Files

2026-06-24 09:35:46 +08:00

17 KiB

Executable File

Raw Blame History

comments, description, keywords

comments	description	keywords
true	Get started with Ultralytics Platform in minutes. Learn to create an account, upload datasets, train YOLO models, and deploy to production.	Ultralytics Platform, Quickstart, YOLO models, dataset upload, model training, cloud deployment, machine learning

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.

Region	Label	Location	Best For
US	Americas	Iowa, USA	Americas users, fastest for Americas
EU	Europe, Middle East & Africa	Belgium, Europe	European users, GDPR compliance
AP	Asia Pacific	Hong Kong, Asia-Pacific	Asia-Pacific users, lowest APAC latency

!!! 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:

Email Type	Sign-up Credits	How to Qualify
Work/Company Email	$25.00	Use your company domain (@company.com)
Personal Email	$5.00	Gmail, Yahoo, Outlook, etc.

!!! 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

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.

??? tip "Update Later"

You can update your profile anytime from the Settings page, including your display name, bio, and social links. Note that your username 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.

The sidebar provides access to all Platform sections:

Section	Item	Description
Top	Search	Quick search across all your resources (Cmd+K)
	Home	Dashboard with quick actions and recent activity
	Explore	Discover public projects and datasets
My Projects	Annotate	Your datasets organized for annotation
	Train	Your projects containing trained models
	Deploy	Your active deployments
Bottom	Trash	Deleted items (recoverable for 30 days)
	Settings	Account, billing, and preferences
	Feedback	Send feedback to Ultralytics

Welcome Card

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

Stat	Description
Datasets	Number of datasets
Images	Total images across all datasets
Annotations	Total annotation count
Projects	Number of projects
Models	Total trained models
Exports	Number of model exports
Deployments	Active deployment count

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):

Format	Max Size	Description
Images	50 MB	JPG, PNG, WebP, TIFF, and other common formats
ZIP Archive	10 GB	Compressed folder with images and labels
Video	1 GB	MP4, AVI - frames extracted at ~1 fps (max 100 frames)
YOLO Format	10 GB	Standard YOLO dataset structure with labels

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:

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

!!! tip "YOLO Dataset Structure"

For best results, upload a ZIP 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

Train Your First Model

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

Training Configuration

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

Model	Size	Speed	Accuracy	Recommended GPU
YOLO26n	Nano	Fastest	Good	RTX PRO 6000 (96 GB)
YOLO26s	Small	Fast	Better	RTX PRO 6000 (96 GB)
YOLO26m	Medium	Moderate	High	RTX PRO 6000 (96 GB)
YOLO26l	Large	Slower	Higher	A100 (80 GB)
YOLO26x	Extra Large	Slowest	Best	H100 (80 GB)

!!! info "GPU Selection"

GPUs range from $0.24/hr (RTX 2000 Ada, 16 GB) to $4.99/hr (B200, 180 GB). The default GPU is **RTX PRO 6000** (96 GB Blackwell, $1.89/hr) — a great balance of memory and performance. See the full [GPU pricing table](index.md#what-gpu-options-are-available-for-cloud-training) for all 22 options.

!!! 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:

Subtab	Content
Charts	Training/validation loss curves, mAP, precision, recall
Console	Live training log output
System	GPU utilization, memory usage, hardware metrics

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.

Test Your Model

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

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

Adjust inference parameters:

Parameter	Default	Description
Confidence	0.25	Filter low-confidence predictions
IoU	0.7	Control overlap for NMS
Image Size	640	Resize input for inference

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 "file=@image.jpg"
```

!!! 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.

Deploy to Production

Deploy your model to a dedicated endpoint for production use:

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

!!! 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.

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.

Generate an API key in Settings > Profile (API Keys section)
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

!!! 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

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

??? info "Feedback Privacy"

Your feedback is private and only visible to the Ultralytics team. We use it to prioritize features and fix issues.

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

!!! note

When reporting a bug, please include your browser and operating system details to help us diagnose the issue.

17 KiB Executable File Raw Blame History