ultralytics/docs/en/platform/data/index.md

comments	description	keywords
true	Learn about data management in Ultralytics Platform including dataset upload, annotation tools, and statistics visualization for YOLO model training.	Ultralytics Platform, data management, datasets, annotation, YOLO, computer vision, data preparation, labeling

Data Preparation

Data preparation is the foundation of successful computer vision models. Ultralytics Platform provides comprehensive tools for managing your training data, from upload through annotation to analysis.

Watch: Get Started with Ultralytics Platform - Data

Overview

The Data section of Ultralytics Platform helps you:

• Upload images, videos, and dataset files (ZIP, TAR including .tar.gz/.tgz, NDJSON)
• Annotate with manual drawing tools and SAM-powered smart labeling — choose from SAM 2.1 or the new SAM 3
• Analyze your data with statistics and visualizations
• Export in NDJSON format for local training

Workflow

graph LR
    A[Upload] --> B[Annotate]
    B --> C[Analyze]
    C --> D[Train]

    style A fill:#4CAF50,color:#fff
    style B fill:#2196F3,color:#fff
    style C fill:#FF9800,color:#fff
    style D fill:#9C27B0,color:#fff

Stage	Description
Upload	Import images, videos, or archives with automatic processing
Annotate	Label data with manual tools for all 5 task types, or use SAM annotation for detect, segment, and OBB
Analyze	View class distributions, spatial heatmaps, and dimension statistics
Export	Download in NDJSON format for offline use

Supported Tasks

Ultralytics Platform supports all 5 YOLO task types:

Task	Description	Annotation Tool
Detect	Object detection with bounding boxes	Rectangle tool
Segment	Instance segmentation with pixel masks	Polygon tool
Pose	Keypoint estimation with built-in and custom skeleton templates	Keypoint tool
OBB	Oriented bounding boxes for rotated objects	Oriented box tool
Classify	Image-level classification	Class selector

!!! info "Task Type Selection"

The task type is set when creating a dataset and determines which annotation tools are available. You can change it later from the dataset header task selector, but incompatible annotations won't be displayed after switching.

Key Features

Smart Storage

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

• Deduplication: Identical images stored only once via XXH3-128 hashing
• Integrity: Hash-based addressing ensures data integrity
• Efficiency: Optimized storage and fast processing

Dataset URIs

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

yolo train data=ul://username/datasets/my-dataset

This allows training on the platform's datasets from any machine with your API key configured.

!!! example "Use Platform Data from Python"

```python
from ultralytics import YOLO

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

Dataset Versioning

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

Dataset Tabs

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

Tab	Description
Images	Browse images in grid, compact, or table view with annotation overlays
Classes	View and edit class names, colors, and label counts per class
Charts	Automatic statistics: split distribution, class counts, heatmaps
Models	Models trained on this dataset with metrics and status
Versions	Create and download immutable NDJSON snapshots for reproducible training
Errors	Images that failed processing with error details and fix guidance

Classes and Charts appear when the dataset has images. Errors appears only when processing failures exist. Versions appears for owners, or for non-owners when versions already exist.

Statistics and Visualization

The Charts tab provides automatic analysis including:

• Split Distribution: Donut chart of train/val/test image counts
• Top Classes: Donut chart of most frequent annotation classes
• Image Widths: Histogram of image width distribution
• Image Heights: Histogram of image height distribution
• Points per Instance: Polygon vertex or keypoint count distribution (segment/pose datasets)
• Annotation Locations: 2D heatmap of bounding box center positions
• Image Dimensions: 2D heatmap of width vs height with aspect ratio guide lines

Health and Clustering

Explore your dataset as an interactive 2D scatter plot, detect blurry, overexposed, low-contrast, and near-duplicate images. Brush a region of the plot to filter the gallery by cluster. See Health & Clustering for details.

Quick Links

• Datasets: Upload, manage, and export your training data
• Annotation: Label data with manual and AI-assisted tools
• Cloud Training: Train models on your annotated datasets
• Dataset URI: Use ul:// URIs to train from anywhere

FAQ

What file formats are supported for upload?

Ultralytics Platform supports:

Images: JPEG, PNG, WebP, BMP, TIFF, HEIC, AVIF, JP2, DNG, MPO (max 50MB each)

Videos: MP4, WebM, MOV, AVI, MKV, M4V (max 1GB, frames extracted at 1 FPS, max 100 frames)

Dataset files: ZIP or TAR archives including .tar.gz and .tgz (max 10GB on Free, 20GB on Pro, 50GB on Enterprise) containing images with optional YOLO-format labels, plus NDJSON exports

What is the maximum dataset size?

Storage limits depend on your plan:

Plan	Storage Limit
Free	100 GB
Pro	500 GB
Enterprise	Unlimited

Individual file limits: Images 50MB, Videos 1GB, datasets 10GB on Free / 20GB on Pro / 50GB on Enterprise

Can I use my Platform datasets for local training?

Yes! Use the dataset URI format to train locally:

=== "CLI"

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

=== "Python"

```python
import os

os.environ["ULTRALYTICS_API_KEY"] = "YOUR_API_KEY"

from ultralytics import YOLO

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

Or export your dataset in NDJSON format for fully offline training.