---
plans: [free, pro, enterprise]
comments: true
description: Learn about data management in Ultralytics Platform including dataset upload, annotation tools, and statistics visualization for YOLO model training.
keywords: Ultralytics Platform, data management, datasets, annotation, YOLO, computer vision, data preparation, labeling
---

# Data Preparation

Data preparation is the foundation of successful [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models. [Ultralytics Platform](https://platform.ultralytics.com) provides comprehensive tools for managing your training data, from upload through annotation to analysis.

<p align="center">
  <br>
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    allowfullscreen>
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  <br>
  <strong>Watch:</strong> Get Started with Ultralytics Platform - Data
</p>

## Overview

The Data section of Ultralytics Platform helps you:

- **Upload** images, videos, and dataset files (ZIP, TAR including `.tar.gz`/`.tgz`, NDJSON)
- **Connect** [Google Cloud Storage](../integrations/google-cloud-storage.md), [Amazon S3](../integrations/amazon-s3.md), or [Azure Blob Storage](../integrations/azure-blob-storage.md) and use your data in place without uploading a copy
- **Keep pixels on premise** with Enterprise [On Premise](../integrations/on-premise.md) CPU/GPU workers
- **Annotate** with manual drawing tools and SAM-powered smart labeling — choose from [SAM 2.1](../../models/sam-2.md) or the new [SAM 3](../../models/sam-3.md)
- **Analyze** your data with statistics and visualizations
- **Export** in [NDJSON format](../../datasets/detect/index.md#ultralytics-ndjson-format) for local training

![Ultralytics Platform Data Overview Sidebar Datasets](https://cdn.ul.run/i/c6f7198b77344ed8d712d8c34ec82459.avif)<!-- screenshot -->

## Workflow

```mermaid
graph LR
    A[Upload]:::start --> B[Annotate]:::proc
    B --> C[Analyze]:::proc
    C --> D[Train]:::out

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

| Stage        | Description                                                                                                     |
| ------------ | --------------------------------------------------------------------------------------------------------------- |
| **Upload**   | Import images, videos, or archives with automatic processing                                                    |
| **Annotate** | Label data with manual tools for all 6 task types, or use SAM annotation for detect, segment, semantic, and OBB |
| **Analyze**  | View class distributions, spatial heatmaps, and dimension statistics                                            |
| **Export**   | Download in [NDJSON format](../../datasets/detect/index.md#ultralytics-ndjson-format) for offline use           |

## Supported Tasks

Ultralytics Platform datasets support 6 of the 7 YOLO task types — [depth](../../tasks/depth.md) datasets are coming soon (depth models and prediction are already supported):

| Task                                             | Description                                                     | Annotation Tool   |
| ------------------------------------------------ | --------------------------------------------------------------- | ----------------- |
| **[Detect](../../datasets/detect/index.md)**     | Object detection with bounding boxes                            | Rectangle tool    |
| **[Segment](../../datasets/segment/index.md)**   | Instance segmentation with pixel masks                          | Polygon tool      |
| **[Semantic](../../datasets/semantic/index.md)** | Semantic segmentation with per-class pixel regions              | Polygon tool      |
| **[Classify](../../datasets/classify/index.md)** | Image-level classification                                      | Class selector    |
| **[Pose](../../datasets/pose/index.md)**         | Keypoint estimation with built-in and custom skeleton templates | Keypoint tool     |
| **[OBB](../../datasets/obb/index.md)**           | Oriented bounding boxes for rotated objects                     | Oriented box tool |

!!! 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 image bytes in the same data region reuse one CAS object 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](../api/index.md#using-platform-datasets)):

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

This allows training on the platform's datasets from any machine with your [API key](../account/api-keys.md) 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](datasets.md#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](../train/models.md) 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 when you have edit access, or in read-only mode when versions already exist.

### Clustering

Explore your dataset as an interactive 2D scatter plot where visually similar images sit close together — useful for surfacing clusters, duplicates, and outliers, and for inspecting how splits or classes are distributed across your data. Lasso a region of the plot to filter the gallery to those images. See [Clustering](datasets.md#clustering) for details.

### 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 Dimensions**: Histogram of image width and height distribution (in pixels)
- **Points per Instance**: Polygon vertex or keypoint count distribution (segment/pose datasets)
- **Annotation Locations**: 2D heatmap of bounding box center positions
- **Image Dimensions 2D**: 2D heatmap of width vs height with aspect ratio guide lines

## Quick Links

- [**Datasets**](datasets.md): Upload, manage, and export your training data
- [**Annotation**](annotation.md): Label data with manual and AI-assisted tools
- [**Cloud Training**](../train/cloud-training.md): Train models on your annotated datasets
- [**Dataset URI**](datasets.md#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, 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](../../datasets/detect/index.md#ultralytics-yolo-format), 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](../../datasets/detect/index.md#ultralytics-ndjson-format) to transfer its metadata, splits, annotations, and signed image URLs.
