We took just under 100 brands and our objective was to identify the bottles and extract useful information from their images (such as location, price and brand). For this, we developed a custom model with a good segmentation process flow.

Super-resolution techniques were applied to the price tags for extracting detailed information from them and an Image Classification process was used to the bottles by their brands. Once the SKU details mapping was done, we optimized the assortment planning by answering the below questions:

  • Which SKUs were performing the best in a given market or store?
  • Which SKUs should be allocated to a given market or store based on their performance?
  • What was the optimal product assortment for the retail shelf?
  • What was the optimal number of facings for each product?


Transfer learning refers to the ability of a learning mechanism to improve performance on the current or target task after having learned a different but related concept or skill on a previous task. The object being transferred may refer to instances, features, a particular form of search bias, an action policy, background knowledge, etc. We leverage the transfer learning models for image recognition tasks where the learned model trained on one problem is used to solve a similar problem. The benefit of leveraging such models is manifold and some of them are listed below:

  • Pre-trained models provide us with the efficiency in extracting key features from our data. The initial few layers of a pre-trained model contains vital information
  • Transfer learning saves time and resources in training models from scratch on similar or related tasks
  • Transfer learning works well with less labeled datasets and adapts well in multiple domains

Business Use Cases and Applications

There are multiple direct and indirect applications of this experiment. Some of them include:

  • Extracting SKU assortment from Retail Shelf images – Identify on-shelf and off-shelf products
  • Placement Audit – Validate whether SKU placement is in line with the planogram
  • Identify Out of Stock scenario – Flag vacant spaces on the shelf and prevent out-of-stock scenarios
  • Detecting Counterfeit Products – Identify the unauthorized replicas of the product

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