Ultralytics HUB is a new no-code online tool developed by Ultralytics, the creators of the popular YOLOv5 object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLOv5 models without any coding or technical expertise.
Ultralytics HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and select their model configurations. It also 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 and image segmentation tasks. Overall, Ultralytics HUB is an essential tool for anyone looking to use YOLOv5 for their object detection and image segmentation projects.
Get started now and experience the power and simplicity of Ultralytics HUB for yourself. Sign up for a free account and start building, training, and deploying YOLOv5 and YOLOv8 models today.
1. Upload a Dataset
Ultralytics HUB datasets are just like YOLOv5 🚀 datasets, they use the same structure and the same label formats to keep everything simple.
When you upload a dataset to Ultralytics HUB, make sure to place your dataset YAML inside the dataset root directory as in the example shown below, and then zip for upload to https://hub.ultralytics.com/. Your dataset YAML, directory and zip should all share the same name. For example, if your dataset is called 'coco6' as in our example ultralytics/hub/coco6.zip, then you should have a coco6.yaml inside your coco6/ directory, which should zip to create coco6.zip for upload:
The example coco6.zip dataset in this repository can be downloaded and unzipped to see exactly how to structure your custom dataset.
The dataset YAML is the same standard YOLOv5 YAML format. See the YOLOv5 Train Custom Data tutorial for full details.
# 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, ..] path: # dataset root dir (leave empty for HUB) train: images/train # train images (relative to 'path') 8 images val: images/val # val images (relative to 'path') 8 images test: # test images (optional) # Classes names: 0: person 1: bicycle 2: car 3: motorcycle ...
After zipping your dataset, sign in to Ultralytics HUB and click the Datasets tab. Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 models on it!
2. Train a Model
3. Deploy to Real World
Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run models directly on your mobile device by downloading the Ultralytics App!