Create a model run

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Before you start

• Import data rows: You’ll need to have a set of data rows to upload the predictions on. If you do not already have a set of data rows in Labelbox, you’ll need to import that data first.
• Create an ontology: In order to create a model run, you’ll need to specify the ontology (also called taxonomy) that corresponds to the set of predictions. You may want to re-use an ontology that already exists in Labelbox (e.g., an ontology already used for a labeling project). Or, you may want to use an ontology for your model predictions that does not exist in Labelbox yet. In the latter case, you’ll need to create an ontology.

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Step 1: Create an experiment and your first model run

• Option A: Start from Catalog
  1. Go to the Catalog tab.
  2. Use the filters to select the dataset or a subset of data rows you want to use for this experiment.
  3. Click the Manage selection button at the bottom of the screen.
  4. Select New experiment from the action menu.
• Option B: Start from the Model tab
  1. Navigate to the Model tab.
  2. Click the + Create button in the top right and select Experiment.
  3. Select the batch of data rows you wish to include in this experiment.

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Optional step: Include ground truth labels

• Why this is important: Without ground truth, you can visualize your model’s predictions, but you cannot quantitatively score its performance or identify where it is correct or incorrect.
• How to do it: Simply select the project containing the relevant, reviewed labels from the dropdown menu. If you don’t have labeled data yet or if it resides in a different project, you can skip this for now and link it later from the model run settings.

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Optional step: Include existing model predictions

• Why this is important: This step populates your model run with your model’s output, making it ready for analysis as soon as you finish the setup.
• How to do it: The primary method for uploading predictions is via our Python SDK. In this initial setup screen, you can select an existing upload or choose to upload them in the next step. The detailed, step-by-step guide for generating and uploading this payload is covered in the next section of this tutorial.

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Optional step: Define data splits

• Why this is important: Creating splits allows you to analyze your model’s performance on your validation or test data separately from the data it was trained on. This is critical for diagnosing overfitting and ensuring your model will perform well in the real world.
• How to do it: You have two flexible options for creating splits:
  1. Split by percentage: Easily divide your data by specifying a percentage for each split (e.g., 80% training, 10% validation, 10% test). Labelbox will handle the random assignment of data rows.
  2. Use existing slices: For more control, you can assign pre-existing Slices to your splits. This is useful if you have specific, curated datasets you want to use for validation or testing.

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Step 2: Upload predictions to the model run

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Conceptual overview

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Step-by-step guides

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Step 3: Update your model run config file

1. Go to your Model Run page.
2. Click the Settings icon and select Model run config.
3. Edit the JSON file to include the hyperparameters for this model run.

{
  "model_architecture": "YOLOv8-large",
  "training_epochs": 150,
  "learning_rate": 0.001,
  "optimizer": "Adam",
  "image_size": "640x640",
  "data_augmentation": {
    "horizontal_flip": true,
    "rotation_range": 15
  }
}