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, or click the Upload models icon
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, depth, classify, pose, OBB)
• 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]:::start --> B[Overview]:::proc
    C[Train]:::start --> B
    B --> D[Predict]:::proc
    B --> E[Export]:::proc
    B --> F[Deploy]:::proc
    E --> G[20 Formats]:::out
    F --> H[Endpoint]:::out

    classDef start fill:#4CAF50,color:#fff
    classDef proc fill:#2196F3,color:#fff
    classDef out fill:#9C27B0,color:#fff

Model Page Tabs#

Each model page has the following tabs:

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:

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:

Predict 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, depth, classify, pose, OBB)

Export Tab#

Export your model to 20 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#

When the training run provides them, the Validation subtab also displays performance curves at different confidence thresholds. Available validation plots depend on the artifacts produced by the run.

Export Model#

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

    classDef start fill:#4CAF50,color:#fff
    classDef proc fill:#2196F3,color:#fff
    classDef decide fill:#FF9800,color:#fff
    classDef out fill:#9C27B0,color:#fff

Export your model to 20 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 Start Export
6. Download when complete

Connect Slack alerts to receive a message when an export is ready or fails.

Supported Formats#

The Platform supports export to 20 deployment formats: ONNX, TorchScript, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, LiteRT, TF Edge TPU, PaddlePaddle, NCNN, MNN, RKNN, Qualcomm (QNN), IMX500, Axelera, ExecuTorch, DeepX, Hailo, and Huawei Ascend.

Format Selection Guide#

NVIDIA Jetson TensorRT Targets#

Ultralytics Platform offers the following Jetson target selections for TensorRT .engine exports. As of July 2026, the Jetson export workers use JetPack 7.2 / L4T r39.2, Python 3.12.3, NVIDIA PyTorch 2.12.0a0 (26.04 build), CUDA 13.2, and TensorRT 10.16.1.11 inside the export container.

The timings are single observed end-to-end production routing tests from July 2026, rounded to the nearest second; they are reference measurements, not an SLA or per-SKU performance benchmark. Both Thor selections are built on a T5000 Developer Kit in NVIDIA's T4000 compatibility profile. The six Orin routes are built on an AGX Orin 64GB, where every resulting engine was loaded and run.

RKNN Chip Support#

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

Export Job Lifecycle#

Export jobs progress through the following statuses:

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:

Clone Model#

Clone a model to a different project:

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

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 model's primary .pt checkpoint 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.

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

Custom Metadata#

Open More actions and select Information to review two sections:

• Ultralytics Metadata: Read-only Platform details such as the model ID, project, source dataset, task, status, and timestamps
• Custom Metadata: Your own JSON object for evaluation context, release tracking, governance, or other model-specific data

Custom metadata is separate from training-owned model information, environment details, and training arguments. Workspace viewers can inspect it, while members with edit access can replace the object. The serialized metadata object is limited to 500,000 characters, and each top-level key is limited to 128 characters. Save an empty object ({}) to clear custom metadata.

Visibility 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.

Delete Model#

Remove a model you no longer need:

1. Click the Delete model trash icon in the model header
2. Confirm deletion

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 20 export formats

FAQ#