Datasets#

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, and at least one labeled image. The dataset header shows a Ready badge when all three conditions are met, and a Not Ready badge otherwise — click the badge to see exactly which condition is missing.

Upload Dataset#

Ultralytics Platform accepts multiple upload formats for flexibility.

Supported Formats#

Video Codec Support#

The file extension alone isn't enough: a video can still fail if its codec cannot be decoded during processing.

ffmpeg -i input.mov \
  -c:v libx264 -pix_fmt yuv420p \
  -c:a aac -movflags +faststart \
  output.mp4

Preparing Your Dataset#

The Platform supports Ultralytics YOLO, COCO, depth datasets, 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

If labels reference class IDs but no class names are supplied, Platform generates dense placeholder names (class0, class1, …) that you can rename later in the Classes tab.

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

Upload Process#

To create a dataset:

1. Navigate to Annotate in the sidebar
2. Click New Dataset
3. Pick a data source tab (see Data Sources below)
4. Add a name — the URL slug is derived automatically and can be edited — plus an optional description
5. Select the task type (see supported tasks), an optional license (see available licenses), and visibility (public or private)
6. Click Create & Upload for local files, Create & Import for a URL or connected source, or Create Dataset to start empty

To add files to an existing dataset, open its dataset page and either drag the files onto the gallery or click the upload icon in the page header. The upload icon opens your browser's native file picker directly because the dataset task is already defined.

Data Sources#

The New Dataset dialog offers four sources:

Before the Upload Starts#

Platform validates your files in the browser before uploading anything, so common problems surface immediately rather than after a long transfer. Archives are checked for corruption, emptiness, password protection, and YAML syntax errors, and oversized files are listed by name.

Two dialogs may then appear:

After upload, the platform processes your data automatically:

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

    classDef start fill:#4CAF50,color:#fff
    classDef proc fill:#2196F3,color:#fff
    classDef out fill:#9C27B0,color:#fff

1. Validation: Format and size checks
2. Normalization: Large images resized (max 4096px, min dimension 28px), grayscale expanded to RGB, transparency flattened onto white, and EXIF orientation applied
3. Thumbnails: 256px WebP previews generated
4. Label Parsing: YOLO, COCO, and NDJSON 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")

Browse 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.

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
• Image information: Review Platform-generated properties, custom metadata, and embedded file metadata such as EXIF
• Custom metadata: Owners and editors can add or replace a JSON object, including nested values up to 500,000 serialized characters and top-level keys up to 128 characters
• Annotations: Toggle annotation overlay visibility
• Class Breakdown: Per-class label counts with color indicators
• Annotate: When you have edit access, annotation controls are active immediately when the fullscreen viewer opens on desktop
• 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
• Depth curtain: On depth datasets, a draggable divider wipes between the RGB image and its colorized depth map

Filter by Split#

Filter images by their dataset split:

Clustering#

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.

Running 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 in two stages, Computing embeddings and Clustering, 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.

Visualization#

Once analysis completes, the panel shows a 2D scatter of all analyzed images with a legend and a point counter. Gallery filters (split, class, labeled/unlabeled) dim out-of-filter points so you can focus on the subset you care about — the counter then reads visible / total points.

Color 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:

Lasso 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.

Pan and Zoom#

Navigate large scatters directly from your mouse and keyboard, or with the zoom buttons at the bottom-left of the plot:

Re-analyzing#

If your dataset changes after analysis — new images arrive, or the analyzed count no longer matches the dataset — 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.

Dataset Tabs#

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

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#

This tab appears when the dataset has images and its task has classes.

Manage annotation classes for your dataset:

• Class histogram: Bar chart showing annotation count per class, sorted by frequency, with a linear/log scale toggle
• Class table: Sortable, searchable table with index, name, annotation 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
• Merge classes: Select two or more rows and click Merge into one
• Delete classes: Select one or more rows and click Delete

Merge Classes#

Merging consolidates duplicate or overlapping labels — for example folding car, automobile, and vehicle into one class:

1. Select two or more classes using the row checkboxes
2. Click Merge into one in the table header, or right-click the selection
3. Choose which of the selected classes is the target the others merge into
4. Confirm

Every annotation belonging to the source classes is reassigned to the target class, and the source classes are removed. No annotations are deleted, so the dataset's total annotation count is unchanged.

Delete Classes#

1. Select one or more classes using the row checkboxes
2. Click Delete in the table header, or right-click the selection
3. Confirm

Deleting a class removes the class and all of its annotations. For classification datasets the labels are removed but the images remain, becoming unannotated.

Charts Tab#

This tab appears when the dataset has images.

Automatic statistics computed from your dataset:

Charts appear in this order, and each one is omitted when the dataset has no data for it — a raw image dataset shows no annotation charts, and Points per Instance only appears for segment and pose data:

Models Tab#

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

Errors 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

Versions 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

Each version is numbered sequentially (v1, v2, v3...) and is immutable — versions cannot be edited or removed, only their descriptions can be changed. Use the row actions to download or restore any version at any time.

Export 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", "metadata": {"location": {"site": "factory-1"}, "reviewed": true}, "annotations": {"boxes": [[0, 0.5, 0.5, 0.2, 0.3]]}}
{"type": "image", "file": "img002.jpg", "url": "https://...", "width": 1280, "height": 720, "split": "val"}

The optional image-level metadata object is preserved when an NDJSON file is imported into Platform. You can inspect or edit it from the image's fullscreen information panel. For programmatic archive uploads, the Ingest Dataset Data API accepts the equivalent imageMetadata path map.

Pose datasets also carry a kpt_shape field in the dataset header line, inferred from the annotations when it is not already set.

Depth exports declare "task": "depth" and "depth_scale": 1000 in the dataset line. Each image line includes a depth.url for its paired plain uint16 PNG. Ultralytics downloads image and depth URLs concurrently and writes a training YAML with the same scale, so the NDJSON file can be passed directly to model.train(data=...).

See the Ultralytics NDJSON format documentation for full specification.

Image Operations#

Quick Actions#

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

Bulk 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. If moving an image would collide with an identical image already in the target split, Platform asks whether to skip, keep both, or replace.

Split 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.

Bulk Delete#

Delete multiple images at once:

1. Select images in the table view
2. Right-click and choose Delete, or press Cmd/Ctrl+Delete
3. Confirm deletion

Dataset URI#

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

ul://username/datasets/dataset-slug

You can also paste a dataset or model web URL directly (e.g. https://platform.ultralytics.com/username/datasets/dataset-slug); it is automatically rewritten to the ul:// URI. Passing a list of datasets fine-tunes one base model across each in series, for example model.train(data=["ul://username/datasets/a", "ul://username/datasets/b"]).

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

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. Names are limited to 100 characters.
• Description: Click the description (or "Add a description..." placeholder) to edit. Changes auto-save. Descriptions are limited to 1,000 characters.
• Task type: Click the task badge to select a different task type.
• License: Click the license selector to change the dataset license.
• Icon: Click the dataset icon to upload your own image, promote one of the dataset's own images to the icon, or pick a letter and color.

Custom Metadata#

Open More actions and select Information to review two sections:

• Ultralytics Metadata: Read-only Platform details such as the dataset ID, owner, task, image and annotation counts, storage region, and timestamps
• Custom Metadata: Your own JSON object for provenance, capture conditions, customer IDs, governance, or other contextual data

Workspace viewers can inspect metadata, while members with edit access can replace the custom metadata object. The serialized metadata object is limited to 500,000 characters, and each top-level key is limited to 128 characters. Save an empty object ({}) to clear custom metadata.

Clone Dataset#

When viewing a public dataset you do not own, click Clone Dataset to open the clone dialog. Review the destination workspace, name, visibility, and license, then confirm the clone. The copy includes all images, annotations, and class definitions. Public source datasets stay public by default in workspaces whose default visibility is public; Enterprise workspace clones default to private. If the original dataset has a copyleft license, the clone inherits it and the license selector is locked.

The destination slug is auto-renamed if it is already taken, and cloning requires enough remaining storage quota to hold the copy.

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, grab an embed snippet, or share to social platforms.

Delete Dataset#

Delete a dataset you no longer need:

1. Open More actions (the … button) in the dataset header
2. Choose Delete Dataset
3. Confirm in the dialog: "This will move [name] to trash. You can restore it within 30 days."

The same menu holds Information, which opens the metadata dialog described in Custom Metadata, and Refresh, which re-reads the dataset from the server.

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]:::start --> B[New Model]:::proc
    B --> C[Select Project]:::proc
    C --> D[Configure]:::proc
    D --> E[Start Training]:::out

    classDef start fill:#4CAF50,color:#fff
    classDef proc fill:#2196F3,color:#fff
    classDef out fill:#9C27B0,color:#fff

See Cloud Training for details.

FAQ#