Rabot’s customers have different layouts for their packing stations. Therefore, each customer’s environment differs from previous locations. As a result, each customer onboarding requires a retraining of the models for perfect integration into the new environment.

For Rabot, it was crucial to find a solution to address the following issues:

• ‍Scalability: Retraining the model requires a lot of data from the customers. Those images/videos then need to be analyzed to determine which images should be annotated and used for retraining.‍
• Selection: Selecting the right data for training is essential for seamless integration. Existing selection processes were neither efficient nor could they provide an intelligent data selection.

Subsequently, Rabot could only onboard a certain number of customers at a time. They needed to find an efficient way to select the most interesting data to annotate the most relevant images.

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Rabot and Lightly have worked together closely to tackle the above challenges.

Lightly selects the most relevant data to help Rabot retrain its model for each customer as part of its machine learning pipeline. It uses the self-supervised learning feature to find the most diverse images.

Thanks to Lightly, Rabot reduced the amount of data required for retraining, resulting in a higher speed for retraining. In practice, Rabot could identify the 10% relevant training data while keeping model performance high. Thus, shortening the retraining process time by 50% significantly resulted in a 2x faster customer onboarding for Rabot.

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