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docs/en/guides/data-collection-and-annotation.md
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@ -137,7 +137,7 @@ Bouncing your ideas and queries off other computer vision enthusiasts can help a
### Where to Find Help and Support
- **GitHub Issues:** Visit the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
### Official Documentation

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docs/en/guides/defining-project-goals.md
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@ -115,7 +115,7 @@ Connecting with other computer vision enthusiasts can be incredibly helpful for
### Community Support Channels
- **GitHub Issues:** Head over to the YOLOv8 GitHub repository. You can use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers can assist with specific problems you encounter.
- **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://ultralytics.com/discord/). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas.
- **Ultralytics Discord Server:** Become part of the [Ultralytics Discord server](https://discord.com/invite/ultralytics). Connect with fellow users and developers, seek support, exchange knowledge, and discuss ideas.
### Comprehensive Guides and Documentation

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docs/en/guides/docker-quickstart.md
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@ -10,7 +10,7 @@ keywords: Ultralytics, Docker, Quickstart Guide, CPU support, GPU support, NVIDI
<img width="800" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-docker-package-visual.avif" alt="Ultralytics Docker Package Visual">
</p>
This guide serves as a comprehensive introduction to setting up a Docker environment for your Ultralytics projects. [Docker](https://docker.com/) is a platform for developing, shipping, and running applications in containers. It is particularly beneficial for ensuring that the software will always run the same, regardless of where it's deployed. For more details, visit the Ultralytics Docker repository on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics).
This guide serves as a comprehensive introduction to setting up a Docker environment for your Ultralytics projects. [Docker](https://www.docker.com/) is a platform for developing, shipping, and running applications in containers. It is particularly beneficial for ensuring that the software will always run the same, regardless of where it's deployed. For more details, visit the Ultralytics Docker repository on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics).
[![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
[![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics)](https://hub.docker.com/r/ultralytics/ultralytics)
@ -27,7 +27,7 @@ This guide serves as a comprehensive introduction to setting up a Docker environ
## Prerequisites
- Make sure Docker is installed on your system. If not, you can download and install it from [Docker's website](https://www.docker.com/products/docker-desktop).
- Make sure Docker is installed on your system. If not, you can download and install it from [Docker's website](https://www.docker.com/products/docker-desktop/).
- Ensure that your system has an NVIDIA GPU and NVIDIA drivers are installed.
---

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@ -204,7 +204,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md)
3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md)
For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord).
For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://discord.com/invite/ultralytics).
## FAQ

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@ -288,7 +288,7 @@ When you're getting started with YOLOv8, having a helpful community and support
- **GitHub Discussions:** The YOLOv8 repository on GitHub has a "Discussions" section where you can ask questions, report issues, and suggest improvements.
- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://ultralytics.com/discord/) where you can interact with other users and developers.
- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and developers.
### Official Documentation and Resources

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docs/en/guides/model-deployment-practices.md
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@ -122,7 +122,7 @@ Being part of a community of computer vision enthusiasts can help you solve prob
### Community Resources
- **GitHub Issues:** Explore the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
### Official Documentation

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docs/en/guides/model-evaluation-insights.md
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@ -128,7 +128,7 @@ Sharing your ideas and questions with other computer vision enthusiasts can insp
### Finding Help and Support
- **GitHub Issues:** Explore the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to ask questions, report bugs, and suggest features. The community and maintainers are available to assist with any issues you encounter.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
### Official Documentation

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docs/en/guides/model-monitoring-and-maintenance.md
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@ -124,7 +124,7 @@ Joining a community of computer vision enthusiasts can help you solve problems a
### Community Resources
- **GitHub Issues:** Check out the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are highly active and supportive.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
### Official Documentation

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@ -129,7 +129,7 @@ Becoming part of a community of computer vision enthusiasts can aid in solving p
### Community Resources
- **GitHub Issues:** Explore the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
### Official Documentation

2
docs/en/guides/model-training-tips.md
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@ -147,7 +147,7 @@ Being part of a community of computer vision enthusiasts can help you solve prob
### Community Resources
- **GitHub Issues:** Visit the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The community and maintainers are very active and ready to help.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to chat with other users and developers, get support, and share your experiences.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to chat with other users and developers, get support, and share your experiences.
### Official Documentation

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docs/en/guides/nvidia-jetson.md
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@ -54,7 +54,7 @@ The first step after getting your hands on an NVIDIA Jetson device is to flash N
1. If you own an official NVIDIA Development Kit such as the Jetson Orin Nano Developer Kit, you can [download an image and prepare an SD card with JetPack for booting the device](https://developer.nvidia.com/embedded/learn/get-started-jetson-orin-nano-devkit).
2. If you own any other NVIDIA Development Kit, you can [flash JetPack to the device using SDK Manager](https://docs.nvidia.com/sdk-manager/install-with-sdkm-jetson/index.html).
3. If you own a Seeed Studio reComputer J4012 device, you can [flash JetPack to the included SSD](https://wiki.seeedstudio.com/reComputer_J4012_Flash_Jetpack) and if you own a Seeed Studio reComputer J1020 v2 device, you can [flash JetPack to the eMMC/ SSD](https://wiki.seeedstudio.com/reComputer_J2021_J202_Flash_Jetpack).
3. If you own a Seeed Studio reComputer J4012 device, you can [flash JetPack to the included SSD](https://wiki.seeedstudio.com/reComputer_J4012_Flash_Jetpack/) and if you own a Seeed Studio reComputer J1020 v2 device, you can [flash JetPack to the eMMC/ SSD](https://wiki.seeedstudio.com/reComputer_J2021_J202_Flash_Jetpack/).
4. If you own any other third party device powered by the NVIDIA Jetson module, it is recommended to follow [command-line flashing](https://docs.nvidia.com/jetson/archives/r35.5.0/DeveloperGuide/IN/QuickStart.html).
!!! Note

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docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md
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@ -115,7 +115,7 @@ Balancing latency and throughput optimization requires understanding your applic
- **Latency Optimization:** Ideal for real-time applications requiring immediate responses (e.g., consumer-grade apps).
- **Throughput Optimization:** Best for scenarios with many concurrent inferences, maximizing resource use (e.g., large-scale deployments).
Using OpenVINO's high-level performance hints and multi-device modes can help strike the right balance. Choose the appropriate [OpenVINO Performance hints](https://docs.ultralytics.com/integrations/openvino#openvino-performance-hints) based on your specific requirements.
Using OpenVINO's high-level performance hints and multi-device modes can help strike the right balance. Choose the appropriate [OpenVINO Performance hints](https://docs.ultralytics.com/integrations/openvino/#openvino-performance-hints) based on your specific requirements.
### Can I use Ultralytics YOLO models with other AI frameworks besides OpenVINO?

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@ -133,7 +133,7 @@ Having discussions about your project with other computer vision enthusiasts can
### Channels to Connect with the Community
- **GitHub Issues:** Visit the YOLOv8 GitHub repository and use the [Issues tab](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features. The community and maintainers are there to help with any issues you face.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
### Official Documentation

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docs/en/guides/raspberry-pi.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, Raspberry Pi, setup, guide, benchmarks, computer
# Quick Start Guide: Raspberry Pi with Ultralytics YOLOv8
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [Raspberry Pi](https://www.raspberrypi.com) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLOv8 on these small and powerful devices.
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [Raspberry Pi](https://www.raspberrypi.com/) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLOv8 on these small and powerful devices.
<p align="center">
<br>

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docs/en/guides/steps-of-a-cv-project.md
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@ -189,7 +189,7 @@ Connecting with a community of computer vision enthusiasts can help you tackle a
### Community Resources
- **GitHub Issues:** Check out the [YOLOv8 GitHub repository](https://github.com/ultralytics/ultralytics/issues) and use the Issues tab to ask questions, report bugs, and suggest new features. The active community and maintainers are there to help with specific issues.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to interact with other users and developers, get support, and share insights.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to interact with other users and developers, get support, and share insights.
### Official Documentation

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docs/en/guides/streamlit-live-inference.md
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@ -86,7 +86,7 @@ Engage with the community to learn more, troubleshoot issues, and share your pro
### Where to Find Help and Support
- **GitHub Issues:** Visit the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics/issues) to raise questions, report bugs, and suggest features.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://ultralytics.com/discord/) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
- **Ultralytics Discord Server:** Join the [Ultralytics Discord server](https://discord.com/invite/ultralytics) to connect with other users and developers, get support, share knowledge, and brainstorm ideas.
### Official Documentation

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@ -6,7 +6,7 @@ keywords: Triton Inference Server, YOLOv8, Ultralytics, NVIDIA, deep learning, A
# Triton Inference Server with Ultralytics YOLOv8
The [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) (formerly known as TensorRT Inference Server) is an open-source software solution developed by NVIDIA. It provides a cloud inference solution optimized for NVIDIA GPUs. Triton simplifies the deployment of AI models at scale in production. Integrating Ultralytics YOLOv8 with Triton Inference Server allows you to deploy scalable, high-performance deep learning inference workloads. This guide provides steps to set up and test the integration.
The [Triton Inference Server](https://developer.nvidia.com/triton-inference-server) (formerly known as TensorRT Inference Server) is an open-source software solution developed by NVIDIA. It provides a cloud inference solution optimized for NVIDIA GPUs. Triton simplifies the deployment of AI models at scale in production. Integrating Ultralytics YOLOv8 with Triton Inference Server allows you to deploy scalable, high-performance deep learning inference workloads. This guide provides steps to set up and test the integration.
<p align="center">
<br>
@ -147,7 +147,7 @@ By following the above steps, you can deploy and run Ultralytics YOLOv8 models e
### How do I set up Ultralytics YOLOv8 with NVIDIA Triton Inference Server?
Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) involves a few key steps:
Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps:
1. **Export YOLOv8 to ONNX format**:
@ -213,7 +213,7 @@ This setup can help you efficiently deploy YOLOv8 models at scale on Triton Infe
### What benefits does using Ultralytics YOLOv8 with NVIDIA Triton Inference Server offer?
Integrating [Ultralytics YOLOv8](../models/yolov8.md) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) provides several advantages:
Integrating [Ultralytics YOLOv8](../models/yolov8.md) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) provides several advantages:
- **Scalable AI Inference**: Triton allows serving multiple models from a single server instance, supporting dynamic model loading and unloading, making it highly scalable for diverse AI workloads.
- **High Performance**: Optimized for NVIDIA GPUs, Triton Inference Server ensures high-speed inference operations, perfect for real-time applications such as object detection.
@ -223,7 +223,7 @@ For detailed instructions on setting up and running YOLOv8 with Triton, you can
### Why should I export my YOLOv8 model to ONNX format before using Triton Inference Server?
Using ONNX (Open Neural Network Exchange) format for your [Ultralytics YOLOv8](../models/yolov8.md) model before deploying it on [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) offers several key benefits:
Using ONNX (Open Neural Network Exchange) format for your [Ultralytics YOLOv8](../models/yolov8.md) model before deploying it on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) offers several key benefits:
- **Interoperability**: ONNX format supports transfer between different deep learning frameworks (such as PyTorch, TensorFlow), ensuring broader compatibility.
- **Optimization**: Many deployment environments, including Triton, optimize for ONNX, enabling faster inference and better performance.
@ -242,7 +242,7 @@ You can follow the steps in the [exporting guide](../modes/export.md) to complet
### Can I run inference using the Ultralytics YOLOv8 model on Triton Inference Server?
Yes, you can run inference using the [Ultralytics YOLOv8](../models/yolov8.md) model on [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server). Once your model is set up in the Triton Model Repository and the server is running, you can load and run inference on your model as follows:
Yes, you can run inference using the [Ultralytics YOLOv8](../models/yolov8.md) model on [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server). Once your model is set up in the Triton Model Repository and the server is running, you can load and run inference on your model as follows:
```python
from ultralytics import YOLO

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@ -121,7 +121,7 @@ You can access these metrics from the training logs or by using tools like Tenso
- [TensorBoard](https://www.tensorflow.org/tensorboard): TensorBoard is a popular choice for visualizing training metrics, including loss, accuracy, and more. You can integrate it with your YOLOv8 training process.
- [Comet](https://bit.ly/yolov8-readme-comet): Comet provides an extensive toolkit for experiment tracking and comparison. It allows you to track metrics, hyperparameters, and even model weights. Integration with YOLO models is also straightforward, providing you with a complete overview of your experiment cycle.
- [Ultralytics HUB](https://hub.ultralytics.com): Ultralytics HUB offers a specialized environment for tracking YOLO models, giving you a one-stop platform to manage metrics, datasets, and even collaborate with your team. Given its tailored focus on YOLO, it offers more customized tracking options.
- [Ultralytics HUB](https://hub.ultralytics.com/): Ultralytics HUB offers a specialized environment for tracking YOLO models, giving you a one-stop platform to manage metrics, datasets, and even collaborate with your team. Given its tailored focus on YOLO, it offers more customized tracking options.
Each of these tools offers its own set of advantages, so you may want to consider the specific needs of your project when making a choice.
@ -270,7 +270,7 @@ Engaging with a community of like-minded individuals can significantly enhance y
**GitHub Issues:** The YOLOv8 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
**Ultralytics Discord Server:** Ultralytics has a [Discord server](https://ultralytics.com/discord/) where you can interact with other users and the developers.
**Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and the developers.
### Official Documentation and Resources
@ -312,7 +312,7 @@ This sets the training process to the first GPU. Consult the `nvidia-smi` comman
### How can I monitor and track my YOLOv8 model training progress?
Tracking and visualizing training progress can be efficiently managed through tools like [TensorBoard](https://www.tensorflow.org/tensorboard), [Comet](https://bit.ly/yolov8-readme-comet), and [Ultralytics HUB](https://hub.ultralytics.com). These tools allow you to log and visualize metrics such as loss, precision, recall, and mAP. Implementing [early stopping](#continuous-monitoring-parameters) based on these metrics can also help achieve better training outcomes.
Tracking and visualizing training progress can be efficiently managed through tools like [TensorBoard](https://www.tensorflow.org/tensorboard), [Comet](https://bit.ly/yolov8-readme-comet), and [Ultralytics HUB](https://hub.ultralytics.com/). These tools allow you to log and visualize metrics such as loss, precision, recall, and mAP. Implementing [early stopping](#continuous-monitoring-parameters) based on these metrics can also help achieve better training outcomes.
### What should I do if YOLOv8 is not recognizing my dataset format?

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@ -159,7 +159,7 @@ Tapping into a community of enthusiasts and experts can amplify your journey wit
- **GitHub Issues:** The YOLOv8 repository on GitHub has an [Issues tab](https://github.com/ultralytics/ultralytics/issues) where you can ask questions, report bugs, and suggest new features. The community and maintainers are active here, and it's a great place to get help with specific problems.
- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://ultralytics.com/discord/) where you can interact with other users and the developers.
- **Ultralytics Discord Server:** Ultralytics has a [Discord server](https://discord.com/invite/ultralytics) where you can interact with other users and the developers.
### Official Documentation and Resources: