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.

<strong>Watch:</strong> Get Started with Ultralytics Platform - QuickStart

The following interactive diagram outlines the four primary stages of the Ultralytics Platform workflow. Click any stage or sub-step to access detailed instructions for that section.

graph LR
    A(Sign Up) --> B(Prepare Data) --> C(Train) --> D(Deploy)
    A -.- A1["<a href='#get-started'>Create account</a>
<a href='#region-selection'>Select region</a>"]
    B -.- B1["<a href='#upload-your-first-dataset'>Upload dataset</a>
<a href='#create-your-first-project'>Create Project</a>"]
    C -.- C1["<a href='#training-configuration'>Configure training</a>
<a href='#monitor-training'>Monitor progress</a>"]
    D -.- D1["<a href='#test-your-model'>Test model</a>
<a href='#deploy-to-production'>Deploy endpoint</a>"]

    click A "#get-started"
    click B "#upload-your-first-dataset"
    click C "#train-your-first-model"
    click D "#deploy-to-production"

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.

!!! warning "Region is Permanent"

Your region selection cannot be changed after account creation. Choose the region closest to you or your users for best performance.

Free Credits

Every new account receives free credits for cloud GPU training:

!!! tip "Maximize Your Credits"

Sign up with a work email to receive $25 in credits. If you signed up with a personal email, you can verify a work email later to unlock the additional $20 in credits.

Complete Your Profile

The onboarding flow guides you through three steps:

1. Profile - Enter your display name, unique username (permanent, cannot be changed later), organization (optional), and primary use case
2. Data Region - Select US, EU, or AP with a visual world map showing latency
3. Complete - Review your selections, optionally apply a promo code, and finish signup to claim your welcome credits

??? tip "Update Later"

You can update your profile anytime from [Settings](account/settings.md), including your display name, bio, and social links. Note that your username and data region cannot be changed after signup.

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 dataset 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.

Global Search

Press Cmd+K (Mac) or Ctrl+K (Windows/Linux) to open the search bar. Search across pages, projects, datasets, and deployments instantly.

AI Chat Assistant

A floating chat widget is available on every page. Click it to ask questions about YOLO training, annotation, deployment, or any Platform feature. The assistant provides context-aware help based on the current page.

Onboarding Tours

The Platform includes guided tours that introduce key features as you explore different sections:

!!! tip "Enterprise Users"

Enterprise plan users see an enhanced Nav Tour with enterprise-specific guidance on the Train step.

Restart Tours

To replay any tour:

• Redo Tour button — Click your profile avatar (bottom-left of the sidebar) to open the user menu, then select Redo Tour. This resets all tours so they replay on your next visit to each section.
• URL parameter — Navigate to platform.ultralytics.com/home?tour=nav to restart the Nav Tour directly.

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)

!!! tip "YOLO Dataset Structure"

For best results, upload a ZIP or TAR archive (including `.tar.gz` and `.tgz`) with the standard YOLO structure:

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

For full syntax across tasks, see [detect](../datasets/detect/index.md#ultralytics-yolo-format), [segment](../datasets/segment/index.md#ultralytics-yolo-format), [pose](../datasets/pose/index.md#ultralytics-yolo-format), [OBB](../datasets/obb/index.md#yolo-obb-format), and [classify](../datasets/classify/index.md#dataset-structure-for-yolo-classification-tasks) dataset guides.

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. The default is RTX PRO 6000 (96 GB Blackwell, $1.69/hr), which handles every YOLO26 variant. See the full GPU pricing table or the Cloud Training GPU step for the complete list and tier gating.

!!! warning "Credit Balance Required"

Cloud training requires a positive credit balance sufficient to cover the estimated job cost. Check your balance in [`Settings > Billing`](account/billing.md). New accounts receive free credits ($5 for personal email, $25 for work email).

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.

!!! tip "Cancel Training"

You can cancel a running training job at any time. You're only charged for the compute time used up to that point.

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:

=== "Python"

```python
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"

```bash
curl -X POST "https://platform.ultralytics.com/api/models/{model_id}/predict" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "[email protected]"
```

!!! tip "Auto-Inference"

The Predict tab runs inference automatically when you drop an image — no need to click a button. Example images (bus.jpg, zidane.jpg) are preloaded for instant testing.

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
• Scale-to-zero behavior: No idle compute cost (deployments currently run a single active instance)
• Monitoring: Request metrics and logs

!!! info "Deployment Lifecycle"

Endpoints can be **started**, **stopped**, and **deleted**. Stopped endpoints don't incur compute costs but retain their configuration. Restart a stopped endpoint with one click.

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 > API Keys
2. Set the environment variable and train with a project/name format:

export ULTRALYTICS_API_KEY="YOUR_API_KEY"

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

!!! note "API Key Format"

API keys start with `ul_` followed by 40 hex characters (43 characters total). Keys are full-access tokens scoped to your workspace.

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

Feedback & Help

The Help page in the sidebar footer includes an in-app feedback form. You can rate your experience, choose a feedback type (bug, feature request, or general), and attach screenshots.

If you need more help:

• AI Chat: Click the floating chat widget on any page for instant help
• Documentation: Browse these docs for detailed guides on datasets, annotation, training, deployment, and billing
• Discord: Join our Discord community for discussions
• GitHub: Report issues on GitHub
• REST API: See the API reference or try the interactive API docs for programmatic access to all Platform features