description: Discover Ultralytics HUB, the all-in-one web tool for training and deploying YOLOv5 and YOLOv8 models. Get started quickly with pre-trained models and user-friendly features.
keywords: Ultralytics HUB, YOLO models, train YOLO, YOLOv5, YOLOv8, object detection, model deployment, machine learning, deep learning, AI tools, dataset upload, model training
👋 Hello from the [Ultralytics](https://www.ultralytics.com/) Team! We've been working hard these last few months to launch [Ultralytics HUB](https://www.ultralytics.com/hub), a new web tool for training and deploying all your YOLOv5 and YOLOv8 🚀 models from one spot!
We hope that the resources here will help you get the most out of HUB. Please browse the HUB <ahref="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <ahref="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <ahref="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
[Ultralytics HUB](https://www.ultralytics.com/hub) is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It also offers a range of pre-trained models to choose from, making it extremely easy for users to get started. Once a model is trained, it can be effortlessly previewed in the [Ultralytics HUB App](app/index.md) before being deployed for real-time classification, object detection, and instance segmentation tasks.
We hope that the resources here will help you get the most out of HUB. Please browse the HUB <ahref="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <ahref="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <ahref="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
- **User-Friendly Interface:** Intuitive design for easy dataset uploads and model training.
- **Pre-Trained Models:** Access to a variety of pre-trained YOLOv5 and YOLOv8 models.
- **Cloud Training:** Seamless cloud training capabilities, detailed on the [Cloud Training](cloud-training.md) page.
- **Real-Time Deployment:** Effortlessly deploy models for real-time applications using the [Ultralytics HUB App](app/index.md).
- **Team Collaboration:** Collaborate with your team efficiently through the [Teams](teams.md) feature.
Explore more about the advantages in our [Ultralytics HUB Blog](https://www.ultralytics.com/blog/ultralytics-hub-a-game-changer-for-computer-vision).
### Can I use Ultralytics HUB for object detection on mobile devices?
Yes, Ultralytics HUB supports object detection on mobile devices. You can run YOLOv5 and YOLOv8 models on both iOS and Android devices using the Ultralytics HUB App. For more details:
- **iOS:** Learn about CoreML acceleration on iPhones and iPads in the [iOS](app/ios.md) section.
- **Android:** Explore TFLite acceleration on Android devices in the [Android](app/android.md) section.
### How do I manage and organize my projects in Ultralytics HUB?
Ultralytics HUB allows you to manage and organize your projects efficiently. You can group your models into projects for better organization. To learn more:
- Visit the [Projects](projects.md) page for detailed instructions on creating, editing, and managing projects.
- Use the [Teams](teams.md) feature to collaborate with team members and share resources.
### What integrations are available with Ultralytics HUB?
Ultralytics HUB offers seamless integrations with various platforms to enhance your machine learning workflows. Some key integrations include:
- **Roboflow:** For dataset management and model training. Learn more on the [Integrations](integrations.md) page.
- **Google Colab:** Efficiently train models using Google Colab's cloud-based environment. Detailed steps are available in the [Google Colab](https://docs.ultralytics.com/integrations/google-colab/) section.
- **Weights & Biases:** For enhanced experiment tracking and visualization. Explore the [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/) integration.
