---
comments: true
description: Upload custom datasets to Ultralytics HUB for YOLOv5 and YOLOv8 models. Follow YAML structure, zip and upload. Scan & train new models.
---
# HUB Datasets
## 1. Upload a 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.
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](https://hub.ultralytics.com/). Your **dataset YAML, directory
and zip** should all share the same name. For example, if your dataset is called 'coco8' as in our
example [ultralytics/hub/example_datasets/coco8.zip](https://github.com/ultralytics/hub/blob/master/example_datasets/coco8.zip), then you should have a `coco8.yaml` inside your `coco8/` directory, which should zip to create `coco8.zip` for upload:
```bash
zip -r coco8.zip coco8
```
The [example_datasets/coco8.zip](https://github.com/ultralytics/hub/blob/master/example_datasets/coco8.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 and YOLOv8 YAML format. See
the [YOLOv5 and YOLOv8 Train Custom Data tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) for full details.
```yaml
# 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](https://bit.ly/ultralytics_hub) and click the Datasets tab.
Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 or YOLOv8 models on it!
