Ultralytics Platform Quickstart

Ultralytics Platform is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It offers a range of pretrained models to choose from, making it easy for users to get started. Once a model is trained, it can be tested directly in the browser and deployed to production with a single click.

journey
    title Your First Model in 5 Minutes
    section Sign Up
      Create account: 5: User
      Select region: 5: User
    section Prepare Data
      Upload dataset: 5: User
      Review images: 4: User
    section Train
      Configure training: 5: User
      Monitor progress: 3: Platform
    section Deploy
      Test model: 5: User
      Deploy endpoint: 5: User

Get Started

Ultralytics Platform offers a variety of easy signup options. You can register and log in using your Google or GitHub accounts, or with your email address.

Region Selection

During onboarding, you'll be asked to select your data region. The Platform automatically measures latency to each region and recommends the closest one. This is an important choice as it determines where your data, models, and deployments will be stored.

Free Credits

Every new account receives free credits for cloud GPU training:

Complete Your Profile

Before selecting your region, you'll complete your profile with a display name, username, optional company, and primary use case. The onboarding flow has three steps: Profile, Data Region, and Complete.

Home Dashboard

After signing in, you will be directed to the Home page of Ultralytics Platform, which provides a welcome card with workspace stats, quick access to datasets, projects, and storage, and a recent activity feed.

Sidebar Navigation

The sidebar provides access to all Platform sections:

Welcome Card

The welcome card shows your profile, plan badge, and workspace statistics at a glance:

Quick Actions

Below the welcome card, the dashboard shows three cards:

• Datasets: Create a new dataset or drop images, videos, or ZIP files to upload. Shows your recent datasets.
• Projects: Create a new project or drop .pt model files to upload. Shows your recent projects.
• Storage: Overview of your storage usage (datasets, models, exports) with plan limits.

A Recent Activity table at the bottom shows your latest datasets, models, and training runs.

Upload Your First Dataset

Navigate to Annotate in the sidebar and click New Dataset to add your training data. You can also drag and drop files directly onto the Datasets card on the Home dashboard.

Ultralytics Platform supports multiple upload formats (full details in Datasets):

graph LR
    A[Drop Files] --> B[Auto-Package ZIP]
    B --> C[Upload to Storage]
    C --> D[Backend Worker]
    D --> E[Resize & Thumbnail]
    E --> F[Parse Labels]
    F --> G[Compute Statistics]
    G --> H[Dataset Ready]

After upload, the platform automatically processes your data:

1. Images larger than 4096px are resized (preserving aspect ratio)
2. 256px thumbnails are generated for fast browsing
3. Labels are parsed and validated (YOLO .txt format)
4. Statistics are computed (class distribution, heatmaps, dimensions)

my-dataset.zip
├── data.yaml          # Class names and splits
├── train/
│   ├── images/
│   │   ├── img001.jpg
│   │   └── img002.jpg
│   └── labels/
│       ├── img001.txt
│       └── img002.txt
└── val/
    ├── images/
    └── labels/

Read more about datasets and supported formats for detect, segment, pose, OBB, and classify.

Create Your First Project

Projects help you organize related models and experiments. Navigate to Projects and click "Create Project".

Enter a name and optional description for your project. Projects contain:

• Models: Trained checkpoints
• Activity Log: History of changes

Read more about projects.

Train Your First Model

From your project, click Train Model to start cloud training.

Training Configuration

1. Select Dataset: Choose from your uploaded datasets (only datasets with a train split are shown)
2. Choose Model: Select a base model — official Ultralytics models or your own trained models
3. Set Epochs: Number of training iterations (default: 100)
4. Select GPU: Choose compute resources based on your budget and model size

Monitor Training

Once training starts, you can monitor progress in real-time through three subtabs:

Metrics are streamed in real-time via SSE (Server-Sent Events). After training completes, validation plots are generated including confusion matrix, PR curves, and F1 curves.

Read more about cloud training.

Test Your Model

After training completes, test your model directly in the browser:

1. Navigate to your model's Predict tab
2. Upload an image, drag and drop, or use example images (auto-inference on drop)
3. View inference results with bounding boxes rendered on canvas

Adjust inference parameters:

The Predict tab provides ready-to-use code examples with your actual API key pre-filled:

import requests

url = "https://platform.ultralytics.com/api/models/{model_id}/predict"
headers = {"Authorization": "Bearer your_api_key"}

with open("image.jpg", "rb") as f:
    response = requests.post(url, headers=headers, files={"file": f})

print(response.json())

curl -X POST "https://platform.ultralytics.com/api/models/{model_id}/predict" \
  -H "Authorization: Bearer your_api_key" \
  -F "file=@image.jpg"

Read more about inference.

Deploy to Production

Deploy your model to a dedicated endpoint for production use:

1. Navigate to your model's Deploy tab
2. Select a region from the interactive world map (43 available regions)
3. The map shows real-time latency measurements with traffic light colors (green < 100ms, yellow < 200ms, red > 200ms)
4. Click Deploy to create your endpoint

graph LR
    A[Select Region] --> B[Deploy]
    B --> C[Provisioning ~1 min]
    C --> D[Running]
    D --> E{Lifecycle}
    E --> F[Stop]
    E --> G[Delete]
    F --> H[Resume]
    H --> D

Your endpoint will be ready in about a minute with:

• Unique URL: HTTPS endpoint for API calls
• Auto-Scaling: Scales with traffic automatically
• Monitoring: Request metrics and logs

After deployment, you can manage all your endpoints from the Deploy section in the sidebar, which shows a global map with active deployments, overview metrics, and a list of all endpoints.

Read more about endpoints.

Remote Training (Optional)

If you prefer to train on your own hardware, you can stream metrics to the platform using your API key. This works like Weights & Biases — train anywhere, monitor on the platform.

1. Generate an API key in Settings > Profile (API Keys section)
2. Set the environment variable and train with a project/name format:

export ULTRALYTICS_API_KEY="ul_your_api_key_here"

yolo train model=yolo26n.pt data=coco.yaml epochs=100 project=username/my-project name=exp1

Read more about API keys, dataset URIs, and remote training.

Feedback

We value your feedback! Use the feedback button to help us improve the platform.

Need Help?

If you encounter any issues or have questions:

• Documentation: Browse these docs for detailed guides
• Discord: Join our Discord community for discussions
• GitHub: Report issues on GitHub

📅 Created 1 month ago ✏️ Updated 6 days ago

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