minimal memory footprint. - [TFLite](tflite.md): Developed by [Google](https://www.google.com/), 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](edge-tpu.md): Developed by [Google](https://www.google.com/) for optimizing TensorFlow Lite models on Edge TPUs, this model format ensures high-speed, efficient [edge computing](https://www.ultralytics.com/glossary/edge-computing). - [TFLite Edge TPU](edge-tpu.md): Developed by [Google](https://www.google.com/) for optimizing TensorFlow Lite models on Edge TPUs, this model format ensures high-speed, efficient [edge computing](https://www.ultralytics.com/glossary/edge-computing).
- [TF.js](tfjs.md): Developed by [Google](https://www.google.com/) to facilitate machine learning in browsers and Node.js, TF.js allows JavaScript-based deployment of ML models. - [TensorRT](tensorrt.md): Developed by [NVIDIA](https://www.nvidia.com/), this high-performance [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference framework and model format optimizes AI models for accelerated speed and efficiency on NVIDIA GPUs, ensuring streamlined deployment.
- [PaddlePaddle](paddlepaddle.md): An open-source deep learning platform by [Baidu](https://www.baidu.com/), PaddlePaddle enables the efficient deployment of AI models and focuses on the scalability of industrial applications. - [TorchScript](torchscript.md): Developed as part of the [PyTorch](https://pytorch.org/) framework, TorchScript enables efficient execution and deployment of machine learning models in various production environments without the need for Python dependencies.
- [NCNN](ncnn.md): Developed by [Tencent](http://www.tencent.com/), NCNN is an efficient [neural network](https://www.ultralytics.com/glossary/neural-network-nn) inference framework tailored for mobile devices. It enables direct deployment of AI models into apps, optimizing performance across various mobile platforms.
- [VS Code](vscode.md): 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. - [VS Code](vscode.md): 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.

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mkdocs.yml
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@ -392,35 +392,35 @@ nav:
- Clearml Logging: yolov5/tutorials/clearml_logging_integration.md - Clearml Logging: yolov5/tutorials/clearml_logging_integration.md
- Integrations: - Integrations:
- integrations/index.md - integrations/index.md
- TorchScript: integrations/torchscript.md - Amazon SageMaker: integrations/amazon-sagemaker.md
- ClearML: integrations/clearml.md
- Comet ML: integrations/comet.md
- CoreML: integrations/coreml.md
- DVC: integrations/dvc.md
- Google Colab: integrations/google-colab.md
- Gradio: integrations/gradio.md
- IBM Watsonx: integrations/ibm-watsonx.md
- JupyterLab: integrations/jupyterlab.md
- Kaggle: integrations/kaggle.md
- MLflow: integrations/mlflow.md
- NCNN: integrations/ncnn.md
- Neural Magic: integrations/neural-magic.md
- ONNX: integrations/onnx.md - ONNX: integrations/onnx.md
- OpenVINO: integrations/openvino.md - OpenVINO: integrations/openvino.md
- TensorRT: integrations/tensorrt.md
- CoreML: integrations/coreml.md
- TF SavedModel: integrations/tf-savedmodel.md
- TF GraphDef: integrations/tf-graphdef.md
- TFLite: integrations/tflite.md
- TFLite Edge TPU: integrations/edge-tpu.md
- TF.js: integrations/tfjs.md
- PaddlePaddle: integrations/paddlepaddle.md - PaddlePaddle: integrations/paddlepaddle.md
- NCNN: integrations/ncnn.md - Paperspace Gradient: integrations/paperspace.md
- Comet ML: integrations/comet.md
- Ray Tune: integrations/ray-tune.md - Ray Tune: integrations/ray-tune.md
- Roboflow: integrations/roboflow.md - Roboflow: integrations/roboflow.md
- MLflow: integrations/mlflow.md - TF GraphDef: integrations/tf-graphdef.md
- ClearML: integrations/clearml.md - TF SavedModel: integrations/tf-savedmodel.md
- DVC: integrations/dvc.md - TF.js: integrations/tfjs.md
- Weights & Biases: integrations/weights-biases.md - TFLite: integrations/tflite.md
- Neural Magic: integrations/neural-magic.md - TFLite Edge TPU: integrations/edge-tpu.md
- Gradio: integrations/gradio.md
- TensorBoard: integrations/tensorboard.md - TensorBoard: integrations/tensorboard.md
- Amazon SageMaker: integrations/amazon-sagemaker.md - TensorRT: integrations/tensorrt.md
- Paperspace Gradient: integrations/paperspace.md - TorchScript: integrations/torchscript.md
- Google Colab: integrations/google-colab.md
- Kaggle: integrations/kaggle.md
- JupyterLab: integrations/jupyterlab.md
- IBM Watsonx: integrations/ibm-watsonx.md
- VS Code: integrations/vscode.md - VS Code: integrations/vscode.md
- Weights & Biases: integrations/weights-biases.md
- HUB: - HUB:
- hub/index.md - hub/index.md
- Web: - Web: