ultralytics/docs/en/platform/train/models.md

comments	description	keywords
true	Learn how to manage, analyze, and export trained models in Ultralytics Platform with support for 17+ deployment formats.	Ultralytics Platform, models, model management, export, ONNX, TensorRT, CoreML, YOLO

Models

Ultralytics Platform provides comprehensive model management for training, analyzing, and deploying YOLO models. Upload pretrained models or train new ones directly on the platform.

Upload Model

Upload existing model weights to the platform:

1. Navigate to your project
2. Drag and drop .pt files onto the project page or models sidebar
3. Model metadata is parsed automatically from the file

Multiple files can be uploaded simultaneously (up to 3 concurrent).

Supported model formats:

Format	Extension	Description
PyTorch	.pt	Native Ultralytics format

After upload, the platform parses model metadata:

• Task type (detect, segment, pose, OBB, classify)
• Architecture (YOLO26n, YOLO26s, etc.)
• Class names and count
• Input size and parameters
• Training results and metrics (if present in checkpoint)

Train Model

Train a new model directly on the platform:

1. Navigate to your project
2. Click New Model
3. Select base model and dataset
4. Configure training parameters
5. Choose cloud or local training
6. Start training

See Cloud Training for detailed instructions.

Model Lifecycle

graph LR
    A[Upload .pt] --> B[Overview]
    C[Train] --> B
    B --> D[Predict]
    B --> E[Export]
    B --> F[Deploy]
    E --> G[17+ Formats]
    F --> H[Endpoint]

    style A fill:#4CAF50,color:#fff
    style C fill:#FF9800,color:#fff
    style E fill:#2196F3,color:#fff
    style F fill:#9C27B0,color:#fff

Model Page Tabs

Each model page has the following tabs:

Tab	Content
Overview	Model metadata, key metrics, dataset link
Train	Training charts, console output, system stats
Predict	Interactive browser inference
Export	Format conversion with GPU selection
Deploy	Endpoint creation and management

Overview Tab

Displays model metadata and key metrics:

• Model name (editable), status badge, task type
• Final metrics (mAP50, mAP50-95, precision, recall)
• Metric sparkline charts showing training progression
• Training arguments (epochs, batch size, image size, etc.)
• Dataset link (when trained with a Platform dataset)
• Download button for model weights

Train Tab

The Train tab has three subtabs:

Charts Subtab

Interactive training metric charts showing loss curves and performance metrics over epochs:

Chart Group	Metrics
Metrics	mAP50, mAP50-95, precision, recall
Train Loss	train/box_loss, train/cls_loss, train/dfl_loss
Val Loss	val/box_loss, val/cls_loss, val/dfl_loss
Learning Rate	lr/pg0, lr/pg1, lr/pg2

Console Subtab

Live console output from the training process:

• Real-time log streaming during training
• Epoch progress bars and validation results
• Error detection with highlighted error banners
• ANSI color support for formatted output

System Subtab

GPU and system metrics during training:

Metric	Description
GPU Util	GPU utilization percentage
GPU Memory	GPU memory usage
GPU Temp	GPU temperature
CPU Usage	CPU utilization
RAM	System memory usage
Disk	Disk usage

Predict Tab

Run interactive inference directly in the browser:

• Upload an image, use example images, or use webcam
• Results display with bounding boxes, masks, or keypoints
• Auto-inference when an image is provided
• Supports all task types (detect, segment, pose, OBB, classify)

!!! tip "Quick Testing"

The Predict tab runs inference on Ultralytics Cloud, so you don't need a local GPU. Results are displayed with interactive overlays matching the model's task type.

Export Tab

Export your model to 17+ deployment formats. See Export Model below and the core Export mode guide for full details.

Deploy Tab

Create and manage dedicated inference endpoints. See Deployments for details.

Validation Plots

After training completes, view detailed validation analysis:

Confusion Matrix

Interactive heatmap showing prediction accuracy per class:

PR/F1 Curves

Performance curves at different confidence thresholds:

Curve	Description
Precision-Recall	Trade-off between precision and recall
F1-Confidence	F1 score at different confidence levels
Precision-Confidence	Precision at different confidence levels
Recall-Confidence	Recall at different confidence levels

Export Model

graph LR
    A[Select Format] --> B[Configure Args]
    B --> C[Export]
    C --> D{GPU Required?}
    D -->|Yes| E[Cloud GPU Export]
    D -->|No| F[CPU Export]
    E --> G[Download]
    F --> G

    style A fill:#2196F3,color:#fff
    style C fill:#FF9800,color:#fff
    style G fill:#4CAF50,color:#fff

Export your model to 17+ deployment formats:

1. Navigate to the Export tab
2. Select target format
3. Configure export arguments (image size, half precision, dynamic, etc.)
4. For GPU-required formats (TensorRT), select a GPU type
5. Click Export
6. Download when complete

Supported Formats

The Platform supports export to 17+ deployment formats: ONNX, TorchScript, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TF Lite, TF Edge TPU, TF.js, PaddlePaddle, NCNN, MNN, RKNN, IMX500, Axelera, and ExecuTorch.

Format Selection Guide

Target	Recommended Format	Notes
NVIDIA GPUs	TensorRT	Maximum inference speed
Intel Hardware	OpenVINO	CPUs, GPUs, and VPUs
Apple Devices	CoreML	iOS, macOS, Apple Silicon
Android	TF Lite or NCNN	Best mobile performance
Web Browsers	TF.js or ONNX	ONNX via ONNX Runtime Web
Edge Devices	TF Edge TPU or RKNN	Coral and Rockchip (see supported chips)
General	ONNX	Works with most runtimes

RKNN Chip Support

When exporting to RKNN format, select your target Rockchip device:

Chip	Description
RK3588	High-end edge SoC
RK3576	Mid-range edge SoC
RK3568	Mid-range edge SoC
RK3566	Mid-range edge SoC
RK3562	Entry-level edge SoC
RV1103	Vision processor
RV1106	Vision processor
RV1103B	Vision processor
RV1106B	Vision processor
RK2118	AI processor
RV1126B	Vision processor

Export Job Lifecycle

Export jobs progress through the following statuses:

Status	Description
Queued	Export job is waiting to start
Starting	Export job is initializing
Running	Export is in progress
Completed	Export finished — download available
Failed	Export failed (see error message)
Cancelled	Export was cancelled by the user

!!! tip "Export Time"

Export time varies by format. TensorRT exports may take several minutes due to engine optimization. GPU-required formats (TensorRT) run on Ultralytics Cloud GPUs — the default export GPU is RTX 5090.

Bulk Export Actions

• Export All: Click Export All to start export jobs for all CPU-based formats with default settings.
• Delete All Exports: Click Delete All to remove all exports for the model.

Format Restrictions

Some export formats have architecture or task restrictions:

Format	Restriction
IMX500	Available only for YOLOv8n and YOLO11n
Axelera	Detect models only
PaddlePaddle	Not available for YOLO26 detect/segment/pose/OBB models

!!! note "Additional Export Rules"

- Classification exports do not include NMS.
- CoreML exports with batch sizes greater than `1` use `dynamic=true`.
- Unsupported format/model combinations are disabled in the export dialog before you launch.

Clone Model

Clone a model to a different project:

1. Open the model page
2. Click the Clone button
3. Select the destination project
4. Click Clone

The model and its weights are copied to the target project.

Download Model

Download your model weights:

1. Navigate to the model's Overview tab
2. Click the Download button
3. The original .pt file downloads automatically

Exported formats can be downloaded from the Export tab after export completes.

Dataset Linking

Models can be linked to their source dataset:

• View which dataset was used for training
• Click the dataset card on the Overview tab to navigate to it
• Track data lineage

When training with Platform datasets using the ul:// URI format, linking is automatic.

!!! example "Dataset URI Format"

```bash
# Train with a Platform dataset — linking is automatic
yolo train model=yolo26n.pt data=ul://username/datasets/my-dataset epochs=100
```

The `ul://` scheme resolves to your Platform dataset. The trained model's Overview tab will show a link back to this dataset (see [Using Platform Datasets](../api/index.md#using-platform-datasets)).

Visibility Settings

Control who can see your model:

Setting	Description
Private	Only you can access
Public	Anyone can view on Explore page

To change visibility, click the visibility badge (e.g., private or public) on the model page. Switching to private takes effect immediately. Switching to public shows a confirmation dialog before applying.

Delete Model

Remove a model you no longer need:

1. Open model actions menu
2. Click Delete
3. Confirm deletion

!!! note "Trash and Restore"

Deleted models go to Trash for 30 days. Restore from [Settings > Trash](../account/trash.md).

See Also

• Inference: Test models in the browser with the Predict tab
• Endpoints: Deploy models to production with dedicated endpoints
• Cloud Training: Configure and run training jobs on cloud GPUs
• Export Formats: Full guide to all 17+ export formats

FAQ

What model architectures are supported?

Ultralytics Platform fully supports all YOLO architectures with dedicated projects:

• YOLO26: n, s, m, l, x variants (latest, recommended) — platform.ultralytics.com/ultralytics/yolo26
• YOLO11: n, s, m, l, x variants — platform.ultralytics.com/ultralytics/yolo11
• YOLOv8: n, s, m, l, x variants — platform.ultralytics.com/ultralytics/yolov8
• YOLOv5: n, s, m, l, x variants — platform.ultralytics.com/ultralytics/yolov5

All architectures support 5 task types: detect, segment, pose, OBB, and classify.

Can I download my trained model?

Yes, download your model weights from the model page:

1. Click the download icon on the Overview tab
2. The original .pt file downloads automatically
3. Exported formats can be downloaded from the Export tab

How do I compare models across projects?

Currently, model comparison is within projects. To compare across projects:

1. Clone models to a single project, or
2. Export metrics and compare externally

What's the maximum model size?

There's no strict limit, but very large models (>2GB) may have longer upload and processing times.

Can I fine-tune pretrained models?

Yes! You can use any of the official YOLO26 models as a base, or select one of your own completed models from the model selector in the training dialog. The Platform supports fine-tuning from any uploaded checkpoint.