Update URLs to redirects (#16048)

docs/en/integrations/clearml.md

@@ -8,7 +8,7 @@ keywords: YOLOv8, ClearML, MLOps, Ultralytics, machine learning, object detectio
 
 MLOps bridges the gap between creating and deploying machine learning models in real-world settings. It focuses on efficient deployment, scalability, and ongoing management to ensure models perform well in practical applications.
 
-[Ultralytics YOLOv8](https://ultralytics.com) effortlessly integrates with ClearML, streamlining and enhancing your object detection model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
+[Ultralytics YOLOv8](https://www.ultralytics.com/) effortlessly integrates with ClearML, streamlining and enhancing your object detection model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
 
 ## ClearML
 

docs/en/integrations/comet.md

@@ -8,7 +8,7 @@ keywords: YOLOv8, Comet ML, logging, machine learning, training, model checkpoin
 
 Logging key training details such as parameters, metrics, image predictions, and model checkpoints is essential in machine learning—it keeps your project transparent, your progress measurable, and your results repeatable.
 
-[Ultralytics YOLOv8](https://ultralytics.com) seamlessly integrates with Comet ML, efficiently capturing and optimizing every aspect of your YOLOv8 object detection model's training process. In this guide, we'll cover the installation process, Comet ML setup, real-time insights, custom logging, and offline usage, ensuring that your YOLOv8 training is thoroughly documented and fine-tuned for outstanding results.
+[Ultralytics YOLOv8](https://www.ultralytics.com/) seamlessly integrates with Comet ML, efficiently capturing and optimizing every aspect of your YOLOv8 object detection model's training process. In this guide, we'll cover the installation process, Comet ML setup, real-time insights, custom logging, and offline usage, ensuring that your YOLOv8 training is thoroughly documented and fine-tuned for outstanding results.
 
 ## Comet ML
 
@@ -16,7 +16,7 @@ Logging key training details such as parameters, metrics, image predictions, and
   <img width="640" src="https://www.comet.com/docs/v2/img/landing/home-hero.svg" alt="Comet ML Overview">
 </p>
 
-[Comet ML](https://www.comet.ml/) is a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments. It allows you to log metrics, parameters, media, and more during your model training and monitor your experiments through an aesthetically pleasing web interface. Comet ML helps data scientists iterate more rapidly, enhances transparency and reproducibility, and aids in the development of production models.
+[Comet ML](https://www.comet.com/site/) is a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments. It allows you to log metrics, parameters, media, and more during your model training and monitor your experiments through an aesthetically pleasing web interface. Comet ML helps data scientists iterate more rapidly, enhances transparency and reproducibility, and aids in the development of production models.
 
 ## Harnessing the Power of YOLOv8 and Comet ML
 

docs/en/integrations/coreml.md

@@ -113,9 +113,9 @@ Having successfully exported your Ultralytics YOLOv8 models to CoreML, the next
 
 - **[CoreML Tools](https://apple.github.io/coremltools/docs-guides/)**: This guide includes instructions and examples to convert models from TensorFlow, PyTorch, and other libraries to Core ML.
 
-- **[ML and Vision](https://developer.apple.com/videos/ml-vision)**: A collection of comprehensive videos that cover various aspects of using and implementing CoreML models.
+- **[ML and Vision](https://developer.apple.com/videos/)**: A collection of comprehensive videos that cover various aspects of using and implementing CoreML models.
 
-- **[Integrating a Core ML Model into Your App](https://developer.apple.com/documentation/coreml/integrating_a_core_ml_model_into_your_app)**: A comprehensive guide on integrating a CoreML model into an iOS application, detailing steps from preparing the model to implementing it in the app for various functionalities.
+- **[Integrating a Core ML Model into Your App](https://developer.apple.com/documentation/coreml/integrating-a-core-ml-model-into-your-app)**: A comprehensive guide on integrating a CoreML model into an iOS application, detailing steps from preparing the model to implementing it in the app for various functionalities.
 
 ## Summary
 
@@ -169,7 +169,7 @@ CoreML provides numerous advantages for deploying [Ultralytics YOLOv8](https://g
 - **Ease of Integration**: Offers a seamless integration experience with Apple's ecosystems, including iOS, macOS, watchOS, and tvOS.
 - **Versatility**: Supports a wide range of machine learning tasks such as image analysis, audio processing, and natural language processing using the CoreML framework.
 
-For more details on integrating your CoreML model into an iOS app, check out the guide on [Integrating a Core ML Model into Your App](https://developer.apple.com/documentation/coreml/integrating_a_core_ml_model_into_your_app).
+For more details on integrating your CoreML model into an iOS app, check out the guide on [Integrating a Core ML Model into Your App](https://developer.apple.com/documentation/coreml/integrating-a-core-ml-model-into-your-app).
 
 ### What are the deployment options for YOLOv8 models exported to CoreML?
 

docs/en/integrations/dvc.md

@@ -8,7 +8,7 @@ keywords: YOLOv8, DVCLive, experiment tracking, machine learning, model training
 
 Experiment tracking in machine learning is critical to model development and evaluation. It involves recording and analyzing various parameters, metrics, and outcomes from numerous training runs. This process is essential for understanding model performance and making data-driven decisions to refine and optimize models.
 
-Integrating DVCLive with [Ultralytics YOLOv8](https://ultralytics.com) transforms the way experiments are tracked and managed. This integration offers a seamless solution for automatically logging key experiment details, comparing results across different runs, and visualizing data for in-depth analysis. In this guide, we'll understand how DVCLive can be used to streamline the process.
+Integrating DVCLive with [Ultralytics YOLOv8](https://www.ultralytics.com/) transforms the way experiments are tracked and managed. This integration offers a seamless solution for automatically logging key experiment details, comparing results across different runs, and visualizing data for in-depth analysis. In this guide, we'll understand how DVCLive can be used to streamline the process.
 
 ## DVCLive
 

docs/en/integrations/edge-tpu.md

@@ -6,7 +6,7 @@ keywords: YOLOv8, TFLite Edge TPU, TensorFlow Lite, model export, machine learni
 
 # Learn to Export to TFLite Edge TPU Format From YOLOv8 Model
 
-Deploying computer vision models on devices with limited computational power, such as mobile or embedded systems, can be tricky. Using a model format that is optimized for faster performance simplifies the process. The [TensorFlow Lite](https://www.tensorflow.org/lite) [Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) or TFLite Edge TPU model format is designed to use minimal power while delivering fast performance for neural networks.
+Deploying computer vision models on devices with limited computational power, such as mobile or embedded systems, can be tricky. Using a model format that is optimized for faster performance simplifies the process. The [TensorFlow Lite](https://ai.google.dev/edge/litert) [Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) or TFLite Edge TPU model format is designed to use minimal power while delivering fast performance for neural networks.
 
 The export to TFLite Edge TPU format feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and low-power inferencing. In this guide, we'll walk you through converting your models to the TFLite Edge TPU format, making it easier for your models to perform well on various mobile and embedded devices.
 

docs/en/integrations/index.md

@@ -35,7 +35,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 
 - [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.
+- [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.
 
@@ -65,7 +65,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 
 - [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.
 
-- [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 models efficiently across various Intel CPU and GPU platforms.
 
@@ -73,15 +73,15 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 
 - [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.
 
-- [TF SavedModel](tf-savedmodel.md): Developed by [Google](https://www.google.com), 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 SavedModel](tf-savedmodel.md): Developed by [Google](https://www.google.com/), 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](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 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.
 
-- [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.
+- [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.
 
-- [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.
+- [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.
 
 - [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.
 
@@ -111,7 +111,7 @@ Let's collaborate to make the Ultralytics YOLO ecosystem more expansive and feat
 
 ### 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](https://hub.ultralytics.com) page and get started quickly with our [Quickstart](https://docs.ultralytics.com/hub/quickstart/) guide.
+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](https://hub.ultralytics.com/) page and get started quickly with our [Quickstart](https://docs.ultralytics.com/hub/quickstart/) guide.
 
 ### How do I integrate Ultralytics YOLO models with Roboflow for dataset management?
 

docs/en/integrations/mlflow.md

@@ -10,7 +10,7 @@ keywords: MLflow, Ultralytics YOLO, machine learning, experiment tracking, metri
 
 ## Introduction
 
-Experiment logging is a crucial aspect of machine learning workflows that enables tracking of various metrics, parameters, and artifacts. It helps to enhance model reproducibility, debug issues, and improve model performance. [Ultralytics](https://ultralytics.com) YOLO, known for its real-time object detection capabilities, now offers integration with [MLflow](https://mlflow.org/), an open-source platform for complete machine learning lifecycle management.
+Experiment logging is a crucial aspect of machine learning workflows that enables tracking of various metrics, parameters, and artifacts. It helps to enhance model reproducibility, debug issues, and improve model performance. [Ultralytics](https://www.ultralytics.com/) YOLO, known for its real-time object detection capabilities, now offers integration with [MLflow](https://mlflow.org/), an open-source platform for complete machine learning lifecycle management.
 
 This documentation page is a comprehensive guide to setting up and utilizing the MLflow logging capabilities for your Ultralytics YOLO project.
 

docs/en/integrations/neural-magic.md

@@ -6,7 +6,7 @@ keywords: YOLOv8, DeepSparse, Neural Magic, model optimization, object detection
 
 # Optimizing YOLOv8 Inferences with Neural Magic's DeepSparse Engine
 
-When deploying object detection models like [Ultralytics YOLOv8](https://ultralytics.com) on various hardware, you can bump into unique issues like optimization. This is where YOLOv8's integration with Neural Magic's DeepSparse Engine steps in. It transforms the way YOLOv8 models are executed and enables GPU-level performance directly on CPUs.
+When deploying object detection models like [Ultralytics YOLOv8](https://www.ultralytics.com/) on various hardware, you can bump into unique issues like optimization. This is where YOLOv8's integration with Neural Magic's DeepSparse Engine steps in. It transforms the way YOLOv8 models are executed and enables GPU-level performance directly on CPUs.
 
 This guide shows you how to deploy YOLOv8 using Neural Magic's DeepSparse, how to run inferences, and also how to benchmark performance to ensure it is optimized.
 

docs/en/integrations/ray-tune.md

@@ -10,7 +10,7 @@ Hyperparameter tuning is vital in achieving peak model performance by discoverin
 
 ## Accelerate Tuning with Ultralytics YOLOv8 and Ray Tune
 
-[Ultralytics YOLOv8](https://ultralytics.com) incorporates Ray Tune for hyperparameter tuning, streamlining the optimization of YOLOv8 model hyperparameters. With Ray Tune, you can utilize advanced search strategies, parallelism, and early stopping to expedite the tuning process.
+[Ultralytics YOLOv8](https://www.ultralytics.com/) incorporates Ray Tune for hyperparameter tuning, streamlining the optimization of YOLOv8 model hyperparameters. With Ray Tune, you can utilize advanced search strategies, parallelism, and early stopping to expedite the tuning process.
 
 ### Ray Tune
 

docs/en/integrations/roboflow.md

@@ -12,10 +12,10 @@ keywords: Roboflow, YOLOv8, data labeling, computer vision, model training, mode
 
     Ultralytics offers two licensing options:
 
-    - The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an [OSI-approved](https://opensource.org/licenses/) open-source license ideal for students and enthusiasts.
-    - The [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services.
+    - The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an [OSI-approved](https://opensource.org/license) open-source license ideal for students and enthusiasts.
+    - The [Enterprise License](https://www.ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services.
 
-    For more details see [Ultralytics Licensing](https://ultralytics.com/license).
+    For more details see [Ultralytics Licensing](https://www.ultralytics.com/license).
 
 In this guide, we are going to showcase how to find, label, and organize data for use in training a custom Ultralytics YOLOv8 model. Use the table of contents below to jump directly to a specific section:
 
@@ -27,7 +27,7 @@ In this guide, we are going to showcase how to find, label, and organize data fo
 - Upload custom YOLOv8 model weights for testing and deployment
 - Gather Data for Training a Custom YOLOv8 Model
 
-Roboflow provides two services that can help you collect data for YOLOv8 models: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://roboflow.com/collect?ref=ultralytics).
+Roboflow provides two services that can help you collect data for YOLOv8 models: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://github.com/roboflow/roboflow-collect?ref=ultralytics).
 
 Universe is an online repository with over 250,000 vision datasets totalling over 100 million images.
 
@@ -47,13 +47,13 @@ For YOLOv8, select "YOLOv8" as the export format:
 <img src="https://github.com/ultralytics/docs/releases/download/0/roboflow-universe-dataset-export-1.avif" alt="Roboflow Universe dataset export" width="800">
 </p>
 
-Universe also has a page that aggregates all [public fine-tuned YOLOv8 models uploaded to Roboflow](https://universe.roboflow.com/search?q=model:yolov8). You can use this page to explore pre-trained models you can use for testing or [for automated data labeling](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling) or to prototype with [Roboflow inference](https://roboflow.com/inference?ref=ultralytics).
+Universe also has a page that aggregates all [public fine-tuned YOLOv8 models uploaded to Roboflow](https://universe.roboflow.com/search?q=model%3Ayolov8&ref=ultralytics). You can use this page to explore pre-trained models you can use for testing or [for automated data labeling](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling?ref=ultralytics) or to prototype with [Roboflow inference](https://github.com/roboflow/inference?ref=ultralytics).
 
 If you want to gather images yourself, try [Collect](https://github.com/roboflow/roboflow-collect), an open source project that allows you to automatically gather images using a webcam on the edge. You can use text or image prompts with Collect to instruct what data should be collected, allowing you to capture only the useful data you need to build your vision model.
 
 ## Upload, Convert and Label Data for YOLOv8 Format
 
-[Roboflow Annotate](https://docs.roboflow.com/annotate/use-roboflow-annotate) is an online annotation tool for use in labeling images for object detection, classification, and segmentation.
+[Roboflow Annotate](https://docs.roboflow.com/annotate/use-roboflow-annotate?ref=ultralytics) is an online annotation tool for use in labeling images for object detection, classification, and segmentation.
 
 To label data for a YOLOv8 object detection, instance segmentation, or classification model, first create a project in Roboflow.
 
@@ -127,7 +127,7 @@ You can narrow your search to images with a particular tag using the "Tags" sele
 <img src="https://github.com/ultralytics/docs/releases/download/0/filter-images-by-tag.avif" alt="Filter images by tag" width="350">
 </p>
 
-Before you start training a model with your dataset, we recommend using Roboflow [Health Check](https://docs.roboflow.com/datasets/dataset-health-check), a web tool that provides an insight into your dataset and how you can improve the dataset prior to training a vision model.
+Before you start training a model with your dataset, we recommend using Roboflow [Health Check](https://docs.roboflow.com/datasets/dataset-health-check?ref=ultralytics), a web tool that provides an insight into your dataset and how you can improve the dataset prior to training a vision model.
 
 To use Health Check, click the "Health Check" sidebar link. A list of statistics will appear that show the average size of images in your dataset, class balance, a heatmap of where annotations are in your images, and more.
 
@@ -157,7 +157,7 @@ When your dataset version has been generated, you can export your data into a ra
 <img src="https://github.com/ultralytics/docs/releases/download/0/exporting-dataset.avif" alt="Exporting a dataset" width="800">
 </p>
 
-You are now ready to train YOLOv8 on a custom dataset. Follow this [written guide](https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset/) and [YouTube video](https://www.youtube.com/watch?v=wuZtUMEiKWY) for step-by-step instructions or refer to the [Ultralytics documentation](../modes/train.md).
+You are now ready to train YOLOv8 on a custom dataset. Follow this [written guide](https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset/?ref=ultralytics) and [YouTube video](https://www.youtube.com/watch?v=wuZtUMEiKWY) for step-by-step instructions or refer to the [Ultralytics documentation](../modes/train.md).
 
 ## Upload Custom YOLOv8 Model Weights for Testing and Deployment
 
@@ -178,7 +178,7 @@ dataset = project.version(VERSION).download("yolov8")
 project.version(dataset.version).deploy(model_type="yolov8", model_path=f"{HOME}/runs/detect/train/")
 ```
 
-In this code, replace the project ID and version ID with the values for your account and project. [Learn how to retrieve your Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
+In this code, replace the project ID and version ID with the values for your account and project. [Learn how to retrieve your Roboflow API key](https://docs.roboflow.com/api-reference/authentication?ref=ultralytics#retrieve-an-api-key).
 
 When you run the code above, you will be asked to authenticate. Then, your model will be uploaded and an API will be created for your project. This process can take up to 30 minutes to complete.
 
@@ -188,7 +188,7 @@ To test your model and find deployment instructions for supported SDKs, go to th
 <img src="https://github.com/ultralytics/docs/releases/download/0/running-inference-example-image.avif" alt="Running inference on an example image" width="800">
 </p>
 
-You can also use your uploaded model as a [labeling assistant](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling). This feature uses your trained model to recommend annotations on images uploaded to Roboflow.
+You can also use your uploaded model as a [labeling assistant](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling?ref=ultralytics). This feature uses your trained model to recommend annotations on images uploaded to Roboflow.
 
 ## How to Evaluate YOLOv8 Models
 
@@ -227,9 +227,9 @@ You can use Vector Analysis to:
 Want to learn more about using Roboflow for creating YOLOv8 models? The following resources may be helpful in your work.
 
 - [Train YOLOv8 on a Custom Dataset](https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb): Follow our interactive notebook that shows you how to train a YOLOv8 model on a custom dataset.
-- [Autodistill](https://autodistill.github.io/autodistill/): Use large foundation vision models to label data for specific models. You can label images for use in training YOLOv8 classification, detection, and segmentation models with Autodistill.
-- [Supervision](https://roboflow.github.io/supervision/): A Python package with helpful utilities for use in working with computer vision models. You can use supervision to filter detections, compute confusion matrices, and more, all in a few lines of Python code.
-- [Roboflow Blog](https://blog.roboflow.com/): The Roboflow Blog features over 500 articles on computer vision, covering topics from how to train a YOLOv8 model to annotation best practices.
+- [Autodistill](https://docs.autodistill.com/): Use large foundation vision models to label data for specific models. You can label images for use in training YOLOv8 classification, detection, and segmentation models with Autodistill.
+- [Supervision](https://supervision.roboflow.com/?ref=ultralytics): A Python package with helpful utilities for use in working with computer vision models. You can use supervision to filter detections, compute confusion matrices, and more, all in a few lines of Python code.
+- [Roboflow Blog](https://blog.roboflow.com/?ref=ultralytics): The Roboflow Blog features over 500 articles on computer vision, covering topics from how to train a YOLOv8 model to annotation best practices.
 - [Roboflow YouTube channel](https://www.youtube.com/@Roboflow): Browse dozens of in-depth computer vision guides on our YouTube channel, covering topics from training YOLOv8 models to automated image labeling.
 
 ## Project Showcase
@@ -250,7 +250,7 @@ Labeling data for YOLOv8 models using Roboflow is straightforward with Roboflow
 
 ### What services does Roboflow offer for collecting YOLOv8 training data?
 
-Roboflow provides two key services for collecting YOLOv8 training data: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://roboflow.com/collect?ref=ultralytics). Universe offers access to over 250,000 vision datasets, while Collect helps you gather images using a webcam and automated prompts.
+Roboflow provides two key services for collecting YOLOv8 training data: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://github.com/roboflow/roboflow-collect?ref=ultralytics). Universe offers access to over 250,000 vision datasets, while Collect helps you gather images using a webcam and automated prompts.
 
 ### How can I manage and analyze my YOLOv8 dataset using Roboflow?
 

docs/en/integrations/tensorboard.md

@@ -6,7 +6,7 @@ keywords: YOLOv8, TensorBoard, model training, visualization, machine learning,
 
 # Gain Visual Insights with YOLOv8's Integration with TensorBoard
 
-Understanding and fine-tuning computer vision models like [Ultralytics' YOLOv8](https://ultralytics.com) becomes more straightforward when you take a closer look at their training processes. Model training visualization helps with getting insights into the model's learning patterns, performance metrics, and overall behavior. YOLOv8's integration with TensorBoard makes this process of visualization and analysis easier and enables more efficient and informed adjustments to the model.
+Understanding and fine-tuning computer vision models like [Ultralytics' YOLOv8](https://www.ultralytics.com/) becomes more straightforward when you take a closer look at their training processes. Model training visualization helps with getting insights into the model's learning patterns, performance metrics, and overall behavior. YOLOv8's integration with TensorBoard makes this process of visualization and analysis easier and enables more efficient and informed adjustments to the model.
 
 This guide covers how to use TensorBoard with YOLOv8. You'll learn about various visualizations, from tracking metrics to analyzing model graphs. These tools will help you understand your YOLOv8 model's performance better.