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.
Overview
The Data section of Ultralytics Platform helps you:
- Upload images, videos, and archives (ZIP, TAR, GZ)
- Annotate with manual drawing tools and SAM-powered smart labeling
- 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 bounding boxes, polygons, keypoints, or classifications |
| 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 (17-point COCO format) | Keypoint tool |
| OBB | Oriented bounding boxes for rotated objects | Oriented box tool |
| Classify | Image-level classification | Class selector |
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 settings, 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.
Use Platform Data from Python
from ultralytics import YOLO
model = YOLO("yolo26n.pt")
model.train(data="ul://username/datasets/my-dataset", epochs=100)
Dataset Tabs
Every dataset page provides five tabs:
| 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 |
| Errors | Images that failed processing with error details and fix guidance |
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
Quick Links
- Datasets: Upload and manage your training data
- Annotation: Label data with manual and AI-assisted tools
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)
Archives: ZIP, TAR, TAR.GZ, TGZ, GZ (max 10GB) containing images with optional YOLO-format labels
What is the maximum dataset size?
Storage limits depend on your plan:
| Plan | Storage Limit |
|---|---|
| Free | 100 GB |
| Pro | 500 GB |
| Enterprise | Custom |
Individual file limits: Images 50MB, Videos 1GB, Archives 10GB
Can I use my Platform datasets for local training?
Yes! Use the dataset URI format to train locally:
export ULTRALYTICS_API_KEY="your_key"
yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset epochs=100
import os
os.environ["ULTRALYTICS_API_KEY"] = "your_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.
