Datasets

Ultralytics Platform datasets provide a streamlined solution for managing your training data. Once uploaded, datasets can be immediately used for model training, with automatic processing and statistics generation.

Upload Dataset

Ultralytics Platform accepts multiple upload formats for flexibility.

Supported Formats

Preparing Your Dataset

The Platform supports two annotation formats plus raw uploads: Ultralytics YOLO, COCO, and raw (unannotated images):

my-dataset/
├── images/
│   ├── train/
│   │   ├── img001.jpg
│   │   └── img002.jpg
│   └── val/
│       ├── img003.jpg
│       └── img004.jpg
├── labels/
│   ├── train/
│   │   ├── img001.txt
│   │   └── img002.txt
│   └── val/
│       ├── img003.txt
│       └── img004.txt
└── data.yaml

# data.yaml
path: .
train: images/train
val: images/val

names:
    0: person
    1: car
    2: dog

my-coco-dataset/
├── train/
│   ├── _annotations.coco.json
│   ├── img001.jpg
│   └── img002.jpg
└── val/
    ├── _annotations.coco.json
    ├── img003.jpg
    └── img004.jpg

{
    "images": [{ "id": 1, "file_name": "img001.jpg", "width": 640, "height": 480 }],
    "annotations": [{ "id": 1, "image_id": 1, "category_id": 0, "bbox": [100, 50, 200, 300] }],
    "categories": [{ "id": 0, "name": "person" }]
}

For task-specific format details, see supported tasks and the Datasets Overview.

Upload Process

1. Navigate to Datasets in the sidebar
2. Click New Dataset or drag files into the upload zone
3. Select the task type (see supported tasks)
4. Add a name and optional description
5. Set visibility (public or private) and optional license (see available licenses)
6. Click Create

After upload, the platform processes your data through a multi-stage pipeline:

graph LR
    A[Upload] --> B[Validate]
    B --> C[Normalize]
    C --> D[Thumbnail]
    D --> E[Parse Labels]
    E --> F[Statistics]

    style A fill:#4CAF50,color:#fff
    style B fill:#2196F3,color:#fff
    style C fill:#2196F3,color:#fff
    style D fill:#2196F3,color:#fff
    style E fill:#2196F3,color:#fff
    style F fill:#9C27B0,color:#fff

1. Validation: Format and size checks
2. Normalization: Large images resized (max 4096px, min dimension 28px)
3. Thumbnails: 256px WebP previews generated
4. Label Parsing: YOLO and COCO format labels extracted
5. Statistics: Class distributions and image dimensions computed

from ultralytics.hub import check_dataset

check_dataset("path/to/dataset.zip", task="detect")

Browse Images

View your dataset images in multiple layouts:

Sorting and Filtering

Images can be sorted and filtered for efficient browsing:

Fullscreen Viewer

Click any image to open the fullscreen viewer with:

• Navigation: Arrow keys or thumbnail previews to browse
• Metadata: Filename, dimensions, split badge, annotation count
• Annotations: Toggle annotation overlay visibility
• Class Breakdown: Per-class label counts with color indicators
• Edit: Enter annotation mode to add or modify labels
• Download: Download the original image file
• Delete: Delete the image from the dataset
• Zoom: Cmd/Ctrl+Scroll to zoom in/out
• Pixel view: Toggle pixelated rendering for close inspection

Filter by Split

Filter images by their dataset split:

Dataset Tabs

Each dataset page has five tabs accessible from the tab bar:

Images Tab

The default view showing the image gallery with annotation overlays. Supports grid, compact, and table view modes. Drag and drop files here to add more images.

Classes Tab

Manage annotation classes for your dataset:

• Class histogram: Bar chart showing annotation count per class with linear/log scale toggle
• Class table: Sortable, searchable table with class name, label count, and image count
• Edit class names: Click any class name to rename it inline
• Edit class colors: Click a color swatch to change the class color
• Add new class: Use the input at the bottom to add classes

Charts Tab

Automatic statistics computed from your dataset:

Models Tab

View all models trained on this dataset in a searchable table:

Errors Tab

Images that failed processing are listed here with:

• Error banner: Total count of failed images and guidance
• Error table: Filename, user-friendly error description, fix hints, and preview thumbnail
• Common errors include corrupted files, unsupported formats, images too small (min 28px), and unsupported color modes

Export Dataset

Export your dataset in NDJSON format for offline use:

1. Click the download icon in the dataset header
2. The NDJSON file downloads automatically

The NDJSON format stores one JSON object per line. The first line contains dataset metadata, followed by one line per image:

{"type": "dataset", "task": "detect", "name": "my-dataset", "description": "...", "url": "https://platform.ultralytics.com/...", "class_names": {"0": "person", "1": "car"}, "version": 1, "created_at": "2026-01-15T10:00:00Z", "updated_at": "2026-02-20T14:30:00Z"}
{"type": "image", "file": "img001.jpg", "url": "https://...", "width": 640, "height": 480, "split": "train", "annotations": {"boxes": [[0, 0.5, 0.5, 0.2, 0.3]]}}
{"type": "image", "file": "img002.jpg", "url": "https://...", "width": 1280, "height": 720, "split": "val"}

See the Ultralytics NDJSON format documentation for full specification.

Bulk Operations

Manage images in bulk using the table view's context menu:

Move to Split

Reassign selected images to a different split within the same dataset:

1. Switch to Table view
2. Select images using checkboxes
3. Right-click to open the context menu
4. Choose Move to split > Train, Validation, or Test

You can also drag and drop images onto the split filter tabs in grid view.

Bulk Delete

Delete multiple images at once:

1. Select images in the table view
2. Right-click and choose Delete
3. Confirm deletion

Dataset URI

Reference Platform datasets using the ul:// URI format (see Using Platform Datasets):

ul://username/datasets/dataset-slug

Use this URI to train models from anywhere:

export ULTRALYTICS_API_KEY="your_api_key"
yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset epochs=100

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
model.train(data="ul://username/datasets/my-dataset", epochs=100)

Available Licenses

The Platform supports the following licenses for datasets:

Visibility Settings

Control who can see your dataset:

Visibility is set when creating a dataset in the New Dataset dialog using a toggle switch. Public datasets are visible on the Explore page.

Edit Dataset

Dataset metadata is edited inline directly on the dataset page — no dialog needed:

• Name: Click the dataset name to edit it. Changes auto-save on blur or Enter.
• Description: Click the description (or "Add a description..." placeholder) to edit. Changes auto-save.
• Task type: Click the task badge to select a different task type.
• License: Click the license selector to change the dataset license.

Clone Dataset

When viewing a public dataset you do not own, click Clone Dataset to create a copy in your workspace. The clone includes all images, annotations, and class definitions. If the original dataset has a copyleft license, the clone inherits it and the license selector is locked.

Star and Share

• Star: Click the star button to bookmark a dataset. The star count is visible to all users.
• Share: For public datasets, click the share button to copy a link or share to social platforms.

Delete Dataset

Delete a dataset you no longer need:

1. Open dataset actions menu
2. Click Delete
3. Confirm in the dialog: "This will move [name] to trash. You can restore it within 30 days."

Train on Dataset

Start training directly from your dataset:

1. Click New Model on the dataset page
2. Select a project or create new
3. Configure training parameters
4. Start training

graph LR
    A[Dataset] --> B[New Model]
    B --> C[Select Project]
    C --> D[Configure]
    D --> E[Start Training]

    style A fill:#2196F3,color:#fff
    style E fill:#4CAF50,color:#fff

See Cloud Training for details.

FAQ

What happens to my data after upload?

Your data is processed and stored in your selected region (US, EU, or AP). Images are:

1. Validated for format and size
2. Rejected if minimum dimension is below 28px
3. Normalized if larger than 4096px (preserving aspect ratio; encoded for optimized storage)
4. Stored using Content-Addressable Storage (CAS) with XXH3-128 hashing
5. Thumbnails generated at 256px WebP for fast browsing

How does storage work?

Ultralytics Platform uses Content-Addressable Storage (CAS) for efficient storage:

• Deduplication: Identical images uploaded by different users are stored only once
• Integrity: XXH3-128 hashing ensures data integrity
• Efficiency: Reduces storage costs and speeds up processing
• Regional: Data stays in your selected region (US, EU, or AP)

Can I add images to an existing dataset?

Yes, drag and drop files onto the dataset page or use the upload button to add additional images. New statistics will be computed automatically.

How do I move images between splits?

Use the bulk move-to-split feature:

1. Select images in the table view
2. Right-click and choose Move to split
3. Select the target split (Train, Validation, or Test)

What label formats are supported?

Ultralytics Platform supports two annotation formats for upload:

📅 Created 1 month ago ✏️ Updated 5 days ago

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