v1.0

a53a851b · chenzk · a53a851b · a53a851b · a53a851b · a53a851b
Commit a53a851b authored Jun 11, 2024 by chenzk
20 changed files
--- a/docs/en/help/index.md
+++ b/docs/en/help/index.md
+---
+comments: true
+description: Find comprehensive guides and documents on Ultralytics YOLO tasks. Includes FAQs, contributing guides, CI guide, CLA, MRE guide, code of conduct & more.
+keywords: Ultralytics, YOLO, guides, documents, FAQ, contributing, CI guide, CLA, MRE guide, code of conduct, EHS policy, security policy, privacy policy
+---
+
+Welcome to the Ultralytics Help page! We are dedicated to providing you with detailed resources to enhance your experience with the Ultralytics YOLO models and repositories. This page serves as your portal to guides and documentation designed to assist you with various tasks and answer questions you may encounter while engaging with our repositories.
+
+- [Frequently Asked Questions (FAQ)](FAQ.md): Find answers to common questions and issues encountered by the community of Ultralytics YOLO users and contributors.
+- [Contributing Guide](contributing.md): Discover the protocols for making contributions, including how to submit pull requests, report bugs, and more.
+- [Continuous Integration (CI) Guide](CI.md): Gain insights into the CI processes we employ, complete with status reports for each Ultralytics repository.
+- [Contributor License Agreement (CLA)](CLA.md): Review the CLA to understand the rights and responsibilities associated with contributing to Ultralytics projects.
+- [Minimum Reproducible Example (MRE) Guide](minimum_reproducible_example.md): Learn the process for creating an MRE, which is crucial for the timely and effective resolution of bug reports.
+- [Code of Conduct](code_of_conduct.md): Our community guidelines support a respectful and open atmosphere for all collaborators.
+- [Environmental, Health and Safety (EHS) Policy](environmental-health-safety.md): Delve into our commitment to sustainability and the well-being of all our stakeholders.
+- [Security Policy](security.md): Familiarize yourself with our security protocols and the procedure for reporting vulnerabilities.
+- [Privacy Policy](privacy.md): Read our privacy policy to understand how we protect your data and respect your privacy in all our services and operations.
+
+We encourage you to review these resources for a seamless and productive experience. Our aim is to foster a helpful and friendly environment for everyone in the Ultralytics community. Should you require additional support, please feel free to reach out via GitHub Issues or our official discussion forums. Happy coding!
--- a/docs/en/help/minimum_reproducible_example.md
+++ b/docs/en/help/minimum_reproducible_example.md
+---
+comments: true
+description: Learn how to create minimum reproducible examples (MRE) for efficient bug reporting in Ultralytics YOLO repositories with this step-by-step guide.
+keywords: Ultralytics, YOLO, minimum reproducible example, MRE, bug reports, guide, dependencies, code, troubleshooting
+---
+
+# Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories
+
+When submitting a bug report for Ultralytics YOLO repositories, it's essential to provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) (MRE). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories.
+
+## 1. Isolate the Problem
+
+The first step in creating an MRE is to isolate the problem. This means removing any unnecessary code or dependencies that are not directly related to the issue. Focus on the specific part of the code that is causing the problem and remove any irrelevant code.
+
+## 2. Use Public Models and Datasets
+
+When creating an MRE, use publicly available models and datasets to reproduce the issue. For example, use the 'yolov8n.pt' model and the 'coco8.yaml' dataset. This ensures that the maintainers and contributors can easily run your example and investigate the problem without needing access to proprietary data or custom models.
+
+## 3. Include All Necessary Dependencies
+
+Make sure to include all the necessary dependencies in your MRE. If your code relies on external libraries, specify the required packages and their versions. Ideally, provide a `requirements.txt` file or list the dependencies in your bug report.
+
+## 4. Write a Clear Description of the Issue
+
+Provide a clear and concise description of the issue you're experiencing. Explain the expected behavior and the actual behavior you're encountering. If applicable, include any relevant error messages or logs.
+
+## 5. Format Your Code Properly
+
+When submitting an MRE, format your code properly using code blocks in the issue description. This makes it easier for others to read and understand your code. In GitHub, you can create a code block by wrapping your code with triple backticks (\```) and specifying the language:
+
+<pre>
+```python
+# Your Python code goes here
+```
+</pre>
+
+## 6. Test Your MRE
+
+Before submitting your MRE, test it to ensure that it accurately reproduces the issue. Make sure that others can run your example without any issues or modifications.
+
+## Example of an MRE
+
+Here's an example of an MRE for a hypothetical bug report:
+
+**Bug description:**
+
+When running the `detect.py` script on the sample image from the 'coco8.yaml' dataset, I get an error related to the dimensions of the input tensor.
+
+**MRE:**
+
+```python
+import torch
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO("yolov8n.pt")
+
+# Load a 0-channel image
+image = torch.rand(1, 0, 640, 640)
+
+# Run the model
+results = model(image)
+```
+
+**Error message:**
+
+```
+RuntimeError: Expected input[1, 0, 640, 640] to have 3 channels, but got 0 channels instead
+```
+
+**Dependencies:**
+
+- torch==2.0.0
+- ultralytics==8.0.90
+
+In this example, the MRE demonstrates the issue with a minimal amount of code, uses a public model ('yolov8n.pt'), includes all necessary dependencies, and provides a clear description of the problem along with the error message.
+
+By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.
--- a/docs/en/help/privacy.md
+++ b/docs/en/help/privacy.md
+---
+description: Learn about how Ultralytics collects and uses data to improve user experience, ensure software stability, and address privacy concerns, with options to opt-out.
+keywords: Ultralytics, Data Collection, User Privacy, Google Analytics, Sentry, Crash Reporting, Anonymized Data, Privacy Settings, Opt-Out
+---
+
+# Data Collection for Ultralytics Python Package
+
+## Overview
+
+[Ultralytics](https://ultralytics.com) is dedicated to the continuous enhancement of the user experience and the capabilities of our Python package, including the advanced YOLO models we develop. Our approach involves the gathering of anonymized usage statistics and crash reports, helping us identify opportunities for improvement and ensuring the reliability of our software. This transparency document outlines what data we collect, its purpose, and the choice you have regarding this data collection.
+
+## Anonymized Google Analytics
+
+[Google Analytics](https://developers.google.com/analytics) is a web analytics service offered by Google that tracks and reports website traffic. It allows us to collect data about how our Python package is used, which is crucial for making informed decisions about design and functionality.
+
+### What We Collect
+
+- **Usage Metrics**: These metrics help us understand how frequently and in what ways the package is utilized, what features are favored, and the typical command-line arguments that are used.
+- **System Information**: We collect general non-identifiable information about your computing environment to ensure our package performs well across various systems.
+- **Performance Data**: Understanding the performance of our models during training, validation, and inference helps us in identifying optimization opportunities.
+
+For more information about Google Analytics and data privacy, visit [Google Analytics Privacy](https://support.google.com/analytics/answer/6004245).
+
+### How We Use This Data
+
+- **Feature Improvement**: Insights from usage metrics guide us in enhancing user satisfaction and interface design.
+- **Optimization**: Performance data assist us in fine-tuning our models for better efficiency and speed across diverse hardware and software configurations.
+- **Trend Analysis**: By studying usage trends, we can predict and respond to the evolving needs of our community.
+
+### Privacy Considerations
+
+We take several measures to ensure the privacy and security of the data you entrust to us:
+
+- **Anonymization**: We configure Google Analytics to anonymize the data collected, which means no personally identifiable information (PII) is gathered. You can use our services with the assurance that your personal details remain private.
+- **Aggregation**: Data is analyzed only in aggregate form. This practice ensures that patterns can be observed without revealing any individual user's activity.
+- **No Image Data Collection**: Ultralytics does not collect, process, or view any training or inference images.
+
+## Sentry Crash Reporting
+
+[Sentry](https://sentry.io/) is a developer-centric error tracking software that aids in identifying, diagnosing, and resolving issues in real-time, ensuring the robustness and reliability of applications. Within our package, it plays a crucial role by providing insights through crash reporting, significantly contributing to the stability and ongoing refinement of our software.
+
+!!! Note
+
+    Crash reporting via Sentry is activated only if the `sentry-sdk` Python package is pre-installed on your system. This package isn't included in the `ultralytics` prerequisites and won't be installed automatically by Ultralytics.
+
+### What We Collect
+
+If the `sentry-sdk` Python package is pre-installed on your system a crash event may send the following information:
+
+- **Crash Logs**: Detailed reports on the application's condition at the time of a crash, which are vital for our debugging efforts.
+- **Error Messages**: We record error messages generated during the operation of our package to understand and resolve potential issues quickly.
+
+To learn more about how Sentry handles data, please visit [Sentry's Privacy Policy](https://sentry.io/privacy/).
+
+### How We Use This Data
+
+- **Debugging**: Analyzing crash logs and error messages enables us to swiftly identify and correct software bugs.
+- **Stability Metrics**: By constantly monitoring for crashes, we aim to improve the stability and reliability of our package.
+
+### Privacy Considerations
+
+- **Sensitive Information**: We ensure that crash logs are scrubbed of any personally identifiable or sensitive user data, safeguarding the confidentiality of your information.
+- **Controlled Collection**: Our crash reporting mechanism is meticulously calibrated to gather only what is essential for troubleshooting while respecting user privacy.
+
+By detailing the tools used for data collection and offering additional background information with URLs to their respective privacy pages, users are provided with a comprehensive view of our practices, emphasizing transparency and respect for user privacy.
+
+## Disabling Data Collection
+
+We believe in providing our users with full control over their data. By default, our package is configured to collect analytics and crash reports to help improve the experience for all users. However, we respect that some users may prefer to opt out of this data collection.
+
+To opt out of sending analytics and crash reports, you can simply set `sync=False` in your YOLO settings. This ensures that no data is transmitted from your machine to our analytics tools.
+
+### Inspecting Settings
+
+To gain insight into the current configuration of your settings, you can view them directly:
+
+!!! Example "View settings"
+
+    === "Python"
+        You can use Python to view your settings. Start by importing the `settings` object from the `ultralytics` module. Print and return settings using the following commands:
+        ```python
+        from ultralytics import settings
+
+        # View all settings
+        print(settings)
+
+        # Return analytics and crash reporting setting
+        value = settings['sync']
+        ```
+
+    === "CLI"
+        Alternatively, the command-line interface allows you to check your settings with a simple command:
+        ```bash
+        yolo settings
+        ```
+
+### Modifying Settings
+
+Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:
+
+!!! Example "Update settings"
+
+    === "Python"
+        Within the Python environment, call the `update` method on the `settings` object to change your settings:
+        ```python
+        from ultralytics import settings
+
+        # Disable analytics and crash reporting
+        settings.update({'sync': False})
+
+        # Reset settings to default values
+        settings.reset()
+        ```
+
+    === "CLI"
+        If you prefer using the command-line interface, the following commands will allow you to modify your settings:
+        ```bash
+        # Disable analytics and crash reporting
+        yolo settings sync=False
+
+        # Reset settings to default values
+        yolo settings reset
+        ```
+
+The `sync=False` setting will prevent any data from being sent to Google Analytics or Sentry. Your settings will be respected across all sessions using the Ultralytics package and saved to disk for future sessions.
+
+## Commitment to Privacy
+
+Ultralytics takes user privacy seriously. We design our data collection practices with the following principles:
+
+- **Transparency**: We are open about the data we collect and how it is used.
+- **Control**: We give users full control over their data.
+- **Security**: We employ industry-standard security measures to protect the data we collect.
+
+## Questions or Concerns
+
+If you have any questions or concerns about our data collection practices, please reach out to us via our [contact form](https://ultralytics.com/contact) or via [support@ultralytics.com](mailto:support@ultralytics.com). We are dedicated to ensuring our users feel informed and confident in their privacy when using our package.
--- a/docs/en/help/security.md
+++ b/docs/en/help/security.md
+---
+description: Explore Ultralytics' comprehensive security strategies safeguarding user data and systems. Learn about our diverse security tools, including Snyk, GitHub CodeQL, and Dependabot Alerts.
+keywords: Ultralytics, Comprehensive Security, user data protection, Snyk, GitHub CodeQL, Dependabot, vulnerability management, coding security practices
+---
+
+# Ultralytics Security Policy
+
+At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities.
+
+## Snyk Scanning
+
+We utilize [Snyk](https://snyk.io/advisor/python/ultralytics) to conduct comprehensive security scans on Ultralytics repositories. Snyk's robust scanning capabilities extend beyond dependency checks; it also examines our code and Dockerfiles for various vulnerabilities. By identifying and addressing these issues proactively, we ensure a higher level of security and reliability for our users.
+
+[![ultralytics](https://snyk.io/advisor/python/ultralytics/badge.svg)](https://snyk.io/advisor/python/ultralytics)
+
+## GitHub CodeQL Scanning
+
+Our security strategy includes GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) scanning. CodeQL delves deep into our codebase, identifying complex vulnerabilities like SQL injection and XSS by analyzing the code's semantic structure. This advanced level of analysis ensures early detection and resolution of potential security risks.
+
+[![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml)
+
+## GitHub Dependabot Alerts
+
+[Dependabot](https://docs.github.com/en/code-security/dependabot) is integrated into our workflow to monitor dependencies for known vulnerabilities. When a vulnerability is identified in one of our dependencies, Dependabot alerts us, allowing for swift and informed remediation actions.
+
+## GitHub Secret Scanning Alerts
+
+We employ GitHub [secret scanning](https://docs.github.com/en/code-security/secret-scanning/managing-alerts-from-secret-scanning) alerts to detect sensitive data, such as credentials and private keys, accidentally pushed to our repositories. This early detection mechanism helps prevent potential security breaches and data exposures.
+
+## Private Vulnerability Reporting
+
+We enable private vulnerability reporting, allowing users to discreetly report potential security issues. This approach facilitates responsible disclosure, ensuring vulnerabilities are handled securely and efficiently.
+
+If you suspect or discover a security vulnerability in any of our repositories, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible.
+
+We appreciate your help in keeping all Ultralytics open-source projects secure and safe for everyone 🙏.
--- a/docs/en/hub/api/index.md
+++ b/docs/en/hub/api/index.md
+---
+description: Discover what's next for Ultralytics with our under-construction page, previewing new, groundbreaking AI and ML features coming soon.
+keywords: Ultralytics, coming soon, under construction, new features, AI updates, ML advancements, YOLO, technology preview
+---
+
+# Under Construction 🏗️🌟
+
+Welcome to the Ultralytics "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you!
+
+## Exciting New Features on the Way 🎉
+
+- **Innovative Breakthroughs:** Get ready for advanced features and services that will transform your AI and ML experience.
+- **New Horizons:** Anticipate novel products that redefine AI and ML capabilities.
+- **Enhanced Services:** We're upgrading our services for greater efficiency and user-friendliness.
+
+## Stay Updated 🚧
+
+This placeholder page is your first stop for upcoming developments. Keep an eye out for:
+
+- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news.
+- **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers.
+- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights.
+
+## We Value Your Input 🗣️
+
+Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/contact).
+
+## Thank You, Community! 🌍
+
+Your [contributions](https://docs.ultralytics.com/help/contributing) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
+
+---
+
+Excited for what's coming? Bookmark this page and get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖
--- a/docs/en/hub/app/android.md
+++ b/docs/en/hub/app/android.md
+---
+comments: true
+description: Learn about the Ultralytics Android App, enabling real-time object detection using YOLO models. Discover in-app features, quantization methods, and delegate options for optimal performance.
+keywords: Ultralytics, Android App, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, CPU, GPU, Hexagon, NNAPI
+---
+
+# Ultralytics Android App: Real-time Object Detection with YOLO Models
+
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+  <img width="100%" src="https://user-images.githubusercontent.com/26833433/281124469-6b3b0945-dbb1-44c8-80a9-ef6bc778b299.jpg" alt="Ultralytics HUB preview image"></a>
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+  <br>
+  <br>
+  <a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
+    <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>&nbsp;
+</div>
+
+The Ultralytics Android App is a powerful tool that allows you to run YOLO models directly on your Android device for real-time object detection. This app utilizes TensorFlow Lite for model optimization and various hardware delegates for acceleration, enabling fast and efficient object detection.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/AIvrQ7y0aLo"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Getting Started with the Ultralytics HUB App (IOS & Android)
+</p>
+
+## Quantization and Acceleration
+
+To achieve real-time performance on your Android device, YOLO models are quantized to either FP16 or INT8 precision. Quantization is a process that reduces the numerical precision of the model's weights and biases, thus reducing the model's size and the amount of computation required. This results in faster inference times without significantly affecting the model's accuracy.
+
+### FP16 Quantization
+
+FP16 (or half-precision) quantization converts the model's 32-bit floating-point numbers to 16-bit floating-point numbers. This reduces the model's size by half and speeds up the inference process, while maintaining a good balance between accuracy and performance.
+
+### INT8 Quantization
+
+INT8 (or 8-bit integer) quantization further reduces the model's size and computation requirements by converting its 32-bit floating-point numbers to 8-bit integers. This quantization method can result in a significant speedup, but it may lead to a slight reduction in mean average precision (mAP) due to the lower numerical precision.
+
+!!! Tip "mAP Reduction in INT8 Models"
+
+    The reduced numerical precision in INT8 models can lead to some loss of information during the quantization process, which may result in a slight decrease in mAP. However, this trade-off is often acceptable considering the substantial performance gains offered by INT8 quantization.
+
+## Delegates and Performance Variability
+
+Different delegates are available on Android devices to accelerate model inference. These delegates include CPU, [GPU](https://www.tensorflow.org/lite/android/delegates/gpu), [Hexagon](https://www.tensorflow.org/lite/android/delegates/hexagon) and [NNAPI](https://www.tensorflow.org/lite/android/delegates/nnapi). The performance of these delegates varies depending on the device's hardware vendor, product line, and specific chipsets used in the device.
+
+1. **CPU**: The default option, with reasonable performance on most devices.
+2. **GPU**: Utilizes the device's GPU for faster inference. It can provide a significant performance boost on devices with powerful GPUs.
+3. **Hexagon**: Leverages Qualcomm's Hexagon DSP for faster and more efficient processing. This option is available on devices with Qualcomm Snapdragon processors.
+4. **NNAPI**: The Android Neural Networks API (NNAPI) serves as an abstraction layer for running ML models on Android devices. NNAPI can utilize various hardware accelerators, such as CPU, GPU, and dedicated AI chips (e.g., Google's Edge TPU, or the Pixel Neural Core).
+
+Here's a table showing the primary vendors, their product lines, popular devices, and supported delegates:
+
+| Vendor                                  | Product Lines                                                                        | Popular Devices                                                                                                                                                                | Delegates Supported      |
+|-----------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|
+| [Qualcomm](https://www.qualcomm.com/)   | [Snapdragon (e.g., 800 series)](https://www.qualcomm.com/snapdragon)                 | [Samsung Galaxy S21](https://www.samsung.com/global/galaxy/galaxy-s21-5g/), [OnePlus 9](https://www.oneplus.com/9), [Google Pixel 6](https://store.google.com/product/pixel_6) | CPU, GPU, Hexagon, NNAPI |
+| [Samsung](https://www.samsung.com/)     | [Exynos (e.g., Exynos 2100)](https://www.samsung.com/semiconductor/minisite/exynos/) | [Samsung Galaxy S21 (Global version)](https://www.samsung.com/global/galaxy/galaxy-s21-5g/)                                                                                    | CPU, GPU, NNAPI          |
+| [MediaTek](https://i.mediatek.com/)     | [Dimensity (e.g., Dimensity 1200)](https://i.mediatek.com/dimensity-1200)            | [Realme GT](https://www.realme.com/global/realme-gt), [Xiaomi Redmi Note](https://www.mi.com/en/phone/redmi/note-list)                                                         | CPU, GPU, NNAPI          |
+| [HiSilicon](https://www.hisilicon.com/) | [Kirin (e.g., Kirin 990)](https://www.hisilicon.com/en/products/Kirin)               | [Huawei P40 Pro](https://consumer.huawei.com/en/phones/p40-pro/), [Huawei Mate 30 Pro](https://consumer.huawei.com/en/phones/mate30-pro/)                                      | CPU, GPU, NNAPI          |
+| [NVIDIA](https://www.nvidia.com/)       | [Tegra (e.g., Tegra X1)](https://developer.nvidia.com/content/tegra-x1)              | [NVIDIA Shield TV](https://www.nvidia.com/en-us/shield/shield-tv/), [Nintendo Switch](https://www.nintendo.com/switch/)                                                        | CPU, GPU, NNAPI          |
+
+Please note that the list of devices mentioned is not exhaustive and may vary depending on the specific chipsets and device models. Always test your models on your target devices to ensure compatibility and optimal performance.
+
+Keep in mind that the choice of delegate can affect performance and model compatibility. For example, some models may not work with certain delegates, or a delegate may not be available on a specific device. As such, it's essential to test your model and the chosen delegate on your target devices for the best results.
+
+## Getting Started with the Ultralytics Android App
+
+To get started with the Ultralytics Android App, follow these steps:
+
+1. Download the Ultralytics App from the [Google Play Store](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app).
+
+2. Launch the app on your Android device and sign in with your Ultralytics account. If you don't have an account yet, create one [here](https://hub.ultralytics.com/).
+
+3. Once signed in, you will see a list of your trained YOLO models. Select a model to use for object detection.
+
+4. Grant the app permission to access your device's camera.
+
+5. Point your device's camera at objects you want to detect. The app will display bounding boxes and class labels in real-time as it detects objects.
+
+6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
+
+With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
--- a/docs/en/hub/app/index.md
+++ b/docs/en/hub/app/index.md
+---
+comments: true
+description: Explore the Ultralytics HUB App, offering the ability to run YOLOv5 and YOLOv8 models on your iOS and Android devices with optimized performance.
+keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object detection, image recognition, mobile device, hardware acceleration, Apple Neural Engine, Android GPU, NNAPI, custom model training
+---
+
+# Ultralytics HUB App
+
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+  <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+  <br>
+  <br>
+  <a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
+    <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="Apple App store"></a>
+  <a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
+    <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>&nbsp;
+</div>
+
+Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mobile app that allows you to run YOLOv5 and YOLOv8 models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) and [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) devices. With the HUB App, you can utilize hardware acceleration features like Apple's Neural Engine (ANE) or Android GPU and Neural Network API (NNAPI) delegates to achieve impressive performance on your mobile device.
+
+## Features
+
+- **Run YOLOv5 and YOLOv8 models**: Experience the power of YOLO models on your mobile device for real-time object detection and image recognition tasks.
+- **Hardware Acceleration**: Benefit from Apple ANE on iOS devices or Android GPU and NNAPI delegates for optimized performance.
+- **Custom Model Training**: Train custom models with the Ultralytics HUB platform and preview them live using the HUB App.
+- **Mobile Compatibility**: The HUB App supports both iOS and Android devices, bringing the power of YOLO models to a wide range of users.
+
+## App Documentation
+
+- [**iOS**](ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
+- [**Android**](android.md): Explore TFLite acceleration on Android mobile devices.
+
+Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
--- a/docs/en/hub/app/ios.md
+++ b/docs/en/hub/app/ios.md
+---
+comments: true
+description: Execute object detection in real-time on your iOS devices utilizing YOLO models. Leverage the power of the Apple Neural Engine and Core ML for fast and efficient object detection.
+keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple Neural Engine, Core ML, FP16, INT8, quantization
+---
+
+# Ultralytics iOS App: Real-time Object Detection with YOLO Models
+
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+  <img width="100%" src="https://user-images.githubusercontent.com/26833433/281124469-6b3b0945-dbb1-44c8-80a9-ef6bc778b299.jpg" alt="Ultralytics HUB preview image"></a>
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+  <br>
+  <br>
+  <a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
+    <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="Apple App store"></a>
+</div>
+
+The Ultralytics iOS App is a powerful tool that allows you to run YOLO models directly on your iPhone or iPad for real-time object detection. This app utilizes the Apple Neural Engine and Core ML for model optimization and acceleration, enabling fast and efficient object detection.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/AIvrQ7y0aLo"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Getting Started with the Ultralytics HUB App (IOS & Android)
+</p>
+
+## Quantization and Acceleration
+
+To achieve real-time performance on your iOS device, YOLO models are quantized to either FP16 or INT8 precision. Quantization is a process that reduces the numerical precision of the model's weights and biases, thus reducing the model's size and the amount of computation required. This results in faster inference times without significantly affecting the model's accuracy.
+
+### FP16 Quantization
+
+FP16 (or half-precision) quantization converts the model's 32-bit floating-point numbers to 16-bit floating-point numbers. This reduces the model's size by half and speeds up the inference process, while maintaining a good balance between accuracy and performance.
+
+### INT8 Quantization
+
+INT8 (or 8-bit integer) quantization further reduces the model's size and computation requirements by converting its 32-bit floating-point numbers to 8-bit integers. This quantization method can result in a significant speedup, but it may lead to a slight reduction in accuracy.
+
+## Apple Neural Engine
+
+The Apple Neural Engine (ANE) is a dedicated hardware component integrated into Apple's A-series and M-series chips. It's designed to accelerate machine learning tasks, particularly for neural networks, allowing for faster and more efficient execution of your YOLO models.
+
+By combining quantized YOLO models with the Apple Neural Engine, the Ultralytics iOS App achieves real-time object detection on your iOS device without compromising on accuracy or performance.
+
+| Release Year | iPhone Name                                          | Chipset Name                                          | Node Size | ANE TOPs |
+|--------------|------------------------------------------------------|-------------------------------------------------------|-----------|----------|
+| 2017         | [iPhone X](https://en.wikipedia.org/wiki/IPhone_X)   | [A11 Bionic](https://en.wikipedia.org/wiki/Apple_A11) | 10 nm     | 0.6      |
+| 2018         | [iPhone XS](https://en.wikipedia.org/wiki/IPhone_XS) | [A12 Bionic](https://en.wikipedia.org/wiki/Apple_A12) | 7 nm      | 5        |
+| 2019         | [iPhone 11](https://en.wikipedia.org/wiki/IPhone_11) | [A13 Bionic](https://en.wikipedia.org/wiki/Apple_A13) | 7 nm      | 6        |
+| 2020         | [iPhone 12](https://en.wikipedia.org/wiki/IPhone_12) | [A14 Bionic](https://en.wikipedia.org/wiki/Apple_A14) | 5 nm      | 11       |
+| 2021         | [iPhone 13](https://en.wikipedia.org/wiki/IPhone_13) | [A15 Bionic](https://en.wikipedia.org/wiki/Apple_A15) | 5 nm      | 15.8     |
+| 2022         | [iPhone 14](https://en.wikipedia.org/wiki/IPhone_14) | [A16 Bionic](https://en.wikipedia.org/wiki/Apple_A16) | 4 nm      | 17.0     |
+
+Please note that this list only includes iPhone models from 2017 onwards, and the ANE TOPs values are approximate.
+
+## Getting Started with the Ultralytics iOS App
+
+To get started with the Ultralytics iOS App, follow these steps:
+
+1. Download the Ultralytics App from the [App Store](https://apps.apple.com/xk/app/ultralytics/id1583935240).
+
+2. Launch the app on your iOS device and sign in with your Ultralytics account. If you don't have an account yet, create one [here](https://hub.ultralytics.com/).
+
+3. Once signed in, you will see a list of your trained YOLO models. Select a model to use for object detection.
+
+4. Grant the app permission to access your device's camera.
+
+5. Point your device's camera at objects you want to detect. The app will display bounding boxes and class labels in real-time as it detects objects.
+
+6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
+
+With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
--- a/docs/en/hub/cloud-training.md
+++ b/docs/en/hub/cloud-training.md
+---
+comments: true
+description: Learn how to use Ultralytics HUB for efficient and user-friendly AI model training in the cloud. Follow our detailed guide for easy model creation, training, evaluation, and deployment.
+keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
+---
+
+# Cloud Training
+
+[Ultralytics HUB](https://hub.ultralytics.com/) provides a powerful and user-friendly cloud platform to train custom object detection models. Easily select your dataset and the desired training method, then kick off the process with just a few clicks. Ultralytics HUB offers pre-built options and various model architectures to streamline your workflow.
+
+![cloud training cover](https://github.com/ultralytics/ultralytics/assets/19519529/cbfdb3b8-ad35-44a6-afe6-61ec0b8e8b8d)
+
+Read more about creating and other details of a Model at our [HUB Models page](models.md)
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ie3vLUDNYZo"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> New Feature 🌟 Introducing Ultralytics HUB Cloud Training
+</p>
+
+## Selecting an Instance
+
+For details on picking a model and instances for it, please read our [Instances guide Page](models.md)
+
+## Steps to Train the Model
+
+Once the instance has been selected, training a model using Ultralytics HUB is a three-step process, as below:
+
+1. Picking a Dataset - Read more about datasets, steps to add/remove datasets from the [Dataset page](datasets.md)
+2. Picking a Model - Read more about models, steps to create/share and handle a model on the [HUB Models page](models.md)
+3. Training the Model on the Chosen Dataset
+
+Ultralytics HUB offers three training options:
+
+- **Ultralytics Cloud** - Explained in this page.
+- **Google Colab** - Train on Google's popular Colab notebooks.
+- **Bring your own agent** - Train models locally on your own hardware or on-premise GPU servers.
+
+In order to start training your model, follow the instructions presented in these steps.
+
+## Training via Ultralytics Cloud
+
+To start training your model using Ultralytics Cloud, simply select the Training Duration, Available Instances, and Payment options.
+
+**Training Duration** - Ultralytics offers two kinds of training durations:
+
+1. Training based on `Epochs`: This option allows you to train your model based on the number of times your dataset needs to go through the cycle of train, label, and test. The exact pricing based on the number of epochs is hard to determine. Hence, if the credit gets exhausted before the intended number of epochs, the training pauses, and you get a prompt to top-up and resume training.
+2. Timed Training: The timed training feature allows you to fix the time duration of the entire training process and also determines the estimated amount before the start of training.
+
+![Ultralytics cloud screenshot of training duration options](https://github.com/ultralytics/ultralytics/assets/19519529/47b96f3f-a9ea-441a-b065-cba97edc333f)
+
+When the training starts, you can click **Done** and monitor the training progress on the Model page.
+
+## Monitor Your Training
+
+Once the model and mode of training have been selected, you can monitor the training procedure on the `Train` section with the link provided in the terminal (on your agent/Google Colab) or a button from Ultralytics Cloud.
+
+![Monitor your Training](https://github.com/ultralytics/ultralytics/assets/19519529/316f8301-0d60-465e-8c99-aa3daf66433c)
+
+## Stopping and Resuming Your Training
+
+Once the training has started, you can `Stop` the training, which will also correspondingly pause the credit usage. You can then `Resume` the training from the point where it stopped.
+
+![Pausing and Resuming Training](https://github.com/ultralytics/ultralytics/assets/19519529/b2707a93-fa5c-4ee2-8443-6be9e1c2857d)
+
+## Payments and Billing Options
+
+Ultralytics HUB offers `Pay Now` as upfront and/or using `Ultralytics HUB Account` as a wallet to top up and fulfill the billing. You can choose from two types of accounts: `Free` and `Pro` user.
+
+To access your profile, click on the profile picture in the bottom left corner.
+
+![Clicking profile picture](https://github.com/ultralytics/ultralytics/assets/19519529/53e5410e-06f5-4b40-b29d-ef00b5779163)
+
+Click on the Billing tab to view your current plan and options to upgrade it.
+
+![Clicking Upgrade button](https://github.com/ultralytics/ultralytics/assets/19519529/361b43c7-a9d4-4d05-b80b-dc1fa8bce829)
+
+You will be prompted with different available plans, and you can pick from the available plans as shown below.
+
+![Picking a plan](https://github.com/ultralytics/ultralytics/assets/19519529/4326b01c-0d7d-4850-ac4f-ced2de3339ee)
+
+Navigate to the Payment page, fill in the details, and complete the payment.
+
+![Payment Page](https://github.com/ultralytics/ultralytics/assets/19519529/5deebabe-1d8a-485a-b290-e038729c849f)
--- a/docs/en/hub/datasets.md
+++ b/docs/en/hub/datasets.md
+---
+comments: true
+description: Learn how Ultralytics HUB datasets streamline your ML workflow. Upload, format, validate, access, share, edit or delete datasets for Ultralytics YOLO model training.
+keywords: Ultralytics, HUB datasets, YOLO model training, upload datasets, dataset validation, ML workflow, share datasets
+---
+
+# HUB Datasets
+
+[Ultralytics HUB](https://hub.ultralytics.com/) datasets are a practical solution for managing and leveraging your custom datasets.
+
+Once uploaded, datasets can be immediately utilized for model training. This integrated approach facilitates a seamless transition from dataset management to model training, significantly simplifying the entire process.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/R42s2zFtNIY"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Watch: Upload Datasets to Ultralytics HUB | Complete Walkthrough of Dataset Upload Feature
+</p>
+
+## Upload Dataset
+
+Ultralytics HUB datasets are just like YOLOv5 and YOLOv8 🚀 datasets. They use the same structure and the same label formats to keep everything simple.
+
+Before you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML file inside the dataset root directory** and that **your dataset YAML, directory and ZIP have the same name**, as shown in the example below, and then zip the dataset directory.
+
+For example, if your dataset is called "coco8", as our [COCO8](https://docs.ultralytics.com/datasets/detect/coco8) example dataset, then you should have a `coco8.yaml` inside your `coco8/` directory, which will create a `coco8.zip` when zipped:
+
+```bash
+zip -r coco8.zip coco8
+```
+
+You can download our [COCO8](https://github.com/ultralytics/hub/blob/main/example_datasets/coco8.zip) example dataset and unzip it to see exactly how to structure your dataset.
+
+<p align="center">
+  <img  src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_1.jpg" alt="COCO8 Dataset Structure" width="80%">
+</p>
+
+The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format.
+
+!!! Example "coco8.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco8.yaml"
+    ```
+
+After zipping your dataset, you should validate it before uploading it to Ultralytics HUB. Ultralytics HUB conducts the dataset validation check post-upload, so by ensuring your dataset is correctly formatted and error-free ahead of time, you can forestall any setbacks due to dataset rejection.
+
+```py
+from ultralytics.hub import check_dataset
+
+check_dataset('path/to/coco8.zip')
+```
+
+Once your dataset ZIP is ready, navigate to the [Datasets](https://hub.ultralytics.com/datasets) page by clicking on the **Datasets** button in the sidebar.
+
+![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Datasets button in the sidebar](https://github.com/ultralytics/ultralytics/assets/19519529/2d9f774c-100d-4ff4-a82b-2a38ced33c21)
+
+Click on the **Upload Dataset** button on the top right of the page. This action will trigger the **Upload Dataset** dialog.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Upload Dataset button](https://github.com/ultralytics/ultralytics/assets/19519529/52ac10f5-ce42-483a-ac02-1d37d2cba3de)
+
+Upload your dataset in the _Dataset .zip file_ field.
+
+You have the additional option to set a custom name and description for your Ultralytics HUB dataset.
+
+When you're happy with your dataset configuration, click **Upload**.
+
+![Ultralytics HUB screenshot of the Upload Dataset dialog with an arrow pointing to the Upload button](https://github.com/ultralytics/ultralytics/assets/19519529/7d210ff6-bdb2-4535-a661-0470274bd7d6)
+
+After your dataset is uploaded and processed, you will be able to access it from the Datasets page.
+
+![Ultralytics HUB screenshot of the Datasets page with an arrow pointing to one of the datasets](https://github.com/ultralytics/ultralytics/assets/19519529/a05d9b66-f8ba-4474-b8ac-ebe0dd143831)
+
+You can view the images in your dataset grouped by splits (Train, Validation, Test).
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Images tab](https://github.com/ultralytics/ultralytics/assets/19519529/e07468e3-6284-4334-9783-84bfb11130f8)
+
+!!! tip "Tip"
+
+    Each image can be enlarged for better visualization.
+
+    ![Ultralytics HUB screenshot of the Images tab inside the Dataset page with an arrow pointing to the expand icon](https://github.com/ultralytics/ultralytics/assets/19519529/26f411a0-5153-4805-a8c1-cbd379708e28)
+
+    ![Ultralytics HUB screenshot of the Images tab inside the Dataset page with one of the images expanded](https://github.com/ultralytics/ultralytics/assets/19519529/7d5e0d50-85e5-4014-9f5b-464284e5b291)
+
+Also, you can analyze your dataset by click on the **Overview** tab.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Overview tab](https://github.com/ultralytics/ultralytics/assets/19519529/5eaacd5d-fedf-4332-9091-1418c9f333cb)
+
+Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) on your dataset.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Train Model button](https://github.com/ultralytics/ultralytics/assets/19519529/cb709e5f-a10b-478f-a81d-a48f61c193fe)
+
+## Share Dataset
+
+!!! Info "Info"
+
+    Ultralytics HUB's sharing functionality provides a convenient way to share datasets with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
+
+!!! note "Note"
+
+    You have control over the general access of your datasets.
+
+    You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the dataset, regardless of whether they have an Ultralytics HUB account or not.
+
+Navigate to the Dataset page of the dataset you want to share, open the dataset actions dropdown and click on the **Share** option. This action will trigger the **Share Dataset** dialog.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Share option](https://github.com/ultralytics/ultralytics/assets/19519529/9a0e61e7-2838-42b3-8abe-a22980e6c680)
+
+!!! tip "Tip"
+
+    You can also share a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
+
+    ![Ultralytics HUB screenshot of the Datasets page with an arrow pointing to the Share option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_share_dataset_2.jpg)
+
+Set the general access to "Unlisted" and click **Save**.
+
+![Ultralytics HUB screenshot of the Share Dataset dialog with an arrow pointing to the dropdown and one to the Save button](https://github.com/ultralytics/ultralytics/assets/19519529/5818b928-19a3-48a8-892d-27ac1dc684dd)
+
+Now, anyone who has the direct link to your dataset can view it.
+
+!!! tip "Tip"
+
+    You can easily click on the dataset's link shown in the **Share Dataset** dialog to copy it.
+
+    ![Ultralytics HUB screenshot of the Share Dataset dialog with an arrow pointing to the dataset's link](https://github.com/ultralytics/ultralytics/assets/19519529/8ede7d20-2a68-411d-9de5-3175b5ba7038)
+
+## Edit / Delete Dataset
+
+Navigate to the Dataset page of the dataset you want to edit, open the dataset actions dropdown and click on the **Edit** option. This action will trigger the **Update Dataset** dialog.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Edit and Delete option](https://github.com/ultralytics/ultralytics/assets/19519529/6c248c8c-29cd-4bd5-b33d-43e90aa1d000)
+
+Apply the desired modifications to your dataset and then confirm the changes by clicking **Save**.
+
+Navigate to the Dataset page of the dataset you want to delete, open the dataset actions dropdown and click on the **Delete** option. This action will delete the dataset.
+
+!!! note "Note"
+
+    If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
+
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the datasets](https://github.com/ultralytics/ultralytics/assets/19519529/56a9460c-0e06-4659-989d-715211b9d7ce)
--- a/docs/en/hub/index.md
+++ b/docs/en/hub/index.md
+---
+comments: true
+description: Gain insights into training and deploying your YOLOv5 and YOLOv8 models with Ultralytics HUB. Explore pre-trained models, templates and various integrations.
+keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pretrained models, model integrations
+---
+
+# Ultralytics HUB
+
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+  <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+  <br>
+  <br>
+  <a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
+    <img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
+  <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+</div>
+
+👋 Hello from the [Ultralytics](https://ultralytics.com/) Team! We've been working hard these last few months to launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new web tool for training and deploying all your YOLOv5 and YOLOv8 🚀 models from one spot!
+
+## Introduction
+
+HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and train new models quickly. It offers a range of pre-trained models and templates to choose from, making it easy for users to get started with training their own models. Once a model is trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
+</p>
+
+We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
+
+- [**Quickstart**](quickstart.md). Start training and deploying YOLO models with HUB in seconds.
+- [**Datasets: Preparing and Uploading**](datasets.md). Learn how to prepare and upload your datasets to HUB in YOLO format.
+- [**Projects: Creating and Managing**](projects.md). Group your models into projects for improved organization.
+- [**Models: Training and Exporting**](models.md). Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
+- [**Integrations: Options**](integrations.md). Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
+- [**Ultralytics HUB App**](app/index.md). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
+    - [**iOS**](app/ios.md). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
+    - [**Android**](app/android.md). Explore TFLite acceleration on mobile devices.
+- [**Inference API**](inference-api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
--- a/docs/en/hub/inference-api.md
+++ b/docs/en/hub/inference-api.md
+---
+comments: true
+description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or cURL for swift object detection.
+keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, cURL, Quickstart
+---
+
+# YOLO Inference API
+
+The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
+
+![Inference API Screenshot](https://github.com/ultralytics/ultralytics/assets/19519529/a8c00e55-1590-403b-bdee-ed456c60af4d) Screenshot of the Inference API section in the trained model Preview tab.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/OpWpBI35A5Y"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Ultralytics HUB Inference API Walkthrough
+</p>
+
+## API URL
+
+The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:
+
+```
+https://api.ultralytics.com/v1/predict
+```
+
+## Example Usage in Python
+
+To access the YOLO Inference API with the specified model and API key using Python, you can use the following code:
+
+```python
+import requests
+
+# API URL, use actual MODEL_ID
+url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+
+# Headers, use actual API_KEY
+headers = {"x-api-key": "API_KEY"}
+
+# Inference arguments (optional)
+data = {"size": 640, "confidence": 0.25, "iou": 0.45}
+
+# Load image and send request
+with open("path/to/image.jpg", "rb") as image_file:
+    files = {"image": image_file}
+    response = requests.post(url, headers=headers, files=files, data=data)
+
+print(response.json())
+```
+
+In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `path/to/image.jpg` with the path to the image you want to analyze.
+
+## Example Usage with cURL
+
+You can use the YOLO Inference API with client URL (cURL) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
+
+```bash
+curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
+	-H "x-api-key: API_KEY" \
+	-F "image=@/path/to/image.jpg" \
+	-F "size=640" \
+	-F "confidence=0.25" \
+	-F "iou=0.45"
+```
+
+## Passing Arguments
+
+This command sends a POST request to the YOLO Inference API with the specified `MODEL_ID` in the URL and the `API_KEY` in the request `headers`, along with the image file specified by `@path/to/image.jpg`.
+
+Here's an example of passing the `size`, `confidence`, and `iou` arguments via the API URL using the `requests` library in Python:
+
+```python
+import requests
+
+# API URL, use actual MODEL_ID
+url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+
+# Headers, use actual API_KEY
+headers = {"x-api-key": "API_KEY"}
+
+# Inference arguments (optional)
+data = {"size": 640, "confidence": 0.25, "iou": 0.45}
+
+# Load image and send request
+with open("path/to/image.jpg", "rb") as image_file:
+    files = {"image": image_file}
+    response = requests.post(url, headers=headers, files=files, data=data)
+
+print(response.json())
+```
+
+In this example, the `data` dictionary contains the query arguments `size`, `confidence`, and `iou`, which tells the API to run inference at image size 640 with confidence and IoU thresholds of 0.25 and 0.45.
+
+This will send the query parameters along with the file in the POST request. See the table below for a full list of available inference arguments.
+
+| Inference Argument | Default | Type    | Notes                                          |
+|--------------------|---------|---------|------------------------------------------------|
+| `size`             | `640`   | `int`   | valid range is `32` - `1280` pixels            |
+| `confidence`       | `0.25`  | `float` | valid range is `0.01` - `1.0`                  |
+| `iou`              | `0.45`  | `float` | valid range is `0.0` - `0.95`                  |
+| `url`              | `''`    | `str`   | optional image URL if not image file is passed |
+| `normalize`        | `False` | `bool`  |                                                |
+
+## Return JSON format
+
+The YOLO Inference API returns a JSON list with the detection results. The format of the JSON list will be the same as the one produced locally by the `results[0].tojson()` command.
+
+The JSON list contains information about the detected objects, their coordinates, classes, and confidence scores.
+
+### Detect Model Format
+
+YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
+
+!!! Example "Detect Model JSON Response"
+
+    === "`ultralytics`"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load model
+        model = YOLO('yolov8n.pt')
+
+        # Run inference
+        results = model('image.jpg')
+
+        # Print image.jpg results in JSON format
+        print(results[0].tojson())
+        ```
+
+    === "cURL"
+
+        ```bash
+        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
+            -H "x-api-key: API_KEY" \
+            -F "image=@/path/to/image.jpg" \
+            -F "size=640" \
+            -F "confidence=0.25" \
+            -F "iou=0.45"
+        ```
+
+    === "Python"
+
+        ```python
+        import requests
+
+        # API URL, use actual MODEL_ID
+        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+
+        # Headers, use actual API_KEY
+        headers = {"x-api-key": "API_KEY"}
+
+        # Inference arguments (optional)
+        data = {"size": 640, "confidence": 0.25, "iou": 0.45}
+
+        # Load image and send request
+        with open("path/to/image.jpg", "rb") as image_file:
+            files = {"image": image_file}
+            response = requests.post(url, headers=headers, files=files, data=data)
+
+        print(response.json())
+        ```
+
+    === "JSON Response"
+
+        ```json
+        {
+          "success": True,
+          "message": "Inference complete.",
+          "data": [
+            {
+              "name": "person",
+              "class": 0,
+              "confidence": 0.8359682559967041,
+              "box": {
+                "x1": 0.08974208831787109,
+                "y1": 0.27418340047200523,
+                "x2": 0.8706787109375,
+                "y2": 0.9887352837456598
+              }
+            },
+            {
+              "name": "person",
+              "class": 0,
+              "confidence": 0.8189555406570435,
+              "box": {
+                "x1": 0.5847355842590332,
+                "y1": 0.05813225640190972,
+                "x2": 0.8930277824401855,
+                "y2": 0.9903111775716146
+              }
+            },
+            {
+              "name": "tie",
+              "class": 27,
+              "confidence": 0.2909725308418274,
+              "box": {
+                "x1": 0.3433395862579346,
+                "y1": 0.6070465511745877,
+                "x2": 0.40964522361755373,
+                "y2": 0.9849439832899306
+              }
+            }
+          ]
+        }
+        ```
+
+### Segment Model Format
+
+YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
+
+!!! Example "Segment Model JSON Response"
+
+    === "`ultralytics`"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load model
+        model = YOLO('yolov8n-seg.pt')
+
+        # Run inference
+        results = model('image.jpg')
+
+        # Print image.jpg results in JSON format
+        print(results[0].tojson())
+        ```
+
+    === "cURL"
+
+        ```bash
+        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
+            -H "x-api-key: API_KEY" \
+            -F "image=@/path/to/image.jpg" \
+            -F "size=640" \
+            -F "confidence=0.25" \
+            -F "iou=0.45"
+        ```
+
+    === "Python"
+
+        ```python
+        import requests
+
+        # API URL, use actual MODEL_ID
+        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+
+        # Headers, use actual API_KEY
+        headers = {"x-api-key": "API_KEY"}
+
+        # Inference arguments (optional)
+        data = {"size": 640, "confidence": 0.25, "iou": 0.45}
+
+        # Load image and send request
+        with open("path/to/image.jpg", "rb") as image_file:
+            files = {"image": image_file}
+            response = requests.post(url, headers=headers, files=files, data=data)
+
+        print(response.json())
+        ```
+
+    === "JSON Response"
+
+        Note `segments` `x` and `y` lengths may vary from one object to another. Larger or more complex objects may have more segment points.
+        ```json
+        {
+          "success": True,
+          "message": "Inference complete.",
+          "data": [
+            {
+              "name": "person",
+              "class": 0,
+              "confidence": 0.856913149356842,
+              "box": {
+                "x1": 0.1064866065979004,
+                "y1": 0.2798851860894097,
+                "x2": 0.8738358497619629,
+                "y2": 0.9894873725043403
+              },
+              "segments": {
+                "x": [
+                  0.421875,
+                  0.4203124940395355,
+                  0.41718751192092896
+                  ...
+                ],
+                "y": [
+                  0.2888889014720917,
+                  0.2916666567325592,
+                  0.2916666567325592
+                  ...
+                ]
+              }
+            },
+            {
+              "name": "person",
+              "class": 0,
+              "confidence": 0.8512625694274902,
+              "box": {
+                "x1": 0.5757311820983887,
+                "y1": 0.053943040635850696,
+                "x2": 0.8960096359252929,
+                "y2": 0.985154045952691
+              },
+              "segments": {
+                "x": [
+                  0.7515624761581421,
+                  0.75,
+                  0.7437499761581421
+                  ...
+                ],
+                "y": [
+                  0.0555555559694767,
+                  0.05833333358168602,
+                  0.05833333358168602
+                  ...
+                ]
+              }
+            },
+            {
+              "name": "tie",
+              "class": 27,
+              "confidence": 0.6485961675643921,
+              "box": {
+                "x1": 0.33911995887756347,
+                "y1": 0.6057066175672743,
+                "x2": 0.4081430912017822,
+                "y2": 0.9916408962673611
+              },
+              "segments": {
+                "x": [
+                  0.37187498807907104,
+                  0.37031251192092896,
+                  0.3687500059604645
+                  ...
+                ],
+                "y": [
+                  0.6111111044883728,
+                  0.6138888597488403,
+                  0.6138888597488403
+                  ...
+                ]
+              }
+            }
+          ]
+        }
+        ```
+
+### Pose Model Format
+
+YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
+
+!!! Example "Pose Model JSON Response"
+
+    === "`ultralytics`"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load model
+        model = YOLO('yolov8n-seg.pt')
+
+        # Run inference
+        results = model('image.jpg')
+
+        # Print image.jpg results in JSON format
+        print(results[0].tojson())
+        ```
+
+    === "cURL"
+
+        ```bash
+        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
+            -H "x-api-key: API_KEY" \
+            -F "image=@/path/to/image.jpg" \
+            -F "size=640" \
+            -F "confidence=0.25" \
+            -F "iou=0.45"
+        ```
+
+    === "Python"
+
+        ```python
+        import requests
+
+        # API URL, use actual MODEL_ID
+        url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
+
+        # Headers, use actual API_KEY
+        headers = {"x-api-key": "API_KEY"}
+
+        # Inference arguments (optional)
+        data = {"size": 640, "confidence": 0.25, "iou": 0.45}
+
+        # Load image and send request
+        with open("path/to/image.jpg", "rb") as image_file:
+            files = {"image": image_file}
+            response = requests.post(url, headers=headers, files=files, data=data)
+
+        print(response.json())
+        ```
+
+    === "JSON Response"
+
+        Note COCO-keypoints pretrained models will have 17 human keypoints. The `visible` part of the keypoints indicates whether a keypoint is visible or obscured. Obscured keypoints may be outside the image or may not be visible, i.e. a person's eyes facing away from the camera.
+        ```json
+        {
+          "success": True,
+          "message": "Inference complete.",
+          "data": [
+            {
+              "name": "person",
+              "class": 0,
+              "confidence": 0.8439509868621826,
+              "box": {
+                "x1": 0.1125,
+                "y1": 0.28194444444444444,
+                "x2": 0.7953125,
+                "y2": 0.9902777777777778
+              },
+              "keypoints": {
+                "x": [
+                  0.5058594942092896,
+                  0.5103894472122192,
+                  0.4920862317085266
+                  ...
+                ],
+                "y": [
+                  0.48964157700538635,
+                  0.4643048942089081,
+                  0.4465252459049225
+                  ...
+                ],
+                "visible": [
+                  0.8726999163627625,
+                  0.653947651386261,
+                  0.9130823612213135
+                  ...
+                ]
+              }
+            },
+            {
+              "name": "person",
+              "class": 0,
+              "confidence": 0.7474289536476135,
+              "box": {
+                "x1": 0.58125,
+                "y1": 0.0625,
+                "x2": 0.8859375,
+                "y2": 0.9888888888888889
+              },
+              "keypoints": {
+                "x": [
+                  0.778544008731842,
+                  0.7976160049438477,
+                  0.7530890107154846
+                  ...
+                ],
+                "y": [
+                  0.27595141530036926,
+                  0.2378823608160019,
+                  0.23644638061523438
+                  ...
+                ],
+                "visible": [
+                  0.8900790810585022,
+                  0.789978563785553,
+                  0.8974530100822449
+                  ...
+                ]
+              }
+            }
+          ]
+        }
+        ```
--- a/docs/en/hub/integrations.md
+++ b/docs/en/hub/integrations.md
+---
+comments: true
+description: Explore integration options for Ultralytics HUB. Currently featuring Roboflow for dataset integration and multiple export formats for your trained models.
+keywords: Ultralytics HUB, Integrations, Roboflow, Dataset, Export, YOLOv5, YOLOv8, ONNX, CoreML, TensorRT, TensorFlow
+---
+
+# HUB Integrations
+
+🚧 **Under Construction** 🚧
+
+Welcome to the Integrations guide for [Ultralytics HUB](https://hub.ultralytics.com/)! We are in the process of expanding this section to provide you with comprehensive guidance on integrating your YOLOv5 and YOLOv8 models with various platforms and formats. Currently, Roboflow is our available dataset integration, with a wide range of export integrations for your trained models.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
+</p>
+
+## Available Integrations
+
+### Dataset Integrations
+
+- **Roboflow**: Seamlessly import your datasets for training.
+
+### Export Integrations
+
+| Format                                                             | `format` Argument | Model                     | Metadata | Arguments                                           |
+|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|
+| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
+| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
+| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
+| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
+| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
+| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`, `int8`                            |
+| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`                                             |
+| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`                             |
+| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
+| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`, `half`, `int8`                             |
+| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+
+## Coming Soon
+
+- Additional Dataset Integrations
+- Detailed Export Integration Guides
+- Step-by-Step Tutorials for Each Integration
+
+## Need Immediate Assistance?
+
+While we're in the process of creating detailed guides:
+
+- Browse through other [HUB Docs](https://docs.ultralytics.com/hub/) for detailed guides and tutorials.
+- Raise an issue on our [GitHub](https://github.com/ultralytics/hub/) for technical support.
+- Join our [Discord Community](https://ultralytics.com/discord/) for live discussions and community support.
+
+We appreciate your patience as we work to make this section comprehensive and user-friendly. Stay tuned for updates!
--- a/docs/en/hub/models.md
+++ b/docs/en/hub/models.md
+---
+comments: true
+description: Learn how to efficiently train AI models using Ultralytics HUB, a streamlined solution for model creation, training, evaluation, and deployment.
+keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
+---
+
+# Ultralytics HUB Models
+
+[Ultralytics HUB](https://hub.ultralytics.com/) models provide a streamlined solution for training vision AI models on custom datasets.
+
+The process is user-friendly and efficient, involving a simple three-step creation and accelerated training powered by Ultralytics YOLOv8. Real-time updates on model metrics are available during training, allowing users to monitor progress at each step. Once training is completed, models can be previewed and easily deployed to real-world applications. Therefore, Ultralytics HUB offers a comprehensive yet straightforward system for model creation, training, evaluation, and deployment.
+
+The entire process of training a model is detailed on our [Cloud Training Page](cloud-training.md).
+
+![Preview of the Models](https://github.com/ultralytics/ultralytics/assets/19519529/a02e1441-f5f6-4935-ad75-ec18e425d8bd)
+
+## Train Model
+
+Navigate to the [Models](https://hub.ultralytics.com/models) page by clicking on the **Models** button in the sidebar.
+
+Training a model using HUB is a 4-step process:
+
+- **Execute the pre-requisites script**: Run the provided scripts to prepare the virtual environment.
+- **Provide the API and start Training**: Once the model is prepared, provide the API key as instructed and execute the code block.
+- **Check the results and Metrics**: Upon successful execution, a link is provided to the Metrics Page. This page offers comprehensive details on the trained model, including specifications, loss metrics, dataset information, and image distributions. Additionally, the 'Deploy' tab provides access to the trained model's documentation and license details.
+- **Test your model**: Ultralytics HUB offers testing using custom images, device cameras, or links to test on `iPhone` or `Android` devices.
+
+![Ultralytics HUB screenshot of the Home page](https://github.com/ultralytics/ultralytics/assets/19519529/61428720-aa93-4689-b209-ead7f06fa488)
+
+!!! tip "Tip"
+
+    You can also train a model directly from the [Home](https://hub.ultralytics.com/home) page.
+
+    ![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Train Model card](https://github.com/ultralytics/ultralytics/assets/19519529/6f9f06f7-e663-4fa7-800c-98675bf1405b)
+
+Click on the **Train Model** button on the top right of the page to trigger the **Train Model** dialog.
+
+The **Train Model** dialog has three simple steps:
+
+### 1. Dataset
+
+Select the dataset for training and click **Continue**.
+
+![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to a dataset and one to the Continue button](https://github.com/ultralytics/ultralytics/assets/19519529/7ff90f2a-c61e-472f-a573-f725a5bddc1c)
+
+### 2. Model
+
+Choose the project, model name, and architecture. Read more about available architectures in our [YOLOv8](https://docs.ultralytics.com/models/yolov8) (and [YOLOv5](https://docs.ultralytics.com/models/yolov5)) documentation.
+
+Click **Continue** when satisfied with the configuration.
+
+![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to a model architecture and one to the Continue button](https://github.com/ultralytics/ultralytics/assets/19519529/a7a412b3-3e87-48de-b117-c506338f36fb)
+
+!!! note "Note"
+
+    By default, your model will use a pre-trained model (trained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset) to reduce training time.
+
+    Advanced options are available to modify this behavior.
+
+## Preview Model
+
+Ultralytics HUB offers various ways to preview trained models.
+
+You can upload an image in the **Test** card under the **Preview** tab to preview your model.
+
+![Ultralytics HUB screenshot of the Preview tab (Test card) inside the Model page](https://github.com/ultralytics/ultralytics/assets/19519529/a732d13a-8da9-40a8-9f5e-c766bec3fbe9)
+
+Use our Ultralytics Cloud API to effortlessly [run inference](inference-api.md) with your custom model.
+
+![Ultralytics HUB screenshot of the Preview tab (Ultralytics Cloud API card) inside the Model page](https://github.com/ultralytics/ultralytics/assets/19519529/77ae0f6c-d89e-433c-b404-77f71c06def5)
+
+Preview your model in real-time on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) device by [downloading](https://ultralytics.com/app_install) our [Ultralytics HUB Mobile Application](app/index.md).
+
+![Ultralytics HUB screenshot of the Deploy tab inside the Model page with an arrow pointing to the Real-Time Preview card](https://github.com/ultralytics/ultralytics/assets/19519529/8d711052-5ab1-43bc-bc25-a8802a24b301)
+
+## Train the model
+
+Ultralytics HUB offers three training options:
+
+- **Ultralytics Cloud** - Learn more about training via the Ultralytics [Cloud Training Page](cloud-training.md)
+- **Google Colab**
+- **Bring your own agent**
+
+## Training the Model on Google Colab
+
+To start training using Google Colab, follow the instructions on the Google Colab notebook.
+
+<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+</a>
+
+![Google Colab Screenshot](https://github.com/ultralytics/ultralytics/assets/19519529/f19d2e04-d33c-446b-91f9-80396e02b68f)
+
+## Bring your own Agent
+
+Create an API endpoint through Ultralytics HUB to train the Model locally. Follow the provided steps, and access training details via the link generated on the Agent terminal.
+
+![Bring your own agent screenshot](https://github.com/ultralytics/ultralytics/assets/19519529/7d8dcd7a-19ec-4ada-87bf-1a1ba1d01ceb)
+
+## Deploy Model
+
+Export your model to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle, and more.
+
+![Ultralytics HUB screenshot of the Deploy tab inside the Model page with all formats exported](https://github.com/ultralytics/ultralytics/assets/19519529/083a929d-2bbd-45f8-9dec-2767949caaba)
+
+## Share Model
+
+Ultralytics HUB's sharing functionality provides a convenient way to share models. Control the general access of your models, setting them to "Private" or "Unlisted".
+
+Navigate to the Model page, open the model actions dropdown, and click on the **Share** option.
+
+![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Share option](https://github.com/ultralytics/ultralytics/assets/19519529/ac98724e-9267-4557-a792-33073c47bbff)
+
+Set the general access and click **Save**.
+
+![Ultralytics HUB screenshot of the Share Model dialog with an arrow pointing to the dropdown and one to the Save button](https://github.com/ultralytics/ultralytics/assets/19519529/65afcd99-1f9e-4be8-b287-096a7c74fc0e)
+
+Now, anyone with the direct link can view your model.
+
+!!! tip "Tip"
+
+    Easily copy the model's link shown in the **Share Model** dialog by clicking on it.
+
+    ![Ultralytics HUB screenshot of the Share Model dialog with an arrow pointing to the model's link](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_share_model_4.jpg)
+
+## Edit and Delete Model
+
+Navigate to the Model page, open the model actions dropdown, and click on the **Edit** option to update the model. To delete the model, select the **Delete** option.
+
+![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Edit option](https://github.com/ultralytics/ultralytics/assets/19519529/5c2db731-45dc-4f04-ac0f-9ad600c140a1)
--- a/docs/en/hub/on-premise/index.md
+++ b/docs/en/hub/on-premise/index.md
+---
+description: Discover what's next for Ultralytics with our under-construction page, previewing new, groundbreaking AI and ML features coming soon.
+keywords: Ultralytics, coming soon, under construction, new features, AI updates, ML advancements, YOLO, technology preview
+---
+
+# Under Construction 🏗️🌟
+
+Welcome to the Ultralytics "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you!
+
+## Exciting New Features on the Way 🎉
+
+- **Innovative Breakthroughs:** Get ready for advanced features and services that will transform your AI and ML experience.
+- **New Horizons:** Anticipate novel products that redefine AI and ML capabilities.
+- **Enhanced Services:** We're upgrading our services for greater efficiency and user-friendliness.
+
+## Stay Updated 🚧
+
+This placeholder page is your first stop for upcoming developments. Keep an eye out for:
+
+- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news.
+- **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers.
+- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights.
+
+## We Value Your Input 🗣️
+
+Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/contact).
+
+## Thank You, Community! 🌍
+
+Your [contributions](https://docs.ultralytics.com/help/contributing) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
+
+---
+
+Excited for what's coming? Bookmark this page and get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖
--- a/docs/en/hub/projects.md
+++ b/docs/en/hub/projects.md
+---
+comments: true
+description: Learn how to manage Ultralytics HUB projects. Understand effective strategies to create, share, edit, delete, and compare models in an organized workspace.
+keywords: Ultralytics, HUB projects, Create project, Edit project, Share project, Delete project, Compare Models, Model Management
+---
+
+# Ultralytics HUB Projects
+
+[Ultralytics HUB](https://hub.ultralytics.com/) projects provide an effective solution for consolidating and managing your models. If you are working with several models that perform similar tasks or have related purposes, Ultralytics HUB projects allow you to group these models together.
+
+This creates a unified and organized workspace that facilitates easier model management, comparison and development. Having similar models or various iterations together can facilitate rapid benchmarking, as you can compare their effectiveness. This can lead to faster, more insightful iterative development and refinement of your models.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Gc6K5eKrTNQ"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
+</p>
+
+## Create Project
+
+Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar.
+
+![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Projects button in the sidebar](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also create a project directly from the [Home](https://hub.ultralytics.com/home) page.
+
+    ![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Create Project card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_2.jpg)
+
+Click on the **Create Project** button on the top right of the page. This action will trigger the **Create Project** dialog, opening up a suite of options for tailoring your project to your needs.
+
+![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Create Project button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_3.jpg)
+
+Type the name of your project in the _Project name_ field or keep the default name and finalize the project creation with a single click.
+
+You have the additional option to enrich your project with a description and a unique image, enhancing its recognizability on the Projects page.
+
+When you're happy with your project configuration, click **Create**.
+
+![Ultralytics HUB screenshot of the Create Project dialog with an arrow pointing to the Create button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_4.jpg)
+
+After your project is created, you will be able to access it from the Projects page.
+
+![Ultralytics HUB screenshot of the Projects page with an arrow pointing to one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_5.jpg)
+
+Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) inside your project.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Train Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_6.jpg)
+
+## Share Project
+
+!!! Info "Info"
+
+    Ultralytics HUB's sharing functionality provides a convenient way to share projects with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
+
+??? note "Note"
+
+    You have control over the general access of your projects.
+
+    You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the project, regardless of whether they have an Ultralytics HUB account or not.
+
+Navigate to the Project page of the project you want to share, open the project actions dropdown and click on the **Share** option. This action will trigger the **Share Project** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Share option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also share a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Share option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_2.jpg)
+
+Set the general access to "Unlisted" and click **Save**.
+
+![Ultralytics HUB screenshot of the Share Project dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_3.jpg)
+
+!!! Warning "Warning"
+
+    When changing the general access of a project, the general access of the models inside the project will be changed as well.
+
+Now, anyone who has the direct link to your project can view it.
+
+??? tip "Tip"
+
+    You can easily click on the project's link shown in the **Share Project** dialog to copy it.
+
+    ![Ultralytics HUB screenshot of the Share Project dialog with an arrow pointing to the project's link](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_4.jpg)
+
+## Edit Project
+
+Navigate to the Project page of the project you want to edit, open the project actions dropdown and click on the **Edit** option. This action will trigger the **Update Project** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Edit option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also edit a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Edit option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_2.jpg)
+
+Apply the desired modifications to your project and then confirm the changes by clicking **Save**.
+
+![Ultralytics HUB screenshot of the Update Project dialog with an arrow pointing to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_3.jpg)
+
+## Delete Project
+
+Navigate to the Project page of the project you want to delete, open the project actions dropdown and click on the **Delete** option. This action will delete the project.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Delete option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also delete a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Delete option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_2.jpg)
+
+!!! Warning "Warning"
+
+    When deleting a project, the models inside the project will be deleted as well.
+
+??? note "Note"
+
+    If you change your mind, you can restore the project from the [Trash](https://hub.ultralytics.com/trash) page.
+
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_3.jpg)
+
+## Compare Models
+
+Navigate to the Project page of the project where the models you want to compare are located. To use the model comparison feature, click on the **Charts** tab.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Charts tab](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_1.jpg)
+
+This will display all the relevant charts. Each chart corresponds to a different metric and contains the performance of each model for that metric. The models are represented by different colors, and you can hover over each data point to get more information.
+
+![Ultralytics HUB screenshot of the Charts tab inside the Project page](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_2.jpg)
+
+??? tip "Tip"
+
+    Each chart can be enlarged for better visualization.
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with an arrow pointing to the expand icon](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_3.jpg)
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with one of the charts expanded](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_4.jpg)
+
+??? tip "Tip"
+
+    You have the flexibility to customize your view by selectively hiding certain models. This feature allows you to concentrate on the models of interest.
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with an arrow pointing to the hide/unhide icon of one of the model](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_5.jpg)
+
+## Reorder Models
+
+??? note "Note"
+
+    Ultralytics HUB's reordering functionality works only inside projects you own.
+
+Navigate to the Project page of the project where the models you want to reorder are located. Click on the designated reorder icon of the model you want to move and drag it to the desired location.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the reorder icon](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_reorder_models_1.jpg)
+
+## Transfer Models
+
+Navigate to the Project page of the project where the model you want to mode is located, open the project actions dropdown and click on the **Transfer** option. This action will trigger the **Transfer Model** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Transfer option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_1.jpg)
+
+??? tip "Tip"
+
+    You can also transfer a model directly from the [Models](https://hub.ultralytics.com/models) page.
+
+    ![Ultralytics HUB screenshot of the Models page with an arrow pointing to the Transfer option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_2.jpg)
+
+Select the project you want to transfer the model to and click **Save**.
+
+![Ultralytics HUB screenshot of the Transfer Model dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_3.jpg)
--- a/docs/en/hub/quickstart.md
+++ b/docs/en/hub/quickstart.md
+---
+comments: true
+description: Kickstart your journey with Ultralytics HUB. Learn how to train and deploy YOLOv5 and YOLOv8 models in seconds with our Quickstart guide.
+keywords: Ultralytics HUB, Quickstart, YOLOv5, YOLOv8, model training, quick deployment, drag-and-drop interface, real-time object detection
+---
+
+# Quickstart Guide for Ultralytics HUB
+
+HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and train new models quickly. It offers a range of pre-trained models and templates to choose from, making it easy for users to get started with training their own models. Once a model is trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
+</p>
+
+## Creating an Account
+
+[Ultralytics HUB](https://hub.ultralytics.com/) offers multiple easy account creation options. Users can register and sign in using Google, Apple, GitHub accounts, or a work email address.
+
+![Creating an Account](https://github.com/ultralytics/ultralytics/assets/19519529/1dcf454a-68ab-4821-9779-ee33a6e300cf)
+
+## The Dashboard
+
+Upon logging in, users are directed to the HUB dashboard, providing a comprehensive overview. The left pane conveniently offers links for tasks such as Uploading Datasets, Creating Projects, Training Models, Integrating Third-party Applications, Accessing Support, and Managing Trash.
+
+![HUB Dashboard](https://github.com/ultralytics/ultralytics/assets/19519529/108de60e-1b21-4f07-8d46-ed51d8439f67)
+
+## Selecting the Model
+
+Choose a Dataset and train the model by selecting the Project name, Model name, and Architecture. Ultralytics offers a range of YOLOv8, YOLOv5, and YOLOv5u6 Architectures, including pre-trained and custom options.
+
+Read more about Models on the [HUB Models page](models.md).
+
+## Training the Model
+
+There are three ways to train your model: using Google Colab, training locally, or through Ultralytics Cloud. Learn more about training options on the [Cloud Training Page](cloud-training.md).
+
+## Integrating the Model
+
+Integrate your trained model with third-party applications or connect HUB from an external agent. Ultralytics HUB currently supports simple one-click API Integration with Roboflow. Read more about integration on the [Integration Page](integrations.md).
+
+## Need Help?
+
+If you encounter any issues or have questions, we're here to assist you. You can report a bug, request a feature, or ask a question.
+
+![Support Page](https://github.com/ultralytics/ultralytics/assets/19519529/c29bf5c5-72d8-4be4-9f3f-b504968d0bef)
+
+## Data Management
+
+Manage your datasets efficiently with options to restore or permanently delete them from the Trash section in the left column.
+
+![Trash Page](https://github.com/ultralytics/ultralytics/assets/19519529/c3d46107-aa58-4b05-a7a8-44db1ad61bb2)
--- a/docs/en/index.md
+++ b/docs/en/index.md
+---
+comments: true
+description: Explore a complete guide to Ultralytics YOLOv8, a high-speed, high-accuracy object detection & image segmentation model. Installation, prediction, training tutorials and more.
+keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine learning, deep learning, computer vision, YOLOv8 installation, YOLOv8 prediction, YOLOv8 training, YOLO history, YOLO licenses
+---
+
+<div align="center">
+  <p>
+    <a href="https://www.ultralytics.com/blog/ultralytics-yolov8-turns-one-a-year-of-breakthroughs-and-innovations" target="_blank">
+    <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
+  </p>
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+  <br>
+  <br>
+  <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+  <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
+  <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
+  <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
+  <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
+  <br>
+  <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
+  <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+  <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+</div>
+
+Introducing [Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLOv8 is built on cutting-edge advancements in deep learning and computer vision, offering unparalleled performance in terms of speed and accuracy. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
+
+Explore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned machine learning practitioner or new to the field, this hub aims to maximize YOLOv8's potential in your projects
+
+## Where to Start
+
+- **Install** `ultralytics` with pip and get up and running in minutes &nbsp; [:material-clock-fast: Get Started](quickstart.md){ .md-button }
+- **Predict** new images and videos with YOLOv8 &nbsp; [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
+- **Train** a new YOLOv8 model on your own custom dataset &nbsp; [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
+- **Tasks** YOLOv8 tasks like segment, classify, pose and track &nbsp; [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
+- **NEW 🚀 Explore** datasets with advanced semantic and SQL search &nbsp; [:material-magnify-expand: Explore a Dataset](datasets/explorer/index.md){ .md-button }
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/LNwODJXcvt4?si=7n1UvGRLSd9p5wKs"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> How to Train a YOLOv8 model on Your Custom Dataset in <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb" target="_blank">Google Colab</a>.
+</p>
+
+## YOLO: A Brief History
+
+[YOLO](https://arxiv.org/abs/1506.02640) (You Only Look Once), a popular object detection and image segmentation model, was developed by Joseph Redmon and Ali Farhadi at the University of Washington. Launched in 2015, YOLO quickly gained popularity for its high speed and accuracy.
+
+- [YOLOv2](https://arxiv.org/abs/1612.08242), released in 2016, improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters.
+- [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf), launched in 2018, further enhanced the model's performance using a more efficient backbone network, multiple anchors and spatial pyramid pooling.
+- [YOLOv4](https://arxiv.org/abs/2004.10934) was released in 2020, introducing innovations like Mosaic data augmentation, a new anchor-free detection head, and a new loss function.
+- [YOLOv5](https://github.com/ultralytics/yolov5) further improved the model's performance and added new features such as hyperparameter optimization, integrated experiment tracking and automatic export to popular export formats.
+- [YOLOv6](https://github.com/meituan/YOLOv6) was open-sourced by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
+- [YOLOv7](https://github.com/WongKinYiu/yolov7) added additional tasks such as pose estimation on the COCO keypoints dataset.
+- [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
+- [YOLOv9](models/yolov9.md) Introduces innovative methods like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).
+
+## YOLO Licenses: How is Ultralytics YOLO licensed?
+
+Ultralytics offers two licensing options to accommodate diverse use cases:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
+- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license).
+
+Our licensing strategy is designed to ensure that any improvements to our open-source projects are returned to the community. We hold the principles of open source close to our hearts ❤️, and our mission is to guarantee that our contributions can be utilized and expanded upon in ways that are beneficial to all.
--- a/docs/en/integrations/amazon-sagemaker.md
+++ b/docs/en/integrations/amazon-sagemaker.md
+---
+comments: true
+Description: Learn how to deploy YOLOv8 models on Amazon SageMaker Endpoints. This guide covers the essentials of AWS environment setup, model preparation, and deployment using AWS CloudFormation and the AWS Cloud Development Kit (CDK).
+keywords: YOLOv8, Ultralytics, Amazon SageMaker, AWS, CloudFormation, AWS CDK, PyTorch, Model Deployment, Machine Learning, Computer Vision
+---
+
+# A Guide to Deploying YOLOv8 on Amazon SageMaker Endpoints
+
+Deploying advanced computer vision models like [Ultralytics’ YOLOv8](https://github.com/ultralytics/ultralytics) on Amazon SageMaker Endpoints opens up a wide range of possibilities for various machine learning applications. The key to effectively using these models lies in understanding their setup, configuration, and deployment processes. YOLOv8 becomes even more powerful when integrated seamlessly with Amazon SageMaker, a robust and scalable machine learning service by AWS.
+
+This guide will take you through the process of deploying YOLOv8 PyTorch models on Amazon SageMaker Endpoints step by step. You'll learn the essentials of preparing your AWS environment, configuring the model appropriately, and using tools like AWS CloudFormation and the AWS Cloud Development Kit (CDK) for deployment.
+
+## Amazon SageMaker
+
+<p align="center">
+  <img width="640" src="https://d1.awsstatic.com/sagemaker/Amazon-SageMaker-Studio%402x.aa0572ebf4ea9237571644c7f853c914c1d0c985.png" alt="Amazon SageMaker Overview">
+</p>
+
+[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a machine learning service from Amazon Web Services (AWS) that simplifies the process of building, training, and deploying machine learning models. It provides a broad range of tools for handling various aspects of machine learning workflows. This includes automated features for tuning models, options for training models at scale, and straightforward methods for deploying models into production. SageMaker supports popular machine learning frameworks, offering the flexibility needed for diverse projects. Its features also cover data labeling, workflow management, and performance analysis.
+
+## Deploying YOLOv8 on Amazon SageMaker Endpoints
+
+Deploying YOLOv8 on Amazon SageMaker lets you use its managed environment for real-time inference and take advantage of features like autoscaling. Take a look at the AWS architecture below.
+
+<p align="center">
+  <img width="640" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/02/28/ML13353_AWSArchitecture-1024x605.png" alt="AWS Architecture">
+</p>
+
+### Step 1: Setup Your AWS Environment
+
+First, ensure you have the following prerequisites in place:
+
+- An AWS Account: If you don't already have one, sign up for an AWS account.
+
+- Configured IAM Roles: You’ll need an IAM role with the necessary permissions for Amazon SageMaker, AWS CloudFormation, and Amazon S3. This role should have policies that allow it to access these services.
+
+- AWS CLI: If not already installed, download and install the AWS Command Line Interface (CLI) and configure it with your account details. Follow [the AWS CLI instructions](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) for installation.
+
+- AWS CDK: If not already installed, install the AWS Cloud Development Kit (CDK), which will be used for scripting the deployment. Follow [the AWS CDK instructions](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html#getting_started_install) for installation.
+
+- Adequate Service Quota: Confirm that you have sufficient quotas for two separate resources in Amazon SageMaker: one for `ml.m5.4xlarge` for endpoint usage and another for `ml.m5.4xlarge` for notebook instance usage. Each of these requires a minimum of one quota value. If your current quotas are below this requirement, it's important to request an increase for each. You can request a quota increase by following the detailed instructions in the [AWS Service Quotas documentation](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html#quota-console-increase).
+
+### Step 2: Clone the YOLOv8 SageMaker Repository
+
+The next step is to clone the specific AWS repository that contains the resources for deploying YOLOv8 on SageMaker. This repository, hosted on GitHub, includes the necessary CDK scripts and configuration files.
+
+- Clone the GitHub Repository: Execute the following command in your terminal to clone the host-yolov8-on-sagemaker-endpoint repository:
+
+```bash
+git clone https://github.com/aws-samples/host-yolov8-on-sagemaker-endpoint.git
+```
+
+- Navigate to the Cloned Directory: Change your directory to the cloned repository:
+
+```bash
+cd host-yolov8-on-sagemaker-endpoint/yolov8-pytorch-cdk
+```
+
+### Step 3: Set Up the CDK Environment
+
+Now that you have the necessary code, set up your environment for deploying with AWS CDK.
+
+- Create a Python Virtual Environment: This isolates your Python environment and dependencies. Run:
+
+```bash
+python3 -m venv .venv
+```
+
+- Activate the Virtual Environment:
+
+```bash
+source .venv/bin/activate
+```
+
+- Install Dependencies: Install the required Python dependencies for the project:
+
+```bash
+pip3 install -r requirements.txt
+```
+
+- Upgrade AWS CDK Library: Ensure you have the latest version of the AWS CDK library:
+
+```bash
+pip install --upgrade aws-cdk-lib
+```
+
+### Step 4: Create the AWS CloudFormation Stack
+
+- Synthesize the CDK Application: Generate the AWS CloudFormation template from your CDK code:
+
+```bash
+cdk synth
+```
+
+- Bootstrap the CDK Application: Prepare your AWS environment for CDK deployment:
+
+```bash
+cdk bootstrap
+```
+
+- Deploy the Stack: This will create the necessary AWS resources and deploy your model:
+
+```bash
+cdk deploy
+```
+
+### Step 5: Deploy the YOLOv8 Model
+
+Before diving into the deployment instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
+
+After creating the AWS CloudFormation Stack, the next step is to deploy YOLOv8.
+
+- Open the Notebook Instance: Go to the AWS Console and navigate to the Amazon SageMaker service. Select "Notebook Instances" from the dashboard, then locate the notebook instance that was created by your CDK deployment script. Open the notebook instance to access the Jupyter environment.
+
+- Access and Modify inference.py: After opening the SageMaker notebook instance in Jupyter, locate the inference.py file. Edit the output_fn function in inference.py as shown below and save your changes to the script, ensuring that there are no syntax errors.
+
+```python
+import json
+
+def output_fn(prediction_output, content_type):
+    print("Executing output_fn from inference.py ...")
+    infer = {}
+    for result in prediction_output:
+        if result.boxes is not None:
+            infer['boxes'] = result.boxes.numpy().data.tolist()
+        if result.masks is not None:
+            infer['masks'] = result.masks.numpy().data.tolist()
+        if result.keypoints is not None:
+            infer['keypoints'] = result.keypoints.numpy().data.tolist()
+        if result.obb is not None:
+            infer['obb'] = result.obb.numpy().data.tolist()
+        if result.probs is not None:
+            infer['probs'] = result.probs.numpy().data.tolist()
+    return json.dumps(infer)
+```
+
+- Deploy the Endpoint Using 1_DeployEndpoint.ipynb: In the Jupyter environment, open the 1_DeployEndpoint.ipynb notebook located in the sm-notebook directory. Follow the instructions in the notebook and run the cells to download the YOLOv8 model, package it with the updated inference code, and upload it to an Amazon S3 bucket. The notebook will guide you through creating and deploying a SageMaker endpoint for the YOLOv8 model.
+
+### Step 6: Testing Your Deployment
+
+Now that your YOLOv8 model is deployed, it's important to test its performance and functionality.
+
+- Open the Test Notebook: In the same Jupyter environment, locate and open the 2_TestEndpoint.ipynb notebook, also in the sm-notebook directory.
+
+- Run the Test Notebook: Follow the instructions within the notebook to test the deployed SageMaker endpoint. This includes sending an image to the endpoint and running inferences. Then, you’ll plot the output to visualize the model’s performance and accuracy, as shown below.
+
+<p align="center">
+  <img width="640" src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/02/28/ML13353_InferenceOutput.png" alt="Testing Results YOLOv8">
+</p>
+
+- Clean-Up Resources: The test notebook will also guide you through the process of cleaning up the endpoint and the hosted model. This is an important step to manage costs and resources effectively, especially if you do not plan to use the deployed model immediately.
+
+### Step 7: Monitoring and Management
+
+After testing, continuous monitoring and management of your deployed model are essential.
+
+- Monitor with Amazon CloudWatch: Regularly check the performance and health of your SageMaker endpoint using [Amazon CloudWatch](https://aws.amazon.com/cloudwatch/).
+
+- Manage the Endpoint: Use the SageMaker console for ongoing management of the endpoint. This includes scaling, updating, or redeploying the model as required.
+
+By completing these steps, you will have successfully deployed and tested a YOLOv8 model on Amazon SageMaker Endpoints. This process not only equips you with practical experience in using AWS services for machine learning deployment but also lays the foundation for deploying other advanced models in the future.
+
+## Summary
+
+This guide took you step by step through deploying YOLOv8 on Amazon SageMaker Endpoints using AWS CloudFormation and the AWS Cloud Development Kit (CDK). The process includes cloning the necessary GitHub repository, setting up the CDK environment, deploying the model using AWS services, and testing its performance on SageMaker.
+
+For more technical details, refer to [this article](https://aws.amazon.com/blogs/machine-learning/hosting-yolov8-pytorch-model-on-amazon-sagemaker-endpoints/) on the AWS Machine Learning Blog. You can also check out the official [Amazon SageMaker Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) for more insights into various features and functionalities.
+
+Are you interested in learning more about different YOLOv8 integrations? Visit the [Ultralytics integrations guide page](../integrations/index.md) to discover additional tools and capabilities that can enhance your machine-learning projects.
--- a/docs/en/integrations/clearml.md
+++ b/docs/en/integrations/clearml.md
+---
+comments: true
+description: Learn how to streamline and optimize your YOLOv8 model training with ClearML. This guide provides insights into integrating ClearML's MLOps tools for efficient model training, from initial setup to advanced experiment tracking and model management.
+keywords: Ultralytics, YOLOv8, Object Detection, ClearML, Model Training, MLOps, Experiment Tracking, Workflow Optimization
+---
+
+# Training YOLOv8 with ClearML: Streamlining Your MLOps Workflow
+
+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.
+
+## ClearML
+
+<p align="center">
+  <img width="100%" src="https://clear.ml/wp-content/uploads/2023/06/DataOps@2x-1.png" alt="ClearML Overview">
+</p>
+
+[ClearML](https://clear.ml/) is an innovative open-source MLOps platform that is skillfully designed to automate, monitor, and orchestrate machine learning workflows. Its key features include automated logging of all training and inference data for full experiment reproducibility, an intuitive web UI for easy data visualization and analysis, advanced hyperparameter optimization algorithms, and robust model management for efficient deployment across various platforms.
+
+## YOLOv8 Training with ClearML
+
+You can bring automation and efficiency to your machine learning workflow by improving your training process by integrating YOLOv8 with ClearML.
+
+## Installation
+
+To install the required packages, run:
+
+!!! Tip "Installation"
+
+    === "CLI"
+
+        ```bash
+        # Install the required packages for YOLOv8 and ClearML
+        pip install ultralytics clearml
+        ```
+
+For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
+
+## Configuring ClearML
+
+Once you have installed the necessary packages, the next step is to initialize and configure your ClearML SDK. This involves setting up your ClearML account and obtaining the necessary credentials for a seamless connection between your development environment and the ClearML server.
+
+Begin by initializing the ClearML SDK in your environment. The ‘clearml-init’ command starts the setup process and prompts you for the necessary credentials.
+
+!!! Tip "Initial SDK Setup"
+
+    === "CLI"
+
+        ```bash
+        # Initialize your ClearML SDK setup process
+        clearml-init
+        ```
+
+After executing this command, visit the [ClearML Settings page](https://app.clear.ml/settings/workspace-configuration). Navigate to the top right corner and select "Settings." Go to the "Workspace" section and click on "Create new credentials." Use the credentials provided in the "Create Credentials" pop-up to complete the setup as instructed, depending on whether you are configuring ClearML in a Jupyter Notebook or a local Python environment.
+
+## Usage
+
+Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
+
+!!! Example "Usage"
+
+    === "Python"
+
+        ```python
+        from clearml import Task
+        from ultralytics import YOLO
+
+        # Step 1: Creating a ClearML Task
+        task = Task.init(
+            project_name="my_project",
+            task_name="my_yolov8_task"
+        )
+
+        # Step 2: Selecting the YOLOv8 Model
+        model_variant = "yolov8n"
+        task.set_parameter("model_variant", model_variant)
+
+        # Step 3: Loading the YOLOv8 Model
+        model = YOLO(f'{model_variant}.pt')
+
+        # Step 4: Setting Up Training Arguments
+        args = dict(data="coco128.yaml", epochs=16)
+        task.connect(args)
+
+        # Step 5: Initiating Model Training
+        results = model.train(**args)
+        ```
+
+### Understanding the Code
+
+Let’s understand the steps showcased in the usage code snippet above.
+
+**Step 1: Creating a ClearML Task**: A new task is initialized in ClearML, specifying your project and task names. This task will track and manage your model's training.
+
+**Step 2: Selecting the YOLOv8 Model**: The `model_variant` variable is set to 'yolov8n', one of the YOLOv8 models. This variant is then logged in ClearML for tracking.
+
+**Step 3: Loading the YOLOv8 Model**: The selected YOLOv8 model is loaded using Ultralytics' YOLO class, preparing it for training.
+
+**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco128.yaml`) and the number of epochs (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+
+**Step 5: Initiating Model Training**: The model training is started with the specified arguments. The results of the training process are captured in the `results` variable.
+
+### Understanding the Output
+
+Upon running the usage code snippet above, you can expect the following output:
+
+- A confirmation message indicating the creation of a new ClearML task, along with its unique ID.
+- An informational message about the script code being stored, indicating that the code execution is being tracked by ClearML.
+- A URL link to the ClearML results page where you can monitor the training progress and view detailed logs.
+- Download progress for the YOLOv8 model and the specified dataset, followed by a summary of the model architecture and training configuration.
+- Initialization messages for various training components like TensorBoard, Automatic Mixed Precision (AMP), and dataset preparation.
+- Finally, the training process starts, with progress updates as the model trains on the specified dataset. For an in-depth understanding of the performance metrics used during training, read [our guide on performance metrics](../guides/yolo-performance-metrics.md).
+
+### Viewing the ClearML Results Page
+
+By clicking on the URL link to the ClearML results page in the output of the usage code snippet, you can access a comprehensive view of your model's training process.
+
+#### Key Features of the ClearML Results Page
+
+- **Real-Time Metrics Tracking**
+
+    - Track critical metrics like loss, accuracy, and validation scores as they occur.
+    - Provides immediate feedback for timely model performance adjustments.
+
+- **Experiment Comparison**
+
+    - Compare different training runs side-by-side.
+    - Essential for hyperparameter tuning and identifying the most effective models.
+
+- **Detailed Logs and Outputs**
+
+    - Access comprehensive logs, graphical representations of metrics, and console outputs.
+    - Gain a deeper understanding of model behavior and issue resolution.
+
+- **Resource Utilization Monitoring**
+
+    - Monitor the utilization of computational resources, including CPU, GPU, and memory.
+    - Key to optimizing training efficiency and costs.
+
+- **Model Artifacts Management**
+
+    - View, download, and share model artifacts like trained models and checkpoints.
+    - Enhances collaboration and streamlines model deployment and sharing.
+
+For a visual walkthrough of what the ClearML Results Page looks like, watch the video below:
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/iLcC7m3bCes?si=oSEAoZbrg8inCg_2"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> YOLOv8 MLOps Integration using ClearML
+</p>
+
+### Advanced Features in ClearML
+
+ClearML offers several advanced features to enhance your MLOps experience.
+
+#### Remote Execution
+
+ClearML's remote execution feature facilitates the reproduction and manipulation of experiments on different machines. It logs essential details like installed packages and uncommitted changes. When a task is enqueued, the ClearML Agent pulls it, recreates the environment, and runs the experiment, reporting back with detailed results.
+
+Deploying a ClearML Agent is straightforward and can be done on various machines using the following command:
+
+```bash
+clearml-agent daemon --queue <queues_to_listen_to> [--docker]
+```
+
+This setup is applicable to cloud VMs, local GPUs, or laptops. ClearML Autoscalers help manage cloud workloads on platforms like AWS, GCP, and Azure, automating the deployment of agents and adjusting resources based on your resource budget.
+
+### Cloning, Editing, and Enqueuing
+
+ClearML's user-friendly interface allows easy cloning, editing, and enqueuing of tasks. Users can clone an existing experiment, adjust parameters or other details through the UI, and enqueue the task for execution. This streamlined process ensures that the ClearML Agent executing the task uses updated configurations, making it ideal for iterative experimentation and model fine-tuning.
+
+<p align="center"><br>
+  <img width="100%" src="https://clear.ml/docs/latest/assets/images/integrations_yolov5-2483adea91df4d41bfdf1a37d28864d4.gif" alt="Cloning, Editing, and Enqueuing with ClearML">
+</p>
+
+## Summary
+
+This guide has led you through the process of integrating ClearML with Ultralytics' YOLOv8. Covering everything from initial setup to advanced model management, you've discovered how to leverage ClearML for efficient training, experiment tracking, and workflow optimization in your machine learning projects.
+
+For further details on usage, visit [ClearML's official documentation](https://clear.ml/docs/latest/docs/integrations/yolov8/).
+
+Additionally, explore more integrations and capabilities of Ultralytics by visiting the [Ultralytics integration guide page](../integrations/index.md), which is a treasure trove of resources and insights.