Link to this sectionModels#

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

Link to this sectionUpload 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:

After upload, the platform parses model metadata:

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

Link to this sectionTrain 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.

Link to this sectionModel Lifecycle#

graph LR
    A[Upload .pt] --> B[Overview]
    C[Train] --> B
    B --> D[Predict]
    B --> E[Export]
    B --> F[Deploy]
    E --> G[19+ 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

Link to this sectionModel Page Tabs#

Each model page has the following tabs:

Link to this sectionOverview 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

Link to this sectionTrain Tab#

The Train tab has three subtabs:

Link to this sectionCharts Subtab#

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

Link to this sectionConsole 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

Link to this sectionSystem Subtab#

GPU and system metrics during training:

Link to this sectionPredict Tab#

Run interactive inference directly in the browser:

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

Link to this sectionExport Tab#

Export your model to 19+ deployment formats. See Export Model below and the core Export mode guide for full details.

Link to this sectionDeploy Tab#

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

Link to this sectionValidation Plots#

After training completes, view detailed validation analysis:

Link to this sectionConfusion Matrix#

Interactive heatmap showing prediction accuracy per class:

Link to this sectionPR/F1 Curves#

Performance curves at different confidence thresholds:

Link to this sectionExport 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 19+ 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

Link to this sectionSupported Formats#

The Platform supports export to 19+ deployment formats: ONNX, TorchScript, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TF Lite, TF Edge TPU, TF.js, PaddlePaddle, NCNN, MNN, RKNN, Qualcomm (QNN), IMX500, Axelera, ExecuTorch, and DeepX.

Link to this sectionFormat Selection Guide#

Link to this sectionRKNN Chip Support#

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

Link to this sectionExport Job Lifecycle#

Export jobs progress through the following statuses:

Link to this sectionBulk 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.

Link to this sectionFormat Restrictions#

Some export formats have architecture or task restrictions:

Link to this sectionClone 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.

Link to this sectionDownload 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.

Link to this sectionDataset 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.

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

Link to this sectionVisibility Settings#

Control who can see your model:

To change visibility, click the visibility badge (e.g., private or public) in the page header. Visibility is set at the project level, so this controls all models in the project. Switching to private takes effect immediately. Switching to public shows a confirmation dialog before applying.

Link to this sectionDelete Model#

Remove a model you no longer need:

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

Link to this sectionSee 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 19+ export formats

Link to this sectionFAQ#

Link to this sectionWhat 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

YOLO26 supports 6 task types: detect, segment, semantic, pose, OBB, and classify. YOLO11 and YOLOv8 support the same set except semantic segmentation, while YOLOv5 supports detect, segment, and classify.

Link to this sectionCan 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

Link to this sectionHow 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

Link to this sectionWhat's the maximum model size?#

Uploaded .pt model files are limited to 1 GB, and models near that limit may take longer to upload and process.

Link to this sectionCan 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.