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116 lines
6.8 KiB
116 lines
6.8 KiB
# Ultralytics HUB
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
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<br>
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<div align="center">
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<a href="https://github.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
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<br>
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<br>
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<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
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<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
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<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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</div>
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[Ultralytics HUB](https://hub.ultralytics.com) is a new no-code online tool developed
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by [Ultralytics](https://ultralytics.com), the creators of the popular [YOLOv5](https://github.com/ultralytics/yolov5)
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object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLO models
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without any coding or technical expertise.
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Ultralytics HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to
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easily upload their data and select their model configurations. It also offers a range of pre-trained models and
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templates to choose from, making it easy for users to get started with training their own models. Once a model is
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trained, it can be easily deployed and used for real-time object detection and image segmentation tasks. Overall,
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Ultralytics HUB is an essential tool for anyone looking to use YOLO for their object detection and image segmentation
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projects.
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**[Get started now](https://hub.ultralytics.com)** and experience the power and simplicity of Ultralytics HUB for
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yourself. Sign up for a free account and start building, training, and deploying YOLOv5 and YOLOv8 models today.
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## 1. Upload a Dataset
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Ultralytics HUB datasets are just like YOLOv5 🚀 datasets, they use the same structure and the same label formats to keep
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everything simple.
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When you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML inside the dataset root directory**
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as in the example shown below, and then zip for upload to https://hub.ultralytics.com/. Your **dataset YAML, directory
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and zip** should all share the same name. For example, if your dataset is called 'coco6' as in our
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example [ultralytics/hub/coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip), then you should have a
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coco6.yaml inside your coco6/ directory, which should zip to create coco6.zip for upload:
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```bash
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zip -r coco6.zip coco6
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```
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The example [coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip) dataset in this repository can be
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downloaded and unzipped to see exactly how to structure your custom dataset.
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<p align="center">
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<img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" />
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</p>
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The dataset YAML is the same standard YOLOv5 YAML format. See
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the [YOLOv5 Train Custom Data tutorial](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) for full details.
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```yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: # dataset root dir (leave empty for HUB)
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train: images/train # train images (relative to 'path') 8 images
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val: images/val # val images (relative to 'path') 8 images
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test: # test images (optional)
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# Classes
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names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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...
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```
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After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab.
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Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 models on it!
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<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/216763338-9a8812c8-a4e5-4362-8102-40dad7818396.png">
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## 2. Train a Model
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Connect to the Ultralytics HUB notebook and use your model API key to begin training!
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<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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## 3. Deploy to Real World
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Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run
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models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or
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[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading
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the [Ultralytics App](https://ultralytics.com/app_install)!
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## ❓ Issues
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If you are a new [Ultralytics HUB](https://bit.ly/ultralytics_hub) user and have questions or comments, you are in the
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right place! Please raise a [New Issue](https://github.com/ultralytics/hub/issues/new/choose) and let us know what we
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can do to make your life better 😃!
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