March 15, 2023

Introducing Pre-labels Validation: A new way to label your data

Introducing Pre-labels Validation: A new way to label your data

We’re excited to introduce pre-labels validation, a powerful way to make your labeling more accurate, cheaper and faster by injecting your pre-annotations. This fundamentally changes the labeling process by letting our annotators focus on accepting, refining or fixing the pre-labels rather than annotating an image from scratch. 

Example use cases:

  • Reviewing and correcting your model output 
  • Changing annotation classes for a label schema change, e.g. breaking a dent class to big-dent and small-dent
  • Improving an open source labeled dataset by treating the data as pre-labels
  • Reviewing annotation output from your third party labeling teams

With each model iteration, the amount of labeled data required often increases exponentially — presenting a clear incentive for ML teams to increase labeling efficiency with pre-labels. 

Here’s how one of our customers used pre-labels during the feature’s beta phase:

How an automotive company used our pre-labels validation for 30% cost savings

A global renowned automotive company is training machine learning models to detect different car components for their next-generation smart factory. Using Bolt’s pre-labeling solution, the company was able to reduce their overall labeling costs by 30%.

We ingested pre-labels from their existing models for annotators to correct and/or adjust the classes and tightness of the annotations. There was no need to label the images from scratch – money saved for our client. The machine learning team then took the labelers’ output to improve the car components detection models further, which in turn generated better pre-labels for subsequent iterations. This helped to speed up the labeling process even more with each new pre-labels validation batch we did.

To start, simply select the Pre-labels validation project type while creating a new project.

Bryce Wilson
Data Engineer at

Consistent support

If there's one thing that makes SUPA stand out, it's their commitment to providing consistent support throughout the data labeling process. The team actively and efficiently engaged with us to ensure any ambiguity in the dataset was cleared up.

Jonas Olausson
Data Engineer at Black AI
The best interface for self-service labeling.

Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.

Sravan Bhagavatula
Director of Computer Vision at Greyscale AI
Launch a revised batch within hours

I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.

Sparsh Shankar
Associate ML Engineer at Sprinklr
Really quick

The annotators were really quick. I would upload and 5 minutes later - 10 images done. I checked 5 minutes later - 100 images done.

Puneet Garg
Head of Data Science at Carousell
Good quality judgments

The team at [SUPA] has been very professional & easy to work with since we started our collaboration in 2019. They've provided us with good quality judgments to train, tune, and validate our Search & Recommendations models.

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