Link to this sectionDatasets#

Ultralytics Platform datasets provide a streamlined solution for managing your training data. After upload, the platform processes images, labels, and statistics automatically. A dataset is ready to train once processing has completed and it has at least one image in the train split, at least one image in either the val or test split, at least one labeled image, and a total of at least two images.

Link to this sectionUpload Dataset#

Ultralytics Platform accepts multiple upload formats for flexibility.

Link to this sectionSupported Formats#

Link to this sectionPreparing Your Dataset#

The Platform supports Ultralytics YOLO, COCO, Ultralytics NDJSON, and raw (unannotated) uploads:

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

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

Link to this sectionUpload 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 & Upload (or Create Dataset if creating an empty dataset)

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.data.utils import check_det_dataset

check_det_dataset("path/to/data.yaml")

Link to this sectionBrowse Images#

View your dataset images in multiple layouts.

Open the Clustering panel from the gallery toolbar to explore your dataset as an interactive 2D scatter plot.

Link to this sectionSorting and Filtering#

Images can be sorted and filtered for efficient browsing:

Link to this sectionFullscreen 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, Cmd/Ctrl++, or Cmd/Ctrl+= to zoom in, and Cmd/Ctrl+- to zoom out
• Reset view: Cmd/Ctrl + 0 or the reset button to fit the image to the viewer
• Pan: Hold Space and drag to pan the canvas when zoomed
• Pixel view: Toggle pixelated rendering for close inspection

Link to this sectionFilter by Split#

Filter images by their dataset split:

Link to this sectionClustering#

The Clustering panel projects your dataset into an interactive 2D scatter plot where visually similar images sit close together. Use it to surface clusters, spot duplicates and outliers, and inspect how splits or classes are distributed across your data — without leaving the gallery. Open it from the scatter-chart icon in the gallery toolbar on any dataset page.

Link to this sectionRunning Analysis#

Start an analysis:

1. Open a dataset and click the scatter-chart icon in the gallery toolbar
2. Click Analyze Dataset
3. Wait for the progress bar to finish — results appear in the same panel

Analysis runs in the background and can take a few minutes depending on the size of your dataset. You can close the panel or leave the page and come back later.

Link to this sectionVisualization#

Once analysis completes, the panel shows a 2D scatter of all analyzed images. Gallery filters (split, class, labeled/unlabeled) dim out-of-filter points so you can focus on the subset you care about.

Link to this sectionColor By#

Change how data points are shaded with the Color by dropdown in the panel toolbar. Switch view modes at any time — the plot re-colors instantly so you can see how splits, classes, or image properties are distributed across your clusters:

Link to this sectionLasso Selection#

Draw a free-form selection around a region to highlight points on the plot. The gallery filters down to the matching images, so you can inspect, relabel, move, or delete them using the usual image operations.

Link to this sectionPan and Zoom#

Navigate large scatters directly from your mouse and keyboard:

Link to this sectionRe-analyzing#

If your dataset changes after analysis, a Re-analyze button appears at the top of the panel for owners and editors.

Click Re-analyze to recompute embeddings and the 2D projection from scratch.

Link to this sectionDataset Tabs#

Each dataset page can show up to six tabs, depending on the dataset state and your permissions:

Link to this sectionImages 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.

Link to this sectionClasses Tab#

This tab appears when the dataset has images.

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

Link to this sectionCharts Tab#

This tab appears when the dataset has images.

Automatic statistics computed from your dataset:

Link to this sectionModels Tab#

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

Link to this sectionErrors Tab#

This tab appears only when one or more files fail processing.

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

Link to this sectionVersions Tab#

Create immutable NDJSON snapshots of your dataset for reproducible training. Each version captures image counts, class counts, annotation counts, and file size at the time of creation.

To create a version:

1. Open the Versions tab
2. Optionally enter a description (e.g., "Added 500 training images" or "Fixed mislabeled classes")
3. Click + New Version
4. The new version appears in the table
5. Download the version separately from the table when needed

Each version is numbered sequentially (v1, v2, v3...) and stored permanently. You can download any previous version at any time from the versions table.

Link to this sectionExport Dataset#

Export your dataset for offline use with an NDJSON download from the dataset header or the Versions tab.

To export:

1. Click the Download button (download icon) in the dataset header
2. Download the current NDJSON snapshot directly
3. Use the Versions tab when you want an immutable numbered snapshot you can re-download later

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": "...", "bytes": 12345678, "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.

Link to this sectionImage Operations#

Link to this sectionQuick Actions#

Right-click any image in Grid or Compact view to access quick actions:

Link to this sectionBulk 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.

Link to this sectionSplit Redistribution#

Redistribute all images across train, validation, and test splits using custom ratios:

1. Click the split bar in the dataset toolbar to open the Redistribute Splits dialog
2. Adjust split percentages using any of the methods below
3. Review the live image count preview to confirm the distribution
4. Click Apply to randomly reassign all images according to your percentages

The dialog provides three ways to set your target split ratios:

A live preview shows exactly how many images will land in each split before you apply.

Link to this sectionBulk Delete#

Delete multiple images at once:

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

Link to this sectionDataset URI#

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

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

Link to this sectionAvailable Licenses#

The Platform supports the following licenses for datasets:

Link to this sectionVisibility 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.

Link to this sectionEdit 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.

Link to this sectionClone 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.

Link to this sectionStar 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.

Link to this sectionDelete 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."

Link to this sectionTrain 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.

Link to this sectionFAQ#

Link to this sectionWhat 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

Link to this sectionHow 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)

Link to this sectionCan 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.

Link to this sectionHow 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)

Link to this sectionWhat label formats are supported?#

Ultralytics Platform supports YOLO labels, COCO JSON, Ultralytics NDJSON, and raw image uploads:

Link to this sectionCan I annotate the same dataset for multiple task types?#

Yes. Each image stores annotations for all 6 task types (detect, segment, semantic, pose, OBB, classify) together. You can switch the dataset's active task type at any time without losing existing annotations. Only annotations matching the active task type are shown in the editor and included in exports and training — annotations for other tasks are preserved and reappear when you switch back.