ultralytics/docs/en/platform/index.md

comments	description	keywords
true	Ultralytics Platform is an end-to-end computer vision platform for data preparation, model training, and deployment with multi-region infrastructure.	Ultralytics Platform, YOLO, computer vision, model training, cloud deployment, annotation, inference, YOLO11, YOLO26, machine learning

Ultralytics Platform

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Ultralytics Platform is a comprehensive end-to-end computer vision platform that streamlines the entire ML workflow from data preparation to model deployment. Built for teams and individuals who need production-ready computer vision solutions without the infrastructure complexity.

What is Ultralytics Platform?

Ultralytics Platform is designed to replace fragmented ML tooling with a unified solution. It combines the capabilities of:

• Roboflow - Data management and annotation
• Weights & Biases - Experiment tracking
• SageMaker - Cloud training
• HuggingFace - Model deployment
• Arize - Monitoring

All in one platform with native support for YOLO26 and YOLO11 models.

Workflow: Upload → Annotate → Train → Export → Deploy

The Platform provides an end-to-end workflow:

graph LR
    subgraph Data["📁 Data"]
        A[Upload] --> B[Annotate]
        B --> C[Analyze]
    end
    subgraph Train["🚀 Train"]
        D[Configure] --> E[Train on GPU]
        E --> F[View Metrics]
    end
    subgraph Deploy["🌐 Deploy"]
        G[Export] --> H[Deploy Endpoint]
        H --> I[Monitor]
    end
    Data --> Train --> Deploy

Stage	Features
Upload	Images (50MB), videos (1GB), and dataset files (ZIP, TAR including .tar.gz/.tgz, NDJSON) with automatic processing
Annotate	Manual tools for all 5 task types, plus Smart Annotation with SAM and YOLO models for detect, segment, and OBB (see supported tasks)
Train	Cloud GPUs (20 on all plans + 3 Pro/Enterprise-only: H200 NVL, H200 SXM, B200), real-time metrics, project organization
Export	17 deployment formats (ONNX, TensorRT, CoreML, TFLite, etc.; see supported formats)
Deploy	43 global regions with dedicated endpoints, scale-to-zero by default (single active instance), and monitoring

What you can do:

• Upload images, videos, and dataset files to create training datasets
• Visualize annotations with interactive overlays for all 5 YOLO task types (see supported tasks)
• Train models on cloud GPUs (20 on all plans, 23 with Pro or Enterprise for H200 and B200) with real-time metrics
• Export to 17 deployment formats (ONNX, TensorRT, CoreML, TFLite, etc.)
• Deploy to 43 global regions with one-click dedicated endpoints
• Monitor training progress, deployment health, and usage metrics
• Collaborate by making projects and datasets public for the community

Multi-Region Infrastructure

Your data stays in your region. Ultralytics Platform operates infrastructure in three global regions:

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	Taiwan, Asia-Pacific	Asia-Pacific users, lowest APAC latency

You select your region during onboarding, and all your data, models, and deployments remain in that region.

!!! warning "Region is Permanent"

Your data region cannot be changed after account creation. During onboarding, the platform measures latency to each region and recommends the closest one. Choose carefully.

Key Features

Data Preparation

• Dataset Management: Upload images, videos, or dataset files with automatic processing
• Annotation Editor: Manual annotation for all 5 YOLO task types (detect, segment, pose, OBB, classify; see supported tasks)
• Skeleton Templates: Built-in (Person, Hand, Face, Dog, Box) and custom skeleton templates for one-click pose annotation
• Smart Annotation: Use SAM 2.1 (Tiny, Small, Base, Large), SAM 3, pretrained Ultralytics YOLO models, or your own fine-tuned YOLO models from the annotation toolbar for detect, segment, and OBB tasks
• Dataset Versioning: Create numbered NDJSON snapshots with descriptions for reproducible training
• Statistics: Class distribution, location heatmaps, and dimension analysis

graph LR
    A[Upload Dataset/Images/Video] --> B[Auto-Process]
    B --> C[Browse & Filter]
    C --> D{Annotate}
    D --> E[Manual Tools]
    D --> F[SAM Smart]
    D --> G[YOLO Auto-Label]
    E --> H[Train-Ready Dataset]
    F --> H
    G --> H

!!! tip "Supported Task Types"

The annotation editor supports all 5 YOLO task types: **[detect](../datasets/detect/index.md)** (bounding boxes), **[segment](../datasets/segment/index.md)** (polygons), **[pose](../datasets/pose/index.md)** (keypoints), **[OBB](../datasets/obb/index.md)** (oriented boxes), and **[classify](../datasets/classify/index.md)** (image-level labels). Each task type has dedicated drawing tools and keyboard shortcuts.

Model Training

• Cloud Training: Train on cloud GPUs (20 on all plans, 23 with Pro or Enterprise for H200 and B200) with real-time metrics
• Remote Training: Train anywhere and stream metrics to the platform (W&B-style)
• Project Organization: Group related models, compare experiments, track activity
• 17 Export Formats: ONNX, TensorRT, CoreML, TFLite, and more (see supported formats)

You can train models either through the web UI (cloud training) or from your own machine (remote training):

=== "Cloud Training (Web UI)"

1. Navigate to your project
2. Click `Train Model`
3. Select dataset, model, GPU, and epochs
4. Monitor real-time loss curves and metrics

=== "Remote Training (CLI)"

```bash
# Install ultralytics
pip install "ultralytics>=8.4.35"

# Set your API key
export ULTRALYTICS_API_KEY="YOUR_API_KEY"

# Train and stream metrics to the platform
yolo train model=yolo26n.pt data=coco.yaml epochs=100 project=username/my-project name=exp1
```

=== "Remote Training (Python)"

```python
import os

from ultralytics import YOLO

os.environ["ULTRALYTICS_API_KEY"] = "YOUR_API_KEY"

model = YOLO("yolo26n.pt")
model.train(
    data="coco.yaml",
    epochs=100,
    project="username/my-project",
    name="exp1",
)
# Metrics stream to Platform automatically
```

Deployment

• Inference Testing: Test models directly in the browser with custom images
• Dedicated Endpoints: Deploy to 43 global regions with scale-to-zero by default (single active instance)
• Monitoring: Real-time metrics, request logs, and performance dashboards

graph LR
    A[Trained Model] --> B{Action}
    B --> C[Browser Predict]
    B --> D[Export Format]
    B --> E[Deploy Endpoint]
    D --> F[ONNX / TensorRT / CoreML / TFLite / ...]
    E --> G[43 Global Regions]
    G --> H[API Endpoint URL]
    H --> I[Monitor & Scale]

Once deployed, call your endpoint from any language:

=== "Python"

```python
import requests

url = "https://your-endpoint-url/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://your-endpoint-url/predict" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@image.jpg"
```

=== "JavaScript"

```javascript
const form = new FormData();
form.append("file", fileInput.files[0]);

const response = await fetch("https://your-endpoint-url/predict", {
    method: "POST",
    headers: { Authorization: "Bearer YOUR_API_KEY" },
    body: form,
});

const results = await response.json();
console.log(results);
```

Account Management

• Teams & Organizations: Collaborate with team members, manage roles and invites
• API Keys: Secure key management for remote training and API access
• Credits & Billing: Pay-as-you-go training with transparent pricing
• Activity Feed: Track all account events and actions
• Trash & Restore: 30-day soft delete with item recovery
• GDPR Compliance: Data export and account deletion

!!! info "Plan Tiers"

| Feature              | Free           | Pro ($29/mo)            | Enterprise     |
| -------------------- | -------------- | ----------------------- | -------------- |
| Signup Credit        | $5 / $25*      | -                       | Custom         |
| Monthly Credit       | -              | $30/seat/month          | Custom         |
| Models               | 100            | 500                     | Unlimited      |
| Concurrent Trainings | 3              | 10                      | Unlimited      |
| Deployments          | 3              | 10                      | Unlimited      |
| Storage              | 100 GB         | 500 GB                  | Unlimited      |
| Cloud GPU Types      | 20             | 23 (incl. H200 / B200)  | 23             |
| Teams                | -              | Up to 5 members         | Up to 50       |
| Support              | Community      | Priority                | Dedicated      |

*$5 at signup, or $25 with a verified company/work email.

Quick Links

Get started with these resources:

• Quickstart: Create your first project and train a model in minutes
• Datasets: Upload and manage your training data
• Annotation: Label your data with manual and AI-assisted tools
• Projects: Organize your models and experiments
• Cloud Training: Train on cloud GPUs
• Inference: Test your models
• Endpoints: Deploy models to production
• Monitoring: Track deployment performance
• API Keys: Manage API access
• Billing: Credits and payment
• Activity: Track account events
• Trash: Recover deleted items
• REST API: API reference

FAQ

How do I get started with Ultralytics Platform?

To get started with Ultralytics Platform:

1. Sign Up: Create an account at platform.ultralytics.com
2. Select Region: Choose your data region (US, EU, or AP) during onboarding
3. Upload Dataset: Navigate to the Datasets section to upload your data
4. Train Model: Create a project and start training on cloud GPUs
5. Deploy: Test your model and deploy to a dedicated endpoint

For a detailed guide, see the Quickstart page.

What are the benefits of Ultralytics Platform?

Ultralytics Platform offers:

• Unified Workflow: Data, training, and deployment in one place
• Multi-Region: Data residency in US, EU, or AP regions
• No-Code Training: Train advanced YOLO models without writing code
• Real-Time Metrics: Stream training progress and monitor deployments
• 43 Deploy Regions: Deploy models close to your users worldwide
• 5 Task Types: Support for detection, segmentation, pose, OBB, and classification (see task docs)
• AI-Assisted Annotation: Smart annotation with SAM and YOLO models to speed up data preparation

What GPU options are available for cloud training?

Ultralytics Platform supports multiple GPU types for cloud training:

{% include "macros/platform-gpu-table.md" %}

See Cloud Training for complete pricing and GPU options.

How does remote training work?

You can train models on your own hardware and stream real-time metrics to the platform, similar to Weights & Biases.

!!! warning "Package Version Requirement"

Platform integration requires **ultralytics>=8.4.35**. Lower versions will NOT work with Platform.

```bash
pip install "ultralytics>=8.4.35"
```

=== "CLI"

```bash
# Set your API key
export ULTRALYTICS_API_KEY="YOUR_API_KEY"

# Train with project/name to stream metrics
yolo train model=yolo26n.pt data=coco.yaml epochs=100 project=username/my-project name=exp1
```

=== "Python"

```python
import os

from ultralytics import YOLO

os.environ["ULTRALYTICS_API_KEY"] = "YOUR_API_KEY"

model = YOLO("yolo26n.pt")
model.train(
    data="coco.yaml",
    epochs=100,
    project="username/my-project",
    name="exp1",
)
```

=== "Platform Dataset (ul:// URI)"

```bash
# Train using a Platform dataset directly
export ULTRALYTICS_API_KEY="YOUR_API_KEY"

yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset epochs=100 project=username/my-project name=exp1
```

See Cloud Training for more details on remote training.

What annotation tools are available?

The Platform includes a full-featured annotation editor supporting:

• Manual Tools: Bounding boxes, polygons, keypoints with skeleton templates, oriented boxes, classification
• Skeleton Templates: Place all keypoints at once using built-in (Person, Hand, Face, Dog, Box) or custom templates
• Smart Annotation: Use SAM 2.1 or SAM 3 for click-based annotation, or run pretrained Ultralytics YOLO models and your own fine-tuned YOLO models from the toolbar for detect, segment, and OBB
• Keyboard Shortcuts: Efficient workflows with hotkeys

Shortcut	Action
V	Manual (draw) mode
S	Smart mode (SAM or YOLO model)
A	Toggle auto-apply (in Smart mode)
1 - 9	Select class by number
Delete	Delete selected annotation
Ctrl+Z	Undo
Ctrl+Y	Redo
Escape	Save / deselect / exit

See Annotation for the complete guide.

What export formats are supported?

The Platform supports 17 deployment formats:

Format	File Extension	Use Case
ONNX	.onnx	Cross-platform deployment
TorchScript	.torchscript	C++ deployment
OpenVINO	_openvino_model	Intel hardware
TensorRT	.engine	NVIDIA GPU inference
CoreML	.mlpackage	Apple devices
TFLite	.tflite	Mobile/edge devices
TF SavedModel	_saved_model	TensorFlow ecosystem
TF GraphDef	.pb	TensorFlow legacy
PaddlePaddle	_paddle_model	Baidu ecosystem
NCNN	_ncnn_model	Mobile (Android/ARM)
Edge TPU	_edgetpu.tflite	Google Coral devices
TF.js	_web_model	Browser deployment
MNN	.mnn	Alibaba mobile
RKNN	_rknn_model	Rockchip NPU
IMX500	_imx_model	Sony IMX500 sensor
Axelera	_axelera_model	Axelera AI accelerators
ExecuTorch	_executorch_model	PyTorch mobile

See Models Export, the Export mode guide, and the Integrations index for format-specific options.

Troubleshooting

Dataset Issues

Problem	Solution
Dataset won't process	Check file format is supported (JPEG, PNG, WebP, TIFF, HEIC, AVIF, BMP, JP2, DNG, MPO for images). Max file size: images 50 MB, videos 1 GB, dataset archives 10 GB (Free) / 20 GB (Pro) / 50 GB (Enterprise)
Missing annotations	Verify labels are in YOLO format with .txt files matching image filenames, or upload COCO JSON
"Train split required"	Add train/ folder to your dataset structure, or redistribute splits via the split bar
Class names undefined	Add a data.yaml file with names: list (see YOLO format), or define classes in the Classes tab

Training Issues

Problem	Solution
Training won't start	Check credit balance in Settings > Billing. Positive balance required
Out of memory error	Reduce batch size, use smaller model (n/s), or select GPU with more VRAM
Poor metrics	Check dataset quality, increase epochs, try data augmentation, verify class balance
Training slow	Select faster GPU, reduce image size, check dataset isn't bottlenecked

Deployment Issues

Problem	Solution
Endpoint not responding	Check endpoint status (Ready vs Stopped). Cold start may take 5-15 seconds
401 Unauthorized	Verify API key is correct and has required scopes
Slow inference	Check model size, consider TensorRT export, select closer region
Export failed	Some formats require specific model architectures. Try ONNX for broadest compatibility

Common Questions

??? question "Can I change my username after signup?"

No, usernames are permanent and cannot be changed. Choose carefully during signup.

??? question "Can I change my data region?"

No, data region is selected during signup and cannot be changed. To switch regions, create a new account and re-upload your data.

??? question "How do I get more credits?"

Go to Settings > Billing > Add Credits. Purchase credits from $5 to $1000. Purchased credits never expire.

??? question "What happens if training fails?"

You're only charged for completed compute time. Checkpoints are saved, and you can resume training.

??? question "Can I download my trained model?"

Yes, click the download icon on any model page to download the `.pt` file or exported formats.

??? question "How do I share my work publicly?"

Edit your project or dataset settings and toggle visibility to "Public". Public content appears on the Explore page.

??? question "What are the file size limits?"

Images: 50MB, Videos: 1GB, datasets: 10GB on Free, 20GB on Pro, 50GB on Enterprise. For larger files, split into multiple uploads.

??? question "How long are deleted items kept in Trash?"

30 days. After that, items are permanently deleted and cannot be recovered.

??? question "Can I use Platform models commercially?"

Free and Pro plans use the AGPL license. For commercial use without AGPL requirements, see [Ultralytics Licensing](https://www.ultralytics.com/license).