How to: Fine-tune Model with Foundry

Using Labelbox How To
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:wave: Hello Community!

:rocket: Introducing Model Fine-tuning: Allows to tailor the model to custom use cases!

:dart: Problems Addressed:

The Model Fine tuning capability addresses the limitation of using foundational models for detecting objects beyond the scope of their existing feature set.

:hammer_and_wrench: How to Use:

Demo

  1. Go to Model , Experiment and click Create then select Model experiment.

  2. Import data rows with labels to the experiment for training.

  3. Configure the model experiment by giving it a name, ontology containing custom feature to train and define splits.

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Then click Submit which initiates the “Send to Experiment” task.

  1. Once the model run gets populated in the newly created experiment, click on
    “Fine-tune model” button

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  2. Choose a base model(YOLO is only model supported right now)

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  3. Configure training parameters and kick off fine-tuning job.

  4. Use fine-tuned model(found in Model–>Custom) just as you would use a Foundry model for inference and auto-labeling

Model Fine-tuning empowers you to train models that are tailored precisely to your needs for using custom features.

Please try Model Fine-tuning feature and share your valuable feedback.

Cheers! :tada:

3 Likes
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This is awesome, my team is going to try it out this week.

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Will this type of fine tuning be made available for other data types / foundation models?

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We do have a pipeline to get text added to the fine tuning capability to Foundry, I can’t provide a timeline yet, I will revise this message when I have more info.
What would be your requirements?

1 Like